Style fix. (#8390)

* Update fix for potential XSS on /view

This commit uses mimetypes to add more restricted filetypes to prevent from being served, since mimetypes are what browsers use to determine how to serve files.

* Fix typo

Fixed a typo that prevented the program from running

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -15,6 +15,14 @@ body:
         steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
 
         If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+  - type: checkboxes
+    id: custom-nodes-test
+    attributes:
+      label: Custom Node Testing
+      description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
+      options:
+        - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
+          required: true
   - type: textarea
     attributes:
       label: Expected Behavior

.github/ISSUE_TEMPLATE/user-support.yml

@@ -11,6 +11,14 @@ body:
             **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics.
 
                 If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+    - type: checkboxes
+      id: custom-nodes-test
+      attributes:
+        label: Custom Node Testing
+        description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
+        options:
+          - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
+            required: true
     - type: textarea
       attributes:
             label: Your question

CODEOWNERS

@@ -5,20 +5,20 @@
 # Inlined the team members for now.
 
 # Maintainers
-*.md @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/tests/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/tests-unit/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/notebooks/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/script_examples/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/.github/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/requirements.txt @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/pyproject.toml @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+*.md @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests-unit/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/notebooks/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/script_examples/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/.github/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/requirements.txt @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/pyproject.toml @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
 
 # Python web server
-/api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
-/app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
-/utils/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/api_server/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/app/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/utils/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
 
 # Node developers
-/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
-/comfy/comfy_types/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
+/comfy_extras/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
+/comfy/comfy_types/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne

README.md

@@ -110,7 +110,6 @@ ComfyUI follows a weekly release cycle every Friday, with three interconnected r
 
 2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**
    - Builds a new release using the latest stable core version
-   - Version numbers match the core release (e.g., Desktop v1.7.0 uses Core v1.7.0)
 
 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
    - Weekly frontend updates are merged into the core repository
@@ -198,11 +197,11 @@ Put your VAE in: models/vae
 ### AMD GPUs (Linux only)
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.3```
 
-This is the command to install the nightly with ROCm 6.3 which might have some performance improvements:
+This is the command to install the nightly with ROCm 6.4 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.3```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4```
 
 ### Intel GPUs (Windows and Linux)
 
@@ -302,7 +301,7 @@ For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 pyt
 
 ### AMD ROCm Tips
 
-You can enable experimental memory efficient attention on pytorch 2.5 in ComfyUI on RDNA3 and potentially other AMD GPUs using this command:
+You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default.
 
 ```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
 

app/frontend_management.py

@@ -205,6 +205,19 @@ comfyui-workflow-templates is not installed.
 """.strip()
             )
 
+    @classmethod
+    def embedded_docs_path(cls) -> str:
+        """Get the path to embedded documentation"""
+        try:
+            import comfyui_embedded_docs
+
+            return str(
+                importlib.resources.files(comfyui_embedded_docs) / "docs"
+            )
+        except ImportError:
+            logging.info("comfyui-embedded-docs package not found")
+            return None
+
     @classmethod
     def parse_version_string(cls, value: str) -> tuple[str, str, str]:
         """

comfy/cli_args.py

@@ -88,6 +88,7 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE"
 
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
 parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
+parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"

comfy/comfy_types/node_typing.py

@@ -235,7 +235,7 @@ class ComfyNodeABC(ABC):
     DEPRECATED: bool
     """Flags a node as deprecated, indicating to users that they should find alternatives to this node."""
     API_NODE: Optional[bool]
-    """Flags a node as an API node."""
+    """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview."""
 
     @classmethod
     @abstractmethod

comfy/conds.py

@@ -24,6 +24,10 @@ class CONDRegular:
             conds.append(x.cond)
         return torch.cat(conds)
 
+    def size(self):
+        return list(self.cond.size())
+
+
 class CONDNoiseShape(CONDRegular):
     def process_cond(self, batch_size, device, area, **kwargs):
         data = self.cond
@@ -64,6 +68,7 @@ class CONDCrossAttn(CONDRegular):
             out.append(c)
         return torch.cat(out)
 
+
 class CONDConstant(CONDRegular):
     def __init__(self, cond):
         self.cond = cond
@@ -78,3 +83,48 @@ class CONDConstant(CONDRegular):
 
     def concat(self, others):
         return self.cond
+
+    def size(self):
+        return [1]
+
+
+class CONDList(CONDRegular):
+    def __init__(self, cond):
+        self.cond = cond
+
+    def process_cond(self, batch_size, device, **kwargs):
+        out = []
+        for c in self.cond:
+            out.append(comfy.utils.repeat_to_batch_size(c, batch_size).to(device))
+
+        return self._copy_with(out)
+
+    def can_concat(self, other):
+        if len(self.cond) != len(other.cond):
+            return False
+        for i in range(len(self.cond)):
+            if self.cond[i].shape != other.cond[i].shape:
+                return False
+
+        return True
+
+    def concat(self, others):
+        out = []
+        for i in range(len(self.cond)):
+            o = [self.cond[i]]
+            for x in others:
+                o.append(x.cond[i])
+            out.append(torch.cat(o))
+
+        return out
+
+    def size(self):  # hackish implementation to make the mem estimation work
+        o = 0
+        c = 1
+        for c in self.cond:
+            size = c.size()
+            o += math.prod(size)
+            if len(size) > 1:
+                c = size[1]
+
+        return [1, c, o // c]

comfy/ldm/chroma/layers.py

@@ -80,15 +80,13 @@ class DoubleStreamBlock(nn.Module):
         (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
 
         # prepare image for attention
-        img_modulated = self.img_norm1(img)
-        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+        img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img))
         img_qkv = self.img_attn.qkv(img_modulated)
         img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
 
         # prepare txt for attention
-        txt_modulated = self.txt_norm1(txt)
-        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+        txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt))
         txt_qkv = self.txt_attn.qkv(txt_modulated)
         txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
@@ -102,12 +100,12 @@ class DoubleStreamBlock(nn.Module):
         txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
 
         # calculate the img bloks
-        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
-        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
+        img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn))
+        img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img))))
 
         # calculate the txt bloks
-        txt += txt_mod1.gate * self.txt_attn.proj(txt_attn)
-        txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
+        txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn))
+        txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt))))
 
         if txt.dtype == torch.float16:
             txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
@@ -152,7 +150,7 @@ class SingleStreamBlock(nn.Module):
 
     def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
         mod = vec
-        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+        x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x))
         qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
 
         q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
@@ -162,7 +160,7 @@ class SingleStreamBlock(nn.Module):
         attn = attention(q, k, v, pe=pe, mask=attn_mask)
         # compute activation in mlp stream, cat again and run second linear layer
         output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
-        x += mod.gate * output
+        x.addcmul_(mod.gate, output)
         if x.dtype == torch.float16:
             x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
         return x
@@ -178,6 +176,6 @@ class LastLayer(nn.Module):
         shift, scale = vec
         shift = shift.squeeze(1)
         scale = scale.squeeze(1)
-        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x))
         x = self.linear(x)
         return x

comfy/ldm/chroma/model.py

@@ -163,7 +163,7 @@ class Chroma(nn.Module):
         distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype)
 
         # get all modulation index
-        modulation_index = timestep_embedding(torch.arange(mod_index_length), 32).to(img.device, img.dtype)
+        modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype)
         # we need to broadcast the modulation index here so each batch has all of the index
         modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype)
         # and we need to broadcast timestep and guidance along too

comfy/ldm/hunyuan_video/model.py

@@ -228,6 +228,7 @@ class HunyuanVideo(nn.Module):
         y: Tensor,
         guidance: Tensor = None,
         guiding_frame_index=None,
+        ref_latent=None,
         control=None,
         transformer_options={},
     ) -> Tensor:
@@ -238,6 +239,14 @@ class HunyuanVideo(nn.Module):
         img = self.img_in(img)
         vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype))
 
+        if ref_latent is not None:
+            ref_latent_ids = self.img_ids(ref_latent)
+            ref_latent = self.img_in(ref_latent)
+            img = torch.cat([ref_latent, img], dim=-2)
+            ref_latent_ids[..., 0] = -1
+            ref_latent_ids[..., 2] += (initial_shape[-1] // self.patch_size[-1])
+            img_ids = torch.cat([ref_latent_ids, img_ids], dim=-2)
+
         if guiding_frame_index is not None:
             token_replace_vec = self.time_in(timestep_embedding(guiding_frame_index, 256, time_factor=1.0))
             vec_ = self.vector_in(y[:, :self.params.vec_in_dim])
@@ -313,6 +322,8 @@ class HunyuanVideo(nn.Module):
                         img[:, : img_len] += add
 
         img = img[:, : img_len]
+        if ref_latent is not None:
+            img = img[:, ref_latent.shape[1]:]
 
         img = self.final_layer(img, vec, modulation_dims=modulation_dims)  # (N, T, patch_size ** 2 * out_channels)
 
@@ -324,7 +335,7 @@ class HunyuanVideo(nn.Module):
         img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4])
         return img
 
-    def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, control=None, transformer_options={}, **kwargs):
+    def img_ids(self, x):
         bs, c, t, h, w = x.shape
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
@@ -334,7 +345,11 @@ class HunyuanVideo(nn.Module):
         img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
         img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
         img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)
-        img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
+        return repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
+
+    def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs):
+        bs, c, t, h, w = x.shape
+        img_ids = self.img_ids(x)
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
-        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, guiding_frame_index, control, transformer_options)
+        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, guiding_frame_index, ref_latent, control=control, transformer_options=transformer_options)
         return out

comfy/ldm/modules/attention.py

@@ -20,8 +20,11 @@ if model_management.xformers_enabled():
 if model_management.sage_attention_enabled():
     try:
         from sageattention import sageattn
-    except ModuleNotFoundError:
-        logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
+    except ModuleNotFoundError as e:
+        if e.name == "sageattention":
+            logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
+        else:
+            raise e
         exit(-1)
 
 if model_management.flash_attention_enabled():

comfy/ldm/wan/model.py

@@ -247,6 +247,60 @@ class VaceWanAttentionBlock(WanAttentionBlock):
         return c_skip, c
 
 
+class WanCamAdapter(nn.Module):
+    def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1, operation_settings={}):
+        super(WanCamAdapter, self).__init__()
+
+        # Pixel Unshuffle: reduce spatial dimensions by a factor of 8
+        self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8)
+
+        # Convolution: reduce spatial dimensions by a factor
+        #  of 2 (without overlap)
+        self.conv = operation_settings.get("operations").Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+        # Residual blocks for feature extraction
+        self.residual_blocks = nn.Sequential(
+            *[WanCamResidualBlock(out_dim, operation_settings = operation_settings) for _ in range(num_residual_blocks)]
+        )
+
+    def forward(self, x):
+        # Reshape to merge the frame dimension into batch
+        bs, c, f, h, w = x.size()
+        x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w)
+
+        # Pixel Unshuffle operation
+        x_unshuffled = self.pixel_unshuffle(x)
+
+        # Convolution operation
+        x_conv = self.conv(x_unshuffled)
+
+        # Feature extraction with residual blocks
+        out = self.residual_blocks(x_conv)
+
+        # Reshape to restore original bf dimension
+        out = out.view(bs, f, out.size(1), out.size(2), out.size(3))
+
+        # Permute dimensions to reorder (if needed), e.g., swap channels and feature frames
+        out = out.permute(0, 2, 1, 3, 4)
+
+        return out
+
+
+class WanCamResidualBlock(nn.Module):
+    def __init__(self, dim, operation_settings={}):
+        super(WanCamResidualBlock, self).__init__()
+        self.conv1 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, x):
+        residual = x
+        out = self.relu(self.conv1(x))
+        out = self.conv2(out)
+        out += residual
+        return out
+
+
 class Head(nn.Module):
 
     def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}):
@@ -485,13 +539,20 @@ class WanModel(torch.nn.Module):
         x = self.unpatchify(x, grid_sizes)
         return x
 
-    def forward(self, x, timestep, context, clip_fea=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs):
         bs, c, t, h, w = x.shape
         x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
+
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
         h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
         w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
+
+        if time_dim_concat is not None:
+            time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)
+            x = torch.cat([x, time_dim_concat], dim=2)
+            t_len = ((x.shape[2] + (patch_size[0] // 2)) // patch_size[0])
+
         img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
         img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
         img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
@@ -581,7 +642,7 @@ class VaceWanModel(WanModel):
         t,
         context,
         vace_context,
-        vace_strength=1.0,
+        vace_strength,
         clip_fea=None,
         freqs=None,
         transformer_options={},
@@ -607,8 +668,11 @@ class VaceWanModel(WanModel):
                 context = torch.concat([context_clip, context], dim=1)
             context_img_len = clip_fea.shape[-2]
 
+        orig_shape = list(vace_context.shape)
+        vace_context = vace_context.movedim(0, 1).reshape([-1] + orig_shape[2:])
         c = self.vace_patch_embedding(vace_context.float()).to(vace_context.dtype)
         c = c.flatten(2).transpose(1, 2)
+        c = list(c.split(orig_shape[0], dim=0))
 
         # arguments
         x_orig = x
@@ -628,8 +692,9 @@ class VaceWanModel(WanModel):
 
             ii = self.vace_layers_mapping.get(i, None)
             if ii is not None:
-                c_skip, c = self.vace_blocks[ii](c, x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
-                x += c_skip * vace_strength
+                for iii in range(len(c)):
+                    c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+                    x += c_skip * vace_strength[iii]
                 del c_skip
         # head
         x = self.head(x, e)
@@ -637,3 +702,92 @@ class VaceWanModel(WanModel):
         # unpatchify
         x = self.unpatchify(x, grid_sizes)
         return x
+
+class CameraWanModel(WanModel):
+    r"""
+    Wan diffusion backbone supporting both text-to-video and image-to-video.
+    """
+
+    def __init__(self,
+                 model_type='camera',
+                 patch_size=(1, 2, 2),
+                 text_len=512,
+                 in_dim=16,
+                 dim=2048,
+                 ffn_dim=8192,
+                 freq_dim=256,
+                 text_dim=4096,
+                 out_dim=16,
+                 num_heads=16,
+                 num_layers=32,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=True,
+                 eps=1e-6,
+                 flf_pos_embed_token_number=None,
+                 image_model=None,
+                 in_dim_control_adapter=24,
+                 device=None,
+                 dtype=None,
+                 operations=None,
+                 ):
+
+        super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)
+        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+
+        self.control_adapter = WanCamAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:], operation_settings=operation_settings)
+
+
+    def forward_orig(
+        self,
+        x,
+        t,
+        context,
+        clip_fea=None,
+        freqs=None,
+        camera_conditions = None,
+        transformer_options={},
+        **kwargs,
+    ):
+        # embeddings
+        x = self.patch_embedding(x.float()).to(x.dtype)
+        if self.control_adapter is not None and camera_conditions is not None:
+            x_camera = self.control_adapter(camera_conditions).to(x.dtype)
+            x = x + x_camera
+        grid_sizes = x.shape[2:]
+        x = x.flatten(2).transpose(1, 2)
+
+        # time embeddings
+        e = self.time_embedding(
+            sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))
+        e0 = self.time_projection(e).unflatten(1, (6, self.dim))
+
+        # context
+        context = self.text_embedding(context)
+
+        context_img_len = None
+        if clip_fea is not None:
+            if self.img_emb is not None:
+                context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
+                context = torch.concat([context_clip, context], dim=1)
+            context_img_len = clip_fea.shape[-2]
+
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.blocks):
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
+                    return out
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+
+        # head
+        x = self.head(x, e)
+
+        # unpatchify
+        x = self.unpatchify(x, grid_sizes)
+        return x

comfy/lora.py

@@ -283,8 +283,15 @@ def model_lora_keys_unet(model, key_map={}):
         for k in sdk:
             if k.startswith("diffusion_model."):
                 if k.endswith(".weight"):
-                    key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
-                    key_map["lycoris_{}".format(key_lora)] = k #SimpleTuner lycoris format
+                    key_lora = k[len("diffusion_model."):-len(".weight")]
+                    key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format
+                    key_map["transformer.{}".format(key_lora)] = k #SimpleTuner regular format
+
+    if isinstance(model, comfy.model_base.ACEStep):
+        for k in sdk:
+            if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official ACE step lora format
+                key_lora = k[len("diffusion_model."):-len(".weight")]
+                key_map["{}".format(key_lora)] = k
 
     return key_map
 

comfy/model_base.py

@@ -102,6 +102,13 @@ def model_sampling(model_config, model_type):
     return ModelSampling(model_config)
 
 
+def convert_tensor(extra, dtype):
+    if hasattr(extra, "dtype"):
+        if extra.dtype != torch.int and extra.dtype != torch.long:
+            extra = extra.to(dtype)
+    return extra
+
+
 class BaseModel(torch.nn.Module):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel):
         super().__init__()
@@ -135,6 +142,7 @@ class BaseModel(torch.nn.Module):
         logging.info("model_type {}".format(model_type.name))
         logging.debug("adm {}".format(self.adm_channels))
         self.memory_usage_factor = model_config.memory_usage_factor
+        self.memory_usage_factor_conds = ()
 
     def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
         return comfy.patcher_extension.WrapperExecutor.new_class_executor(
@@ -164,9 +172,14 @@ class BaseModel(torch.nn.Module):
         extra_conds = {}
         for o in kwargs:
             extra = kwargs[o]
+
             if hasattr(extra, "dtype"):
-                if extra.dtype != torch.int and extra.dtype != torch.long:
-                    extra = extra.to(dtype)
+                extra = convert_tensor(extra, dtype)
+            elif isinstance(extra, list):
+                ex = []
+                for ext in extra:
+                    ex.append(convert_tensor(ext, dtype))
+                extra = ex
             extra_conds[o] = extra
 
         t = self.process_timestep(t, x=x, **extra_conds)
@@ -325,19 +338,28 @@ class BaseModel(torch.nn.Module):
     def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
         return self.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image)
 
-    def memory_required(self, input_shape):
+    def memory_required(self, input_shape, cond_shapes={}):
+        input_shapes = [input_shape]
+        for c in self.memory_usage_factor_conds:
+            shape = cond_shapes.get(c, None)
+            if shape is not None and len(shape) > 0:
+                input_shapes += shape
+
         if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
             dtype = self.get_dtype()
             if self.manual_cast_dtype is not None:
                 dtype = self.manual_cast_dtype
             #TODO: this needs to be tweaked
-            area = input_shape[0] * math.prod(input_shape[2:])
+            area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes))
             return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (1024 * 1024)
         else:
             #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
-            area = input_shape[0] * math.prod(input_shape[2:])
+            area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes))
             return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024)
 
+    def extra_conds_shapes(self, **kwargs):
+        return {}
+
 
 def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
     adm_inputs = []
@@ -924,6 +946,10 @@ class HunyuanVideo(BaseModel):
         if guiding_frame_index is not None:
             out['guiding_frame_index'] = comfy.conds.CONDRegular(torch.FloatTensor([guiding_frame_index]))
 
+        ref_latent = kwargs.get("ref_latent", None)
+        if ref_latent is not None:
+            out['ref_latent'] = comfy.conds.CONDRegular(self.process_latent_in(ref_latent))
+
         return out
 
     def scale_latent_inpaint(self, latent_image, **kwargs):
@@ -1043,6 +1069,11 @@ class WAN21(BaseModel):
         clip_vision_output = kwargs.get("clip_vision_output", None)
         if clip_vision_output is not None:
             out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.penultimate_hidden_states)
+
+        time_dim_concat = kwargs.get("time_dim_concat", None)
+        if time_dim_concat is not None:
+            out['time_dim_concat'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_concat))
+
         return out
 
 
@@ -1058,23 +1089,39 @@ class WAN21_Vace(WAN21):
         vace_frames = kwargs.get("vace_frames", None)
         if vace_frames is None:
             noise_shape[1] = 32
-            vace_frames = torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)
-
-        for i in range(0, vace_frames.shape[1], 16):
-            vace_frames = vace_frames.clone()
-            vace_frames[:, i:i + 16] = self.process_latent_in(vace_frames[:, i:i + 16])
+            vace_frames = [torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)]
 
         mask = kwargs.get("vace_mask", None)
         if mask is None:
             noise_shape[1] = 64
-            mask = torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)
+            mask = [torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)] * len(vace_frames)
 
-        out['vace_context'] = comfy.conds.CONDRegular(torch.cat([vace_frames.to(noise), mask.to(noise)], dim=1))
+        vace_frames_out = []
+        for j in range(len(vace_frames)):
+            vf = vace_frames[j].clone()
+            for i in range(0, vf.shape[1], 16):
+                vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16])
+            vf = torch.cat([vf, mask[j]], dim=1)
+            vace_frames_out.append(vf)
 
-        vace_strength = kwargs.get("vace_strength", 1.0)
+        vace_frames = torch.stack(vace_frames_out, dim=1)
+        out['vace_context'] = comfy.conds.CONDRegular(vace_frames)
+
+        vace_strength = kwargs.get("vace_strength", [1.0] * len(vace_frames_out))
         out['vace_strength'] = comfy.conds.CONDConstant(vace_strength)
         return out
 
+class WAN21_Camera(WAN21):
+    def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
+        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.CameraWanModel)
+        self.image_to_video = image_to_video
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        camera_conditions = kwargs.get("camera_conditions", None)
+        if camera_conditions is not None:
+            out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions)
+        return out
 
 class Hunyuan3Dv2(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):

comfy/model_detection.py

@@ -361,6 +361,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["model_type"] = "vace"
             dit_config["vace_in_dim"] = state_dict['{}vace_patch_embedding.weight'.format(key_prefix)].shape[1]
             dit_config["vace_layers"] = count_blocks(state_dict_keys, '{}vace_blocks.'.format(key_prefix) + '{}.')
+        elif '{}control_adapter.conv.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["model_type"] = "camera"
         else:
             if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
                 dit_config["model_type"] = "i2v"
@@ -618,6 +620,9 @@ def convert_config(unet_config):
 
 
 def unet_config_from_diffusers_unet(state_dict, dtype=None):
+    if "conv_in.weight" not in state_dict:
+        return None
+
     match = {}
     transformer_depth = []
 

comfy/model_management.py

@@ -297,11 +297,16 @@ except:
 
 try:
     if is_amd():
+        try:
+            rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2]))
+        except:
+            rocm_version = (6, -1)
         arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
         logging.info("AMD arch: {}".format(arch))
+        logging.info("ROCm version: {}".format(rocm_version))
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             if torch_version_numeric[0] >= 2 and torch_version_numeric[1] >= 7:  # works on 2.6 but doesn't actually seem to improve much
-                if any((a in arch) for a in ["gfx1100", "gfx1101"]):  # TODO: more arches
+                if any((a in arch) for a in ["gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches
                     ENABLE_PYTORCH_ATTENTION = True
 except:
     pass
@@ -695,7 +700,7 @@ def unet_inital_load_device(parameters, dtype):
         return torch_dev
 
     cpu_dev = torch.device("cpu")
-    if DISABLE_SMART_MEMORY:
+    if DISABLE_SMART_MEMORY or vram_state == VRAMState.NO_VRAM:
         return cpu_dev
 
     model_size = dtype_size(dtype) * parameters
@@ -1257,6 +1262,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     return False
 
 def supports_fp8_compute(device=None):
+    if args.supports_fp8_compute:
+        return True
+
     if not is_nvidia():
         return False
 

comfy/rmsnorm.py

@@ -30,7 +30,7 @@ if RMSNorm is None:
         def __init__(
             self,
             normalized_shape,
-            eps=None,
+            eps=1e-6,
             elementwise_affine=True,
             device=None,
             dtype=None,

comfy/sampler_helpers.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 import uuid
+import math
+import collections
 import comfy.model_management
 import comfy.conds
 import comfy.utils
@@ -104,6 +106,21 @@ def cleanup_additional_models(models):
         if hasattr(m, 'cleanup'):
             m.cleanup()
 
+def estimate_memory(model, noise_shape, conds):
+    cond_shapes = collections.defaultdict(list)
+    cond_shapes_min = {}
+    for _, cs in conds.items():
+        for cond in cs:
+            for k, v in model.model.extra_conds_shapes(**cond).items():
+                cond_shapes[k].append(v)
+                if cond_shapes_min.get(k, None) is None:
+                    cond_shapes_min[k] = [v]
+                elif math.prod(v) > math.prod(cond_shapes_min[k][0]):
+                    cond_shapes_min[k] = [v]
+
+    memory_required = model.model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:]), cond_shapes=cond_shapes)
+    minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min)
+    return memory_required, minimum_memory_required
 
 def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
     executor = comfy.patcher_extension.WrapperExecutor.new_executor(
@@ -117,9 +134,8 @@ def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=Non
     models, inference_memory = get_additional_models(conds, model.model_dtype())
     models += get_additional_models_from_model_options(model_options)
     models += model.get_nested_additional_models()  # TODO: does this require inference_memory update?
-    memory_required = model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory
-    minimum_memory_required = model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + inference_memory
-    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required)
+    memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds)
+    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory)
     real_model = model.model
 
     return real_model, conds, models

comfy/samplers.py

@@ -256,7 +256,13 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te
             for i in range(1, len(to_batch_temp) + 1):
                 batch_amount = to_batch_temp[:len(to_batch_temp)//i]
                 input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
-                if model.memory_required(input_shape) * 1.5 < free_memory:
+                cond_shapes = collections.defaultdict(list)
+                for tt in batch_amount:
+                    cond = {k: v.size() for k, v in to_run[tt][0].conditioning.items()}
+                    for k, v in to_run[tt][0].conditioning.items():
+                        cond_shapes[k].append(v.size())
+
+                if model.memory_required(input_shape, cond_shapes=cond_shapes) * 1.5 < free_memory:
                     to_batch = batch_amount
                     break
 

comfy/supported_models.py

@@ -992,6 +992,16 @@ class WAN21_FunControl2V(WAN21_T2V):
         out = model_base.WAN21(self, image_to_video=False, device=device)
         return out
 
+class WAN21_Camera(WAN21_T2V):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "camera",
+        "in_dim": 32,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN21_Camera(self, image_to_video=False, device=device)
+        return out
 class WAN21_Vace(WAN21_T2V):
     unet_config = {
         "image_model": "wan2.1",
@@ -1129,6 +1139,6 @@ class ACEStep(supported_models_base.BASE):
     def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model)
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep]
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep]
 
 models += [SVD_img2vid]

comfy/text_encoders/long_clipl.json

@@ -1,25 +0,0 @@
-{
-  "_name_or_path": "openai/clip-vit-large-patch14",
-  "architectures": [
-    "CLIPTextModel"
-  ],
-  "attention_dropout": 0.0,
-  "bos_token_id": 0,
-  "dropout": 0.0,
-  "eos_token_id": 49407,
-  "hidden_act": "quick_gelu",
-  "hidden_size": 768,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 248,
-  "model_type": "clip_text_model",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 1,
-  "projection_dim": 768,
-  "torch_dtype": "float32",
-  "transformers_version": "4.24.0",
-  "vocab_size": 49408
-}

comfy/utils.py

@@ -78,8 +78,6 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
             pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args)
         else:
             pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
-        if "global_step" in pl_sd:
-            logging.debug(f"Global Step: {pl_sd['global_step']}")
         if "state_dict" in pl_sd:
             sd = pl_sd["state_dict"]
         else:

comfy_api/input/video_types.py

@@ -43,3 +43,13 @@ class VideoInput(ABC):
         components = self.get_components()
         return components.images.shape[2], components.images.shape[1]
 
+    def get_duration(self) -> float:
+        """
+        Returns the duration of the video in seconds.
+
+        Returns:
+            Duration in seconds
+        """
+        components = self.get_components()
+        frame_count = components.images.shape[0]
+        return float(frame_count / components.frame_rate)

comfy_api/input_impl/video_types.py

@@ -80,6 +80,38 @@ class VideoFromFile(VideoInput):
                     return stream.width, stream.height
         raise ValueError(f"No video stream found in file '{self.__file}'")
 
+    def get_duration(self) -> float:
+        """
+        Returns the duration of the video in seconds.
+
+        Returns:
+            Duration in seconds
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+        with av.open(self.__file, mode="r") as container:
+            if container.duration is not None:
+                return float(container.duration / av.time_base)
+
+            # Fallback: calculate from frame count and frame rate
+            video_stream = next(
+                (s for s in container.streams if s.type == "video"), None
+            )
+            if video_stream and video_stream.frames and video_stream.average_rate:
+                return float(video_stream.frames / video_stream.average_rate)
+
+            # Last resort: decode frames to count them
+            if video_stream and video_stream.average_rate:
+                frame_count = 0
+                container.seek(0)
+                for packet in container.demux(video_stream):
+                    for _ in packet.decode():
+                        frame_count += 1
+                if frame_count > 0:
+                    return float(frame_count / video_stream.average_rate)
+
+        raise ValueError(f"Could not determine duration for file '{self.__file}'")
+
     def get_components_internal(self, container: InputContainer) -> VideoComponents:
         # Get video frames
         frames = []

comfy_api/torch_helpers/__init__.py

@@ -0,0 +1,5 @@
+from .torch_compile import set_torch_compile_wrapper
+
+__all__ = [
+    "set_torch_compile_wrapper",
+]

comfy_api/torch_helpers/torch_compile.py

@@ -0,0 +1,69 @@
+from __future__ import annotations
+import torch
+
+import comfy.utils
+from comfy.patcher_extension import WrappersMP
+from typing import TYPE_CHECKING, Callable, Optional
+if TYPE_CHECKING:
+    from comfy.model_patcher import ModelPatcher
+    from comfy.patcher_extension import WrapperExecutor
+
+
+COMPILE_KEY = "torch.compile"
+TORCH_COMPILE_KWARGS = "torch_compile_kwargs"
+
+
+def apply_torch_compile_factory(compiled_module_dict: dict[str, Callable]) -> Callable:
+    '''
+    Create a wrapper that will refer to the compiled_diffusion_model.
+    '''
+    def apply_torch_compile_wrapper(executor: WrapperExecutor, *args, **kwargs):
+        try:
+            orig_modules = {}
+            for key, value in compiled_module_dict.items():
+                orig_modules[key] = comfy.utils.get_attr(executor.class_obj, key)
+                comfy.utils.set_attr(executor.class_obj, key, value)
+            return executor(*args, **kwargs)
+        finally:
+            for key, value in orig_modules.items():
+                comfy.utils.set_attr(executor.class_obj, key, value)
+    return apply_torch_compile_wrapper
+
+
+def set_torch_compile_wrapper(model: ModelPatcher, backend: str, options: Optional[dict[str,str]]=None,
+                              mode: Optional[str]=None, fullgraph=False, dynamic: Optional[bool]=None,
+                              keys: list[str]=["diffusion_model"], *args, **kwargs):
+    '''
+    Perform torch.compile that will be applied at sample time for either the whole model or specific params of the BaseModel instance.
+
+    When keys is None, it will default to using ["diffusion_model"], compiling the whole diffusion_model.
+    When a list of keys is provided, it will perform torch.compile on only the selected modules.
+    '''
+    # clear out any other torch.compile wrappers
+    model.remove_wrappers_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY)
+    # if no keys, default to 'diffusion_model'
+    if not keys:
+        keys = ["diffusion_model"]
+    # create kwargs dict that can be referenced later
+    compile_kwargs = {
+        "backend": backend,
+        "options": options,
+        "mode": mode,
+        "fullgraph": fullgraph,
+        "dynamic": dynamic,
+    }
+    # get a dict of compiled keys
+    compiled_modules = {}
+    for key in keys:
+        compiled_modules[key] = torch.compile(
+                model=model.get_model_object(key),
+                **compile_kwargs,
+            )
+    # add torch.compile wrapper
+    wrapper_func = apply_torch_compile_factory(
+        compiled_module_dict=compiled_modules,
+    )
+    # store wrapper to run on BaseModel's apply_model function
+    model.add_wrapper_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY, wrapper_func)
+    # keep compile kwargs for reference
+    model.model_options[TORCH_COMPILE_KWARGS] = compile_kwargs

comfy_api_nodes/README.md

@@ -18,6 +18,8 @@ Follow the instructions [here](https://github.com/Comfy-Org/ComfyUI_frontend) to
 python run main.py --comfy-api-base https://stagingapi.comfy.org
 ```
 
+To authenticate to staging, please login and then ask one of Comfy Org team to whitelist you for access to staging.
+
 API stubs are generated through automatic codegen tools from OpenAPI definitions. Since the Comfy Org OpenAPI definition contains many things from the Comfy Registry as well, we use redocly/cli to filter out only the paths relevant for API nodes.
 
 ### Redocly Instructions 
@@ -28,7 +30,7 @@ When developing locally, use the `redocly-dev.yaml` file to generate pydantic mo
 Before your API node PR merges, make sure to add the `Released` tag to the `openapi.yaml` file and test in staging.
 
 ```bash
-# Download the OpenAPI file from prod server.
+# Download the OpenAPI file from staging server.
 curl -o openapi.yaml https://stagingapi.comfy.org/openapi
 
 # Filter out unneeded API definitions.
@@ -39,3 +41,25 @@ redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_no
 datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel
 
 ```
+
+
+# Merging to Master
+
+Before merging to comfyanonymous/ComfyUI master, follow these steps:
+
+1. Add the "Released" tag to the ComfyUI OpenAPI yaml file for each endpoint you are using in the nodes. 
+1. Make sure the ComfyUI API is deployed to prod with your changes.
+1. Run the code generation again with `redocly.yaml` and the production OpenAPI yaml file.
+
+```bash
+# Download the OpenAPI file from prod server.
+curl -o openapi.yaml https://api.comfy.org/openapi
+
+# Filter out unneeded API definitions.
+npm install -g @redocly/cli
+redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components
+
+# Generate the pydantic datamodels for validation.
+datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel
+
+```

comfy_api_nodes/apinode_utils.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 import io
 import logging
-from typing import Optional
+import mimetypes
+from typing import Optional, Union
 from comfy.utils import common_upscale
 from comfy_api.input_impl import VideoFromFile
 from comfy_api.util import VideoContainer, VideoCodec
@@ -15,6 +16,7 @@ from comfy_api_nodes.apis.client import (
     UploadRequest,
     UploadResponse,
 )
+from server import PromptServer
 
 
 import numpy as np
@@ -60,7 +62,9 @@ def downscale_image_tensor(image, total_pixels=1536 * 1024) -> torch.Tensor:
     return s
 
 
-def validate_and_cast_response(response, timeout: int = None) -> torch.Tensor:
+def validate_and_cast_response(
+    response, timeout: int = None, node_id: Union[str, None] = None
+) -> torch.Tensor:
     """Validates and casts a response to a torch.Tensor.
 
     Args:
@@ -94,6 +98,10 @@ def validate_and_cast_response(response, timeout: int = None) -> torch.Tensor:
             img = Image.open(io.BytesIO(img_data))
 
         elif image_url:
+            if node_id:
+                PromptServer.instance.send_progress_text(
+                    f"Result URL: {image_url}", node_id
+                )
             img_response = requests.get(image_url, timeout=timeout)
             if img_response.status_code != 200:
                 raise ValueError("Failed to download the image")
@@ -207,6 +215,7 @@ def download_url_to_image_tensor(url: str, timeout: int = None) -> torch.Tensor:
     image_bytesio = download_url_to_bytesio(url, timeout)
     return bytesio_to_image_tensor(image_bytesio)
 
+
 def process_image_response(response: requests.Response) -> torch.Tensor:
     """Uses content from a Response object and converts it to a torch.Tensor"""
     return bytesio_to_image_tensor(BytesIO(response.content))
@@ -311,11 +320,27 @@ def tensor_to_data_uri(
     return f"data:{mime_type};base64,{base64_string}"
 
 
+def text_filepath_to_base64_string(filepath: str) -> str:
+    """Converts a text file to a base64 string."""
+    with open(filepath, "rb") as f:
+        file_content = f.read()
+    return base64.b64encode(file_content).decode("utf-8")
+
+
+def text_filepath_to_data_uri(filepath: str) -> str:
+    """Converts a text file to a data URI."""
+    base64_string = text_filepath_to_base64_string(filepath)
+    mime_type, _ = mimetypes.guess_type(filepath)
+    if mime_type is None:
+        mime_type = "application/octet-stream"
+    return f"data:{mime_type};base64,{base64_string}"
+
+
 def upload_file_to_comfyapi(
     file_bytes_io: BytesIO,
     filename: str,
     upload_mime_type: str,
-    auth_kwargs: Optional[dict[str,str]] = None,
+    auth_kwargs: Optional[dict[str, str]] = None,
 ) -> str:
     """
     Uploads a single file to ComfyUI API and returns its download URL.
@@ -350,9 +375,33 @@ def upload_file_to_comfyapi(
     return response.download_url
 
 
+def video_to_base64_string(
+    video: VideoInput,
+    container_format: VideoContainer = None,
+    codec: VideoCodec = None
+) -> str:
+    """
+    Converts a video input to a base64 string.
+
+    Args:
+        video: The video input to convert
+        container_format: Optional container format to use (defaults to video.container if available)
+        codec: Optional codec to use (defaults to video.codec if available)
+    """
+    video_bytes_io = io.BytesIO()
+
+    # Use provided format/codec if specified, otherwise use video's own if available
+    format_to_use = container_format if container_format is not None else getattr(video, 'container', VideoContainer.MP4)
+    codec_to_use = codec if codec is not None else getattr(video, 'codec', VideoCodec.H264)
+
+    video.save_to(video_bytes_io, format=format_to_use, codec=codec_to_use)
+    video_bytes_io.seek(0)
+    return base64.b64encode(video_bytes_io.getvalue()).decode("utf-8")
+
+
 def upload_video_to_comfyapi(
     video: VideoInput,
-    auth_kwargs: Optional[dict[str,str]] = None,
+    auth_kwargs: Optional[dict[str, str]] = None,
     container: VideoContainer = VideoContainer.MP4,
     codec: VideoCodec = VideoCodec.H264,
     max_duration: Optional[int] = None,
@@ -454,7 +503,7 @@ def audio_ndarray_to_bytesio(
 
 def upload_audio_to_comfyapi(
     audio: AudioInput,
-    auth_kwargs: Optional[dict[str,str]] = None,
+    auth_kwargs: Optional[dict[str, str]] = None,
     container_format: str = "mp4",
     codec_name: str = "aac",
     mime_type: str = "audio/mp4",
@@ -481,8 +530,25 @@ def upload_audio_to_comfyapi(
     return upload_file_to_comfyapi(audio_bytes_io, filename, mime_type, auth_kwargs)
 
 
+def audio_to_base64_string(
+    audio: AudioInput, container_format: str = "mp4", codec_name: str = "aac"
+) -> str:
+    """Converts an audio input to a base64 string."""
+    sample_rate: int = audio["sample_rate"]
+    waveform: torch.Tensor = audio["waveform"]
+    audio_data_np = audio_tensor_to_contiguous_ndarray(waveform)
+    audio_bytes_io = audio_ndarray_to_bytesio(
+        audio_data_np, sample_rate, container_format, codec_name
+    )
+    audio_bytes = audio_bytes_io.getvalue()
+    return base64.b64encode(audio_bytes).decode("utf-8")
+
+
 def upload_images_to_comfyapi(
-    image: torch.Tensor, max_images=8, auth_kwargs: Optional[dict[str,str]] = None, mime_type: Optional[str] = None
+    image: torch.Tensor,
+    max_images=8,
+    auth_kwargs: Optional[dict[str, str]] = None,
+    mime_type: Optional[str] = None,
 ) -> list[str]:
     """
     Uploads images to ComfyUI API and returns download URLs.
@@ -547,17 +613,24 @@ def upload_images_to_comfyapi(
     return download_urls
 
 
-def resize_mask_to_image(mask: torch.Tensor, image: torch.Tensor,
-                         upscale_method="nearest-exact", crop="disabled",
-                         allow_gradient=True, add_channel_dim=False):
+def resize_mask_to_image(
+    mask: torch.Tensor,
+    image: torch.Tensor,
+    upscale_method="nearest-exact",
+    crop="disabled",
+    allow_gradient=True,
+    add_channel_dim=False,
+):
     """
     Resize mask to be the same dimensions as an image, while maintaining proper format for API calls.
     """
     _, H, W, _ = image.shape
     mask = mask.unsqueeze(-1)
-    mask = mask.movedim(-1,1)
-    mask = common_upscale(mask, width=W, height=H, upscale_method=upscale_method, crop=crop)
-    mask = mask.movedim(1,-1)
+    mask = mask.movedim(-1, 1)
+    mask = common_upscale(
+        mask, width=W, height=H, upscale_method=upscale_method, crop=crop
+    )
+    mask = mask.movedim(1, -1)
     if not add_channel_dim:
         mask = mask.squeeze(-1)
     if not allow_gradient:
@@ -565,12 +638,41 @@ def resize_mask_to_image(mask: torch.Tensor, image: torch.Tensor,
     return mask
 
 
-def validate_string(string: str, strip_whitespace=True, field_name="prompt", min_length=None, max_length=None):
+def validate_string(
+    string: str,
+    strip_whitespace=True,
+    field_name="prompt",
+    min_length=None,
+    max_length=None,
+):
+    if string is None:
+        raise Exception(f"Field '{field_name}' cannot be empty.")
     if strip_whitespace:
         string = string.strip()
     if min_length and len(string) < min_length:
-        raise Exception(f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long.")
+        raise Exception(
+            f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long."
+        )
     if max_length and len(string) > max_length:
-        raise Exception(f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long.")
-    if not string:
-        raise Exception(f"Field '{field_name}' cannot be empty.")
+        raise Exception(
+            f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long."
+        )
+
+
+def image_tensor_pair_to_batch(
+    image1: torch.Tensor, image2: torch.Tensor
+) -> torch.Tensor:
+    """
+    Converts a pair of image tensors to a batch tensor.
+    If the images are not the same size, the smaller image is resized to
+    match the larger image.
+    """
+    if image1.shape[1:] != image2.shape[1:]:
+        image2 = common_upscale(
+            image2.movedim(-1, 1),
+            image1.shape[2],
+            image1.shape[1],
+            "bilinear",
+            "center",
+        ).movedim(1, -1)
+    return torch.cat((image1, image2), dim=0)

comfy_api_nodes/apis/bfl_api.py

@@ -108,6 +108,24 @@ class BFLFluxProGenerateRequest(BaseModel):
     # )
 
 
+class BFLFluxKontextProGenerateRequest(BaseModel):
+    prompt: str = Field(..., description='The text prompt for what you wannt to edit.')
+    input_image: Optional[str] = Field(None, description='Image to edit in base64 format')
+    seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
+    guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process')
+    steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process')
+    safety_tolerance: Optional[conint(ge=0, le=2)] = Field(
+        2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.'
+    )
+    output_format: Optional[BFLOutputFormat] = Field(
+        BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
+    )
+    aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.')
+    prompt_upsampling: Optional[bool] = Field(
+        None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
+    )
+
+
 class BFLFluxProUltraGenerateRequest(BaseModel):
     prompt: str = Field(..., description='The text prompt for image generation.')
     prompt_upsampling: Optional[bool] = Field(

comfy_api_nodes/apis/client.py

@@ -94,15 +94,19 @@ from __future__ import annotations
 import logging
 import time
 import io
-from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable
+import socket
+from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable, Tuple
 from enum import Enum
 import json
 import requests
-from urllib.parse import urljoin
+from urllib.parse import urljoin, urlparse
 from pydantic import BaseModel, Field
+import uuid # For generating unique operation IDs
 
+from server import PromptServer
 from comfy.cli_args import args
 from comfy import utils
+from . import request_logger
 
 T = TypeVar("T", bound=BaseModel)
 R = TypeVar("R", bound=BaseModel)
@@ -111,6 +115,21 @@ P = TypeVar("P", bound=BaseModel)  # For poll response
 PROGRESS_BAR_MAX = 100
 
 
+class NetworkError(Exception):
+    """Base exception for network-related errors with diagnostic information."""
+    pass
+
+
+class LocalNetworkError(NetworkError):
+    """Exception raised when local network connectivity issues are detected."""
+    pass
+
+
+class ApiServerError(NetworkError):
+    """Exception raised when the API server is unreachable but internet is working."""
+    pass
+
+
 class EmptyRequest(BaseModel):
     """Base class for empty request bodies.
     For GET requests, fields will be sent as query parameters."""
@@ -120,7 +139,7 @@ class EmptyRequest(BaseModel):
 
 class UploadRequest(BaseModel):
     file_name: str = Field(..., description="Filename to upload")
-    content_type: str | None = Field(
+    content_type: Optional[str] = Field(
         None,
         description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.",
     )
@@ -141,7 +160,7 @@ class HttpMethod(str, Enum):
 
 class ApiClient:
     """
-    Client for making HTTP requests to an API with authentication and error handling.
+    Client for making HTTP requests to an API with authentication, error handling, and retry logic.
     """
 
     def __init__(
@@ -151,12 +170,26 @@ class ApiClient:
         comfy_api_key: Optional[str] = None,
         timeout: float = 3600.0,
         verify_ssl: bool = True,
+        max_retries: int = 3,
+        retry_delay: float = 1.0,
+        retry_backoff_factor: float = 2.0,
+        retry_status_codes: Optional[Tuple[int, ...]] = None,
     ):
         self.base_url = base_url
         self.auth_token = auth_token
         self.comfy_api_key = comfy_api_key
         self.timeout = timeout
         self.verify_ssl = verify_ssl
+        self.max_retries = max_retries
+        self.retry_delay = retry_delay
+        self.retry_backoff_factor = retry_backoff_factor
+        # Default retry status codes: 408 (Request Timeout), 429 (Too Many Requests),
+        # 500, 502, 503, 504 (Server Errors)
+        self.retry_status_codes = retry_status_codes or (408, 429, 500, 502, 503, 504)
+
+    def _generate_operation_id(self, path: str) -> str:
+        """Generates a unique operation ID for logging."""
+        return f"{path.strip('/').replace('/', '_')}_{uuid.uuid4().hex[:8]}"
 
     def _create_json_payload_args(
         self,
@@ -211,6 +244,56 @@ class ApiClient:
 
         return headers
 
+    def _check_connectivity(self, target_url: str) -> Dict[str, bool]:
+        """
+        Check connectivity to determine if network issues are local or server-related.
+
+        Args:
+            target_url: URL to check connectivity to
+
+        Returns:
+            Dictionary with connectivity status details
+        """
+        results = {
+            "internet_accessible": False,
+            "api_accessible": False,
+            "is_local_issue": False,
+            "is_api_issue": False
+        }
+
+        # First check basic internet connectivity using a reliable external site
+        try:
+            # Use a reliable external domain for checking basic connectivity
+            check_response = requests.get("https://www.google.com",
+                                         timeout=5.0,
+                                         verify=self.verify_ssl)
+            if check_response.status_code < 500:
+                results["internet_accessible"] = True
+        except (requests.RequestException, socket.error):
+            results["internet_accessible"] = False
+            results["is_local_issue"] = True
+            return results
+
+        # Now check API server connectivity
+        try:
+            # Extract domain from the target URL to do a simpler health check
+            parsed_url = urlparse(target_url)
+            api_base = f"{parsed_url.scheme}://{parsed_url.netloc}"
+
+            # Try to reach the API domain
+            api_response = requests.get(f"{api_base}/health", timeout=5.0, verify=self.verify_ssl)
+            if api_response.status_code < 500:
+                results["api_accessible"] = True
+            else:
+                results["api_accessible"] = False
+                results["is_api_issue"] = True
+        except requests.RequestException:
+            results["api_accessible"] = False
+            # If we can reach the internet but not the API, it's an API issue
+            results["is_api_issue"] = True
+
+        return results
+
     def request(
         self,
         method: str,
@@ -221,9 +304,10 @@ class ApiClient:
         headers: Optional[Dict[str, str]] = None,
         content_type: str = "application/json",
         multipart_parser: Callable = None,
+        retry_count: int = 0,  # Used internally for tracking retries
     ) -> Dict[str, Any]:
         """
-        Make an HTTP request to the API
+        Make an HTTP request to the API with automatic retries for transient errors.
 
         Args:
             method: HTTP method (GET, POST, etc.)
@@ -233,14 +317,19 @@ class ApiClient:
             files: Files to upload
             headers: Additional headers
             content_type: Content type of the request. Defaults to application/json.
+            retry_count: Internal parameter for tracking retries, do not set manually
 
         Returns:
             Parsed JSON response
 
         Raises:
-            requests.RequestException: If the request fails
+            LocalNetworkError: If local network connectivity issues are detected
+            ApiServerError: If the API server is unreachable but internet is working
+            Exception: For other request failures
         """
-        url = urljoin(self.base_url, path)
+        # Use urljoin but ensure path is relative to avoid absolute path behavior
+        relative_path = path.lstrip('/')
+        url = urljoin(self.base_url, relative_path)
         self.check_auth(self.auth_token, self.comfy_api_key)
         # Combine default headers with any provided headers
         request_headers = self.get_headers()
@@ -265,6 +354,16 @@ class ApiClient:
         else:
             payload_args = self._create_json_payload_args(data, request_headers)
 
+        operation_id = self._generate_operation_id(path)
+        request_logger.log_request_response(
+            operation_id=operation_id,
+            request_method=method,
+            request_url=url,
+            request_headers=request_headers,
+            request_params=params,
+            request_data=data if content_type == "application/json" else "[form-data or other]"
+        )
+
         try:
             response = requests.request(
                 method=method,
@@ -275,50 +374,228 @@ class ApiClient:
                 **payload_args,
             )
 
+            # Check if we should retry based on status code
+            if (response.status_code in self.retry_status_codes and
+                retry_count < self.max_retries):
+
+                # Calculate delay with exponential backoff
+                delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
+
+                logging.warning(
+                    f"Request failed with status {response.status_code}. "
+                    f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})"
+                )
+
+                time.sleep(delay)
+                return self.request(
+                    method=method,
+                    path=path,
+                    params=params,
+                    data=data,
+                    files=files,
+                    headers=headers,
+                    content_type=content_type,
+                    multipart_parser=multipart_parser,
+                    retry_count=retry_count + 1,
+                )
+
             # Raise exception for error status codes
             response.raise_for_status()
-        except requests.ConnectionError:
-            raise Exception(
-                f"Unable to connect to the API server at {self.base_url}. Please check your internet connection or verify the service is available."
+
+            # Log successful response
+            response_content_to_log = response.content
+            try:
+                # Attempt to parse JSON for prettier logging, fallback to raw content
+                response_content_to_log = response.json()
+            except json.JSONDecodeError:
+                pass # Keep as bytes/str if not JSON
+
+            request_logger.log_request_response(
+                operation_id=operation_id,
+                request_method=method, # Pass request details again for context in log
+                request_url=url,
+                response_status_code=response.status_code,
+                response_headers=dict(response.headers),
+                response_content=response_content_to_log
             )
 
-        except requests.Timeout:
-            raise Exception(
-                f"Request timed out after {self.timeout} seconds. The server might be experiencing high load or the operation is taking longer than expected."
+        except requests.ConnectionError as e:
+            error_message = f"ConnectionError: {str(e)}"
+            request_logger.log_request_response(
+                operation_id=operation_id,
+                request_method=method,
+                request_url=url,
+                error_message=error_message
             )
+            # Only perform connectivity check if we've exhausted all retries
+            if retry_count >= self.max_retries:
+                # Check connectivity to determine if it's a local or API issue
+                connectivity = self._check_connectivity(self.base_url)
+
+                if connectivity["is_local_issue"]:
+                    raise LocalNetworkError(
+                        "Unable to connect to the API server due to local network issues. "
+                        "Please check your internet connection and try again."
+                    ) from e
+                elif connectivity["is_api_issue"]:
+                    raise ApiServerError(
+                        f"The API server at {self.base_url} is currently unreachable. "
+                        f"The service may be experiencing issues. Please try again later."
+                    ) from e
+
+            # If we haven't exhausted retries yet, retry the request
+            if retry_count < self.max_retries:
+                delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
+                logging.warning(
+                    f"Connection error: {str(e)}. "
+                    f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})"
+                )
+                time.sleep(delay)
+                return self.request(
+                    method=method,
+                    path=path,
+                    params=params,
+                    data=data,
+                    files=files,
+                    headers=headers,
+                    content_type=content_type,
+                    multipart_parser=multipart_parser,
+                    retry_count=retry_count + 1,
+                )
+
+            # If we've exhausted retries and didn't identify the specific issue,
+            # raise a generic exception
+            final_error_message = (
+                f"Unable to connect to the API server after {self.max_retries} attempts. "
+                f"Please check your internet connection or try again later."
+            )
+            request_logger.log_request_response( # Log final failure
+                operation_id=operation_id,
+                request_method=method, request_url=url,
+                error_message=final_error_message
+            )
+            raise Exception(final_error_message) from e
+
+        except requests.Timeout as e:
+            error_message = f"Timeout: {str(e)}"
+            request_logger.log_request_response(
+                operation_id=operation_id,
+                request_method=method, request_url=url,
+                error_message=error_message
+            )
+            # Retry timeouts if we haven't exhausted retries
+            if retry_count < self.max_retries:
+                delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
+                logging.warning(
+                    f"Request timed out. "
+                    f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})"
+                )
+                time.sleep(delay)
+                return self.request(
+                    method=method,
+                    path=path,
+                    params=params,
+                    data=data,
+                    files=files,
+                    headers=headers,
+                    content_type=content_type,
+                    multipart_parser=multipart_parser,
+                    retry_count=retry_count + 1,
+                )
+            final_error_message = (
+                f"Request timed out after {self.timeout} seconds and {self.max_retries} retry attempts. "
+                f"The server might be experiencing high load or the operation is taking longer than expected."
+            )
+            request_logger.log_request_response( # Log final failure
+                operation_id=operation_id,
+                request_method=method, request_url=url,
+                error_message=final_error_message
+            )
+            raise Exception(final_error_message) from e
 
         except requests.HTTPError as e:
             status_code = e.response.status_code if hasattr(e, "response") else None
-            error_message = f"HTTP Error: {str(e)}"
+            original_error_message = f"HTTP Error: {str(e)}"
+            error_content_for_log = None
+            if hasattr(e, "response") and e.response is not None:
+                error_content_for_log = e.response.content
+                try:
+                    error_content_for_log = e.response.json()
+                except json.JSONDecodeError:
+                    pass
+
+
+            # Try to extract detailed error message from JSON response for user display
+            # but log the full error content.
+            user_display_error_message = original_error_message
 
-            # Try to extract detailed error message from JSON response
             try:
-                if hasattr(e, "response") and e.response.content:
+                if hasattr(e, "response") and e.response is not None and e.response.content:
                     error_json = e.response.json()
                     if "error" in error_json and "message" in error_json["error"]:
-                        error_message = f"API Error: {error_json['error']['message']}"
+                        user_display_error_message = f"API Error: {error_json['error']['message']}"
                         if "type" in error_json["error"]:
-                            error_message += f" (Type: {error_json['error']['type']})"
+                            user_display_error_message += f" (Type: {error_json['error']['type']})"
+                    elif isinstance(error_json, dict): # Handle cases where error is just a JSON dict
+                        user_display_error_message = f"API Error: {json.dumps(error_json)}"
+                    else: # Non-dict JSON error
+                        user_display_error_message = f"API Error: {str(error_json)}"
+            except json.JSONDecodeError:
+                # If not JSON, use the raw content if it's not too long, or a summary
+                if hasattr(e, "response") and e.response is not None and e.response.content:
+                    raw_content = e.response.content.decode(errors='ignore')
+                    if len(raw_content) < 200: # Arbitrary limit for display
+                        user_display_error_message = f"API Error (raw): {raw_content}"
                     else:
-                        error_message = f"API Error: {error_json}"
-            except Exception as json_error:
-                # If we can't parse the JSON, fall back to the original error message
-                logging.debug(
-                    f"[DEBUG] Failed to parse error response: {str(json_error)}"
+                        user_display_error_message = f"API Error (raw, status {status_code})"
+
+            request_logger.log_request_response(
+                operation_id=operation_id,
+                request_method=method, request_url=url,
+                response_status_code=status_code,
+                response_headers=dict(e.response.headers) if hasattr(e, "response") and e.response is not None else None,
+                response_content=error_content_for_log,
+                error_message=original_error_message # Log the original exception string as error
+            )
+
+            logging.debug(f"[DEBUG] API Error: {user_display_error_message} (Status: {status_code})")
+            if hasattr(e, "response") and e.response is not None and e.response.content:
+                logging.debug(f"[DEBUG] Response content: {e.response.content}")
+
+            # Retry if the status code is in our retry list and we haven't exhausted retries
+            if (status_code in self.retry_status_codes and
+                retry_count < self.max_retries):
+
+                delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
+                logging.warning(
+                    f"HTTP error {status_code}. "
+                    f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})"
+                )
+                time.sleep(delay)
+                return self.request(
+                    method=method,
+                    path=path,
+                    params=params,
+                    data=data,
+                    files=files,
+                    headers=headers,
+                    content_type=content_type,
+                    multipart_parser=multipart_parser,
+                    retry_count=retry_count + 1,
                 )
 
-            logging.debug(f"[DEBUG] API Error: {error_message} (Status: {status_code})")
-            if hasattr(e, "response") and e.response.content:
-                logging.debug(f"[DEBUG] Response content: {e.response.content}")
+            # Specific error messages for common status codes for user display
             if status_code == 401:
-                error_message = "Unauthorized: Please login first to use this node."
-            if status_code == 402:
-                error_message = "Payment Required: Please add credits to your account to use this node."
-            if status_code == 409:
-                error_message = "There is a problem with your account. Please contact support@comfy.org. "
-            if status_code == 429:
-                error_message = "Rate Limit Exceeded: Please try again later."
-            raise Exception(error_message)
+                user_display_error_message = "Unauthorized: Please login first to use this node."
+            elif status_code == 402:
+                user_display_error_message = "Payment Required: Please add credits to your account to use this node."
+            elif status_code == 409:
+                user_display_error_message = "There is a problem with your account. Please contact support@comfy.org."
+            elif status_code == 429:
+                user_display_error_message = "Rate Limit Exceeded: Please try again later."
+            # else, user_display_error_message remains as parsed from response or original HTTPError string
+
+            raise Exception(user_display_error_message) # Raise with the user-friendly message
 
         # Parse and return JSON response
         if response.content:
@@ -336,26 +613,126 @@ class ApiClient:
         upload_url: str,
         file: io.BytesIO | str,
         content_type: str | None = None,
+        max_retries: int = 3,
+        retry_delay: float = 1.0,
+        retry_backoff_factor: float = 2.0,
     ):
-        """Upload a file to the API. Make sure the file has a filename equal to what the url expects.
+        """Upload a file to the API with retry logic.
 
         Args:
             upload_url: The URL to upload to
             file: Either a file path string, BytesIO object, or tuple of (file_path, filename)
-            mime_type: Optional mime type to set for the upload
+            content_type: Optional mime type to set for the upload
+            max_retries: Maximum number of retry attempts
+            retry_delay: Initial delay between retries in seconds
+            retry_backoff_factor: Multiplier for the delay after each retry
         """
         headers = {}
         if content_type:
             headers["Content-Type"] = content_type
 
+        # Prepare the file data
         if isinstance(file, io.BytesIO):
             file.seek(0)  # Ensure we're at the start of the file
             data = file.read()
-            return requests.put(upload_url, data=data, headers=headers)
         elif isinstance(file, str):
             with open(file, "rb") as f:
                 data = f.read()
-                return requests.put(upload_url, data=data, headers=headers)
+        else:
+            raise ValueError("File must be either a BytesIO object or a file path string")
+
+        # Try the upload with retries
+        last_exception = None
+        operation_id = f"upload_{upload_url.split('/')[-1]}_{uuid.uuid4().hex[:8]}" # Simplified ID for uploads
+
+        # Log initial attempt (without full file data for brevity)
+        request_logger.log_request_response(
+            operation_id=operation_id,
+            request_method="PUT",
+            request_url=upload_url,
+            request_headers=headers,
+            request_data=f"[File data of type {content_type or 'unknown'}, size {len(data)} bytes]"
+        )
+
+        for retry_attempt in range(max_retries + 1):
+            try:
+                response = requests.put(upload_url, data=data, headers=headers)
+                response.raise_for_status()
+                request_logger.log_request_response(
+                    operation_id=operation_id,
+                    request_method="PUT", request_url=upload_url, # For context
+                    response_status_code=response.status_code,
+                    response_headers=dict(response.headers),
+                    response_content="File uploaded successfully." # Or response.text if available
+                )
+                return response
+
+            except (requests.ConnectionError, requests.Timeout, requests.HTTPError) as e:
+                last_exception = e
+                error_message_for_log = f"{type(e).__name__}: {str(e)}"
+                response_content_for_log = None
+                status_code_for_log = None
+                headers_for_log = None
+
+                if hasattr(e, 'response') and e.response is not None:
+                    status_code_for_log = e.response.status_code
+                    headers_for_log = dict(e.response.headers)
+                    try:
+                        response_content_for_log = e.response.json()
+                    except json.JSONDecodeError:
+                        response_content_for_log = e.response.content
+
+
+                request_logger.log_request_response(
+                    operation_id=operation_id,
+                    request_method="PUT", request_url=upload_url,
+                    response_status_code=status_code_for_log,
+                    response_headers=headers_for_log,
+                    response_content=response_content_for_log,
+                    error_message=error_message_for_log
+                )
+
+                if retry_attempt < max_retries:
+                    delay = retry_delay * (retry_backoff_factor ** retry_attempt)
+                    logging.warning(
+                        f"File upload failed: {str(e)}. "
+                        f"Retrying in {delay:.2f}s ({retry_attempt + 1}/{max_retries})"
+                    )
+                    time.sleep(delay)
+                else:
+                    break # Max retries reached
+
+        # If we've exhausted all retries, determine the final error type and raise
+        final_error_message = f"Failed to upload file after {max_retries + 1} attempts. Error: {str(last_exception)}"
+        try:
+            # Check basic internet connectivity
+            check_response = requests.get("https://www.google.com", timeout=5.0, verify=True) # Assuming verify=True is desired
+            if check_response.status_code >= 500: # Google itself has an issue (rare)
+                 final_error_message = (f"Failed to upload file. Internet connectivity check to Google failed "
+                                       f"(status {check_response.status_code}). Original error: {str(last_exception)}")
+                 # Not raising LocalNetworkError here as Google itself might be down.
+            # If Google is reachable, the issue is likely with the upload server or a more specific local problem
+            # not caught by a simple Google ping (e.g., DNS for the specific upload URL, firewall).
+            # The original last_exception is probably most relevant.
+
+        except (requests.RequestException, socket.error) as conn_check_exc:
+            # Could not reach Google, likely a local network issue
+            final_error_message = (f"Failed to upload file due to network connectivity issues "
+                                   f"(cannot reach Google: {str(conn_check_exc)}). "
+                                   f"Original upload error: {str(last_exception)}")
+            request_logger.log_request_response( # Log final failure reason
+                operation_id=operation_id,
+                request_method="PUT", request_url=upload_url,
+                error_message=final_error_message
+            )
+            raise LocalNetworkError(final_error_message) from last_exception
+
+        request_logger.log_request_response( # Log final failure reason if not LocalNetworkError
+            operation_id=operation_id,
+            request_method="PUT", request_url=upload_url,
+            error_message=final_error_message
+        )
+        raise Exception(final_error_message) from last_exception
 
 
 class ApiEndpoint(Generic[T, R]):
@@ -403,6 +780,9 @@ class SynchronousOperation(Generic[T, R]):
         verify_ssl: bool = True,
         content_type: str = "application/json",
         multipart_parser: Callable = None,
+        max_retries: int = 3,
+        retry_delay: float = 1.0,
+        retry_backoff_factor: float = 2.0,
     ):
         self.endpoint = endpoint
         self.request = request
@@ -419,8 +799,12 @@ class SynchronousOperation(Generic[T, R]):
         self.files = files
         self.content_type = content_type
         self.multipart_parser = multipart_parser
+        self.max_retries = max_retries
+        self.retry_delay = retry_delay
+        self.retry_backoff_factor = retry_backoff_factor
+
     def execute(self, client: Optional[ApiClient] = None) -> R:
-        """Execute the API operation using the provided client or create one"""
+        """Execute the API operation using the provided client or create one with retry support"""
         try:
             # Create client if not provided
             if client is None:
@@ -430,6 +814,9 @@ class SynchronousOperation(Generic[T, R]):
                     comfy_api_key=self.comfy_api_key,
                     timeout=self.timeout,
                     verify_ssl=self.verify_ssl,
+                    max_retries=self.max_retries,
+                    retry_delay=self.retry_delay,
+                    retry_backoff_factor=self.retry_backoff_factor,
                 )
 
             # Convert request model to dict, but use None for EmptyRequest
@@ -443,11 +830,6 @@ class SynchronousOperation(Generic[T, R]):
                     if isinstance(value, Enum):
                         request_dict[key] = value.value
 
-            if request_dict:
-               for key, value in request_dict.items():
-                   if isinstance(value, Enum):
-                       request_dict[key] = value.value
-
             # Debug log for request
             logging.debug(
                 f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}"
@@ -455,7 +837,7 @@ class SynchronousOperation(Generic[T, R]):
             logging.debug(f"[DEBUG] Request Data: {json.dumps(request_dict, indent=2)}")
             logging.debug(f"[DEBUG] Query Params: {self.endpoint.query_params}")
 
-            # Make the request
+            # Make the request with built-in retry
             resp = client.request(
                 method=self.endpoint.method.value,
                 path=self.endpoint.path,
@@ -476,8 +858,18 @@ class SynchronousOperation(Generic[T, R]):
             # Parse and return the response
             return self._parse_response(resp)
 
+        except LocalNetworkError as e:
+            # Propagate specific network error types
+            logging.error(f"[ERROR] Local network error: {str(e)}")
+            raise
+
+        except ApiServerError as e:
+            # Propagate API server errors
+            logging.error(f"[ERROR] API server error: {str(e)}")
+            raise
+
         except Exception as e:
-            logging.error(f"[DEBUG] API Exception: {str(e)}")
+            logging.error(f"[ERROR] API Exception: {str(e)}")
             raise Exception(str(e))
 
     def _parse_response(self, resp):
@@ -511,12 +903,19 @@ class PollingOperation(Generic[T, R]):
         failed_statuses: list,
         status_extractor: Callable[[R], str],
         progress_extractor: Callable[[R], float] = None,
+        result_url_extractor: Callable[[R], str] = None,
         request: Optional[T] = None,
         api_base: str | None = None,
         auth_token: Optional[str] = None,
         comfy_api_key: Optional[str] = None,
         auth_kwargs: Optional[Dict[str,str]] = None,
         poll_interval: float = 5.0,
+        max_poll_attempts: int = 120,  # Default max polling attempts (10 minutes with 5s interval)
+        max_retries: int = 3,  # Max retries per individual API call
+        retry_delay: float = 1.0,
+        retry_backoff_factor: float = 2.0,
+        estimated_duration: Optional[float] = None,
+        node_id: Optional[str] = None,
     ):
         self.poll_endpoint = poll_endpoint
         self.request = request
@@ -527,12 +926,19 @@ class PollingOperation(Generic[T, R]):
             self.auth_token = auth_kwargs.get("auth_token", self.auth_token)
             self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key)
         self.poll_interval = poll_interval
+        self.max_poll_attempts = max_poll_attempts
+        self.max_retries = max_retries
+        self.retry_delay = retry_delay
+        self.retry_backoff_factor = retry_backoff_factor
+        self.estimated_duration = estimated_duration
 
         # Polling configuration
         self.status_extractor = status_extractor or (
             lambda x: getattr(x, "status", None)
         )
         self.progress_extractor = progress_extractor
+        self.result_url_extractor = result_url_extractor
+        self.node_id = node_id
         self.completed_statuses = completed_statuses
         self.failed_statuses = failed_statuses
 
@@ -548,11 +954,46 @@ class PollingOperation(Generic[T, R]):
                     base_url=self.api_base,
                     auth_token=self.auth_token,
                     comfy_api_key=self.comfy_api_key,
+                    max_retries=self.max_retries,
+                    retry_delay=self.retry_delay,
+                    retry_backoff_factor=self.retry_backoff_factor,
                 )
             return self._poll_until_complete(client)
+        except LocalNetworkError as e:
+            # Provide clear message for local network issues
+            raise Exception(
+                f"Polling failed due to local network issues. Please check your internet connection. "
+                f"Details: {str(e)}"
+            ) from e
+        except ApiServerError as e:
+            # Provide clear message for API server issues
+            raise Exception(
+                f"Polling failed due to API server issues. The service may be experiencing problems. "
+                f"Please try again later. Details: {str(e)}"
+            ) from e
         except Exception as e:
             raise Exception(f"Error during polling: {str(e)}")
 
+    def _display_text_on_node(self, text: str):
+        """Sends text to the client which will be displayed on the node in the UI"""
+        if not self.node_id:
+            return
+
+        PromptServer.instance.send_progress_text(text, self.node_id)
+
+    def _display_time_progress_on_node(self, time_completed: int):
+        if not self.node_id:
+            return
+
+        if self.estimated_duration is not None:
+            estimated_time_remaining = max(
+                0, int(self.estimated_duration) - int(time_completed)
+            )
+            message = f"Task in progress: {time_completed:.0f}s (~{estimated_time_remaining:.0f}s remaining)"
+        else:
+            message = f"Task in progress: {time_completed:.0f}s"
+        self._display_text_on_node(message)
+
     def _check_task_status(self, response: R) -> TaskStatus:
         """Check task status using the status extractor function"""
         try:
@@ -569,10 +1010,13 @@ class PollingOperation(Generic[T, R]):
     def _poll_until_complete(self, client: ApiClient) -> R:
         """Poll until the task is complete"""
         poll_count = 0
+        consecutive_errors = 0
+        max_consecutive_errors = min(5, self.max_retries * 2)  # Limit consecutive errors
+
         if self.progress_extractor:
             progress = utils.ProgressBar(PROGRESS_BAR_MAX)
 
-        while True:
+        while poll_count < self.max_poll_attempts:
             try:
                 poll_count += 1
                 logging.debug(f"[DEBUG] Polling attempt #{poll_count}")
@@ -599,8 +1043,12 @@ class PollingOperation(Generic[T, R]):
                     data=request_dict,
                 )
 
+                # Successfully got a response, reset consecutive error count
+                consecutive_errors = 0
+
                 # Parse response
                 response_obj = self.poll_endpoint.response_model.model_validate(resp)
+
                 # Check if task is complete
                 status = self._check_task_status(response_obj)
                 logging.debug(f"[DEBUG] Task Status: {status}")
@@ -612,7 +1060,15 @@ class PollingOperation(Generic[T, R]):
                         progress.update_absolute(new_progress, total=PROGRESS_BAR_MAX)
 
                 if status == TaskStatus.COMPLETED:
-                    logging.debug("[DEBUG] Task completed successfully")
+                    message = "Task completed successfully"
+                    if self.result_url_extractor:
+                        result_url = self.result_url_extractor(response_obj)
+                        if result_url:
+                            message = f"Result URL: {result_url}"
+                    else:
+                        message = "Task completed successfully!"
+                    logging.debug(f"[DEBUG] {message}")
+                    self._display_text_on_node(message)
                     self.final_response = response_obj
                     if self.progress_extractor:
                         progress.update(100)
@@ -628,8 +1084,43 @@ class PollingOperation(Generic[T, R]):
                 logging.debug(
                     f"[DEBUG] Waiting {self.poll_interval} seconds before next poll"
                 )
+                for i in range(int(self.poll_interval)):
+                    time_completed = (poll_count * self.poll_interval) + i
+                    self._display_time_progress_on_node(time_completed)
+                    time.sleep(1)
+
+            except (LocalNetworkError, ApiServerError) as e:
+                # For network-related errors, increment error count and potentially abort
+                consecutive_errors += 1
+                if consecutive_errors >= max_consecutive_errors:
+                    raise Exception(
+                        f"Polling aborted after {consecutive_errors} consecutive network errors: {str(e)}"
+                    ) from e
+
+                # Log the error but continue polling
+                logging.warning(
+                    f"Network error during polling (attempt {poll_count}/{self.max_poll_attempts}): {str(e)}. "
+                    f"Will retry in {self.poll_interval} seconds."
+                )
                 time.sleep(self.poll_interval)
 
             except Exception as e:
+                # For other errors, increment count and potentially abort
+                consecutive_errors += 1
+                if consecutive_errors >= max_consecutive_errors or status == TaskStatus.FAILED:
+                    raise Exception(
+                        f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}"
+                    ) from e
+
                 logging.error(f"[DEBUG] Polling error: {str(e)}")
-                raise Exception(f"Error while polling: {str(e)}")
+                logging.warning(
+                    f"Error during polling (attempt {poll_count}/{self.max_poll_attempts}): {str(e)}. "
+                    f"Will retry in {self.poll_interval} seconds."
+                )
+                time.sleep(self.poll_interval)
+
+        # If we've exhausted all polling attempts
+        raise Exception(
+            f"Polling timed out after {poll_count} attempts ({poll_count * self.poll_interval} seconds). "
+            f"The operation may still be running on the server but is taking longer than expected."
+        )

comfy_api_nodes/apis/request_logger.py

@@ -0,0 +1,125 @@
+import os
+import datetime
+import json
+import logging
+import folder_paths
+
+# Get the logger instance
+logger = logging.getLogger(__name__)
+
+def get_log_directory():
+    """
+    Ensures the API log directory exists within ComfyUI's temp directory
+    and returns its path.
+    """
+    base_temp_dir = folder_paths.get_temp_directory()
+    log_dir = os.path.join(base_temp_dir, "api_logs")
+    try:
+        os.makedirs(log_dir, exist_ok=True)
+    except Exception as e:
+        logger.error(f"Error creating API log directory {log_dir}: {e}")
+        # Fallback to base temp directory if sub-directory creation fails
+        return base_temp_dir
+    return log_dir
+
+def _format_data_for_logging(data):
+    """Helper to format data (dict, str, bytes) for logging."""
+    if isinstance(data, bytes):
+        try:
+            return data.decode('utf-8')  # Try to decode as text
+        except UnicodeDecodeError:
+            return f"[Binary data of length {len(data)} bytes]"
+    elif isinstance(data, (dict, list)):
+        try:
+            return json.dumps(data, indent=2, ensure_ascii=False)
+        except TypeError:
+            return str(data) # Fallback for non-serializable objects
+    return str(data)
+
+def log_request_response(
+    operation_id: str,
+    request_method: str,
+    request_url: str,
+    request_headers: dict | None = None,
+    request_params: dict | None = None,
+    request_data: any = None,
+    response_status_code: int | None = None,
+    response_headers: dict | None = None,
+    response_content: any = None,
+    error_message: str | None = None
+):
+    """
+    Logs API request and response details to a file in the temp/api_logs directory.
+    """
+    log_dir = get_log_directory()
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+    filename = f"{timestamp}_{operation_id.replace('/', '_').replace(':', '_')}.log"
+    filepath = os.path.join(log_dir, filename)
+
+    log_content = []
+
+    log_content.append(f"Timestamp: {datetime.datetime.now().isoformat()}")
+    log_content.append(f"Operation ID: {operation_id}")
+    log_content.append("-" * 30 + " REQUEST " + "-" * 30)
+    log_content.append(f"Method: {request_method}")
+    log_content.append(f"URL: {request_url}")
+    if request_headers:
+        log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
+    if request_params:
+        log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
+    if request_data:
+        log_content.append(f"Data/Body:\n{_format_data_for_logging(request_data)}")
+
+    log_content.append("\n" + "-" * 30 + " RESPONSE " + "-" * 30)
+    if response_status_code is not None:
+        log_content.append(f"Status Code: {response_status_code}")
+    if response_headers:
+        log_content.append(f"Headers:\n{_format_data_for_logging(response_headers)}")
+    if response_content:
+        log_content.append(f"Content:\n{_format_data_for_logging(response_content)}")
+    if error_message:
+        log_content.append(f"Error:\n{error_message}")
+
+    try:
+        with open(filepath, "w", encoding="utf-8") as f:
+            f.write("\n".join(log_content))
+        logger.debug(f"API log saved to: {filepath}")
+    except Exception as e:
+        logger.error(f"Error writing API log to {filepath}: {e}")
+
+if __name__ == '__main__':
+    # Example usage (for testing the logger directly)
+    logger.setLevel(logging.DEBUG)
+    # Mock folder_paths for direct execution if not running within ComfyUI full context
+    if not hasattr(folder_paths, 'get_temp_directory'):
+        class MockFolderPaths:
+            def get_temp_directory(self):
+                # Create a local temp dir for testing if needed
+                p = os.path.join(os.path.dirname(__file__), 'temp_test_logs')
+                os.makedirs(p, exist_ok=True)
+                return p
+        folder_paths = MockFolderPaths()
+
+    log_request_response(
+        operation_id="test_operation_get",
+        request_method="GET",
+        request_url="https://api.example.com/test",
+        request_headers={"Authorization": "Bearer testtoken"},
+        request_params={"param1": "value1"},
+        response_status_code=200,
+        response_content={"message": "Success!"}
+    )
+    log_request_response(
+        operation_id="test_operation_post_error",
+        request_method="POST",
+        request_url="https://api.example.com/submit",
+        request_data={"key": "value", "nested": {"num": 123}},
+        error_message="Connection timed out"
+    )
+    log_request_response(
+        operation_id="test_binary_response",
+        request_method="GET",
+        request_url="https://api.example.com/image.png",
+        response_status_code=200,
+        response_content=b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR...' # Sample binary data
+    )

comfy_api_nodes/apis/rodin_api.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+from enum import Enum
+from typing import Optional, List
+from pydantic import BaseModel, Field
+
+
+class Rodin3DGenerateRequest(BaseModel):
+    seed: int = Field(..., description="seed_")
+    tier: str = Field(..., description="Tier of generation.")
+    material: str = Field(..., description="The material type.")
+    quality: str = Field(..., description="The generation quality of the mesh.")
+    mesh_mode: str = Field(..., description="It controls the type of faces of generated models.")
+
+class GenerateJobsData(BaseModel):
+    uuids: List[str] = Field(..., description="str LIST")
+    subscription_key: str = Field(..., description="subscription key")
+
+class Rodin3DGenerateResponse(BaseModel):
+    message: Optional[str] = Field(None, description="Return message.")
+    prompt: Optional[str] = Field(None, description="Generated Prompt from image.")
+    submit_time: Optional[str] = Field(None, description="Submit Time")
+    uuid: Optional[str] = Field(None, description="Task str")
+    jobs: Optional[GenerateJobsData] = Field(None, description="Details of jobs")
+
+class JobStatus(str, Enum):
+    """
+    Status for jobs
+    """
+    Done = "Done"
+    Failed = "Failed"
+    Generating = "Generating"
+    Waiting = "Waiting"
+
+class Rodin3DCheckStatusRequest(BaseModel):
+    subscription_key: str = Field(..., description="subscription from generate endpoint")
+
+class JobItem(BaseModel):
+    uuid: str = Field(..., description="uuid")
+    status: JobStatus = Field(...,description="Status Currently")
+
+class Rodin3DCheckStatusResponse(BaseModel):
+    jobs: List[JobItem] = Field(..., description="Job status List")
+
+class Rodin3DDownloadRequest(BaseModel):
+    task_uuid: str = Field(..., description="Task str")
+
+class RodinResourceItem(BaseModel):
+    url: str = Field(..., description="Download Url")
+    name: str = Field(..., description="File name with ext")
+
+class Rodin3DDownloadResponse(BaseModel):
+    list: List[RodinResourceItem] = Field(..., description="Source List")
+
+
+
+

comfy_api_nodes/apis/tripo_api.py

@@ -0,0 +1,275 @@
+from __future__ import annotations
+from comfy_api_nodes.apis import (
+    TripoModelVersion,
+    TripoTextureQuality,
+)
+from enum import Enum
+from typing import Optional, List, Dict, Any, Union
+
+from pydantic import BaseModel, Field, RootModel
+
+class TripoStyle(str, Enum):
+    PERSON_TO_CARTOON = "person:person2cartoon"
+    ANIMAL_VENOM = "animal:venom"
+    OBJECT_CLAY = "object:clay"
+    OBJECT_STEAMPUNK = "object:steampunk"
+    OBJECT_CHRISTMAS = "object:christmas"
+    OBJECT_BARBIE = "object:barbie"
+    GOLD = "gold"
+    ANCIENT_BRONZE = "ancient_bronze"
+    NONE = "None"
+
+class TripoTaskType(str, Enum):
+    TEXT_TO_MODEL = "text_to_model"
+    IMAGE_TO_MODEL = "image_to_model"
+    MULTIVIEW_TO_MODEL = "multiview_to_model"
+    TEXTURE_MODEL = "texture_model"
+    REFINE_MODEL = "refine_model"
+    ANIMATE_PRERIGCHECK = "animate_prerigcheck"
+    ANIMATE_RIG = "animate_rig"
+    ANIMATE_RETARGET = "animate_retarget"
+    STYLIZE_MODEL = "stylize_model"
+    CONVERT_MODEL = "convert_model"
+
+class TripoTextureAlignment(str, Enum):
+    ORIGINAL_IMAGE = "original_image"
+    GEOMETRY = "geometry"
+
+class TripoOrientation(str, Enum):
+    ALIGN_IMAGE = "align_image"
+    DEFAULT = "default"
+
+class TripoOutFormat(str, Enum):
+    GLB = "glb"
+    FBX = "fbx"
+
+class TripoTopology(str, Enum):
+    BIP = "bip"
+    QUAD = "quad"
+
+class TripoSpec(str, Enum):
+    MIXAMO = "mixamo"
+    TRIPO = "tripo"
+
+class TripoAnimation(str, Enum):
+    IDLE = "preset:idle"
+    WALK = "preset:walk"
+    CLIMB = "preset:climb"
+    JUMP = "preset:jump"
+    RUN = "preset:run"
+    SLASH = "preset:slash"
+    SHOOT = "preset:shoot"
+    HURT = "preset:hurt"
+    FALL = "preset:fall"
+    TURN = "preset:turn"
+
+class TripoStylizeStyle(str, Enum):
+    LEGO = "lego"
+    VOXEL = "voxel"
+    VORONOI = "voronoi"
+    MINECRAFT = "minecraft"
+
+class TripoConvertFormat(str, Enum):
+    GLTF = "GLTF"
+    USDZ = "USDZ"
+    FBX = "FBX"
+    OBJ = "OBJ"
+    STL = "STL"
+    _3MF = "3MF"
+
+class TripoTextureFormat(str, Enum):
+    BMP = "BMP"
+    DPX = "DPX"
+    HDR = "HDR"
+    JPEG = "JPEG"
+    OPEN_EXR = "OPEN_EXR"
+    PNG = "PNG"
+    TARGA = "TARGA"
+    TIFF = "TIFF"
+    WEBP = "WEBP"
+
+class TripoTaskStatus(str, Enum):
+    QUEUED = "queued"
+    RUNNING = "running"
+    SUCCESS = "success"
+    FAILED = "failed"
+    CANCELLED = "cancelled"
+    UNKNOWN = "unknown"
+    BANNED = "banned"
+    EXPIRED = "expired"
+
+class TripoFileTokenReference(BaseModel):
+    type: Optional[str] = Field(None, description='The type of the reference')
+    file_token: str
+
+class TripoUrlReference(BaseModel):
+    type: Optional[str] = Field(None, description='The type of the reference')
+    url: str
+
+class TripoObjectStorage(BaseModel):
+    bucket: str
+    key: str
+
+class TripoObjectReference(BaseModel):
+    type: str
+    object: TripoObjectStorage
+
+class TripoFileEmptyReference(BaseModel):
+    pass
+
+class TripoFileReference(RootModel):
+    root: Union[TripoFileTokenReference, TripoUrlReference, TripoObjectReference, TripoFileEmptyReference]
+
+class TripoGetStsTokenRequest(BaseModel):
+    format: str = Field(..., description='The format of the image')
+
+class TripoTextToModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.TEXT_TO_MODEL, description='Type of task')
+    prompt: str = Field(..., description='The text prompt describing the model to generate', max_length=1024)
+    negative_prompt: Optional[str] = Field(None, description='The negative text prompt', max_length=1024)
+    model_version: Optional[TripoModelVersion] = TripoModelVersion.V2_5
+    face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to')
+    texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model')
+    pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model')
+    image_seed: Optional[int] = Field(None, description='The seed for the text')
+    model_seed: Optional[int] = Field(None, description='The seed for the model')
+    texture_seed: Optional[int] = Field(None, description='The seed for the texture')
+    texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard
+    style: Optional[TripoStyle] = None
+    auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model')
+    quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model')
+
+class TripoImageToModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.IMAGE_TO_MODEL, description='Type of task')
+    file: TripoFileReference = Field(..., description='The file reference to convert to a model')
+    model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation')
+    face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to')
+    texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model')
+    pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model')
+    model_seed: Optional[int] = Field(None, description='The seed for the model')
+    texture_seed: Optional[int] = Field(None, description='The seed for the texture')
+    texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard
+    texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method')
+    style: Optional[TripoStyle] = Field(None, description='The style to apply to the generated model')
+    auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model')
+    orientation: Optional[TripoOrientation] = TripoOrientation.DEFAULT
+    quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model')
+
+class TripoMultiviewToModelRequest(BaseModel):
+    type: TripoTaskType = TripoTaskType.MULTIVIEW_TO_MODEL
+    files: List[TripoFileReference] = Field(..., description='The file references to convert to a model')
+    model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation')
+    orthographic_projection: Optional[bool] = Field(False, description='Whether to use orthographic projection')
+    face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to')
+    texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model')
+    pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model')
+    model_seed: Optional[int] = Field(None, description='The seed for the model')
+    texture_seed: Optional[int] = Field(None, description='The seed for the texture')
+    texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard
+    texture_alignment: Optional[TripoTextureAlignment] = TripoTextureAlignment.ORIGINAL_IMAGE
+    auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model')
+    orientation: Optional[TripoOrientation] = Field(TripoOrientation.DEFAULT, description='The orientation for the model')
+    quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model')
+
+class TripoTextureModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.TEXTURE_MODEL, description='Type of task')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+    texture: Optional[bool] = Field(True, description='Whether to apply texture to the model')
+    pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the model')
+    model_seed: Optional[int] = Field(None, description='The seed for the model')
+    texture_seed: Optional[int] = Field(None, description='The seed for the texture')
+    texture_quality: Optional[TripoTextureQuality] = Field(None, description='The quality of the texture')
+    texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method')
+
+class TripoRefineModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.REFINE_MODEL, description='Type of task')
+    draft_model_task_id: str = Field(..., description='The task ID of the draft model')
+
+class TripoAnimatePrerigcheckRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.ANIMATE_PRERIGCHECK, description='Type of task')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+
+class TripoAnimateRigRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.ANIMATE_RIG, description='Type of task')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+    out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format')
+    spec: Optional[TripoSpec] = Field(TripoSpec.TRIPO, description='The specification for rigging')
+
+class TripoAnimateRetargetRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.ANIMATE_RETARGET, description='Type of task')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+    animation: TripoAnimation = Field(..., description='The animation to apply')
+    out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format')
+    bake_animation: Optional[bool] = Field(True, description='Whether to bake the animation')
+
+class TripoStylizeModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.STYLIZE_MODEL, description='Type of task')
+    style: TripoStylizeStyle = Field(..., description='The style to apply to the model')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+    block_size: Optional[int] = Field(80, description='The block size for stylization')
+
+class TripoConvertModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.CONVERT_MODEL, description='Type of task')
+    format: TripoConvertFormat = Field(..., description='The format to convert to')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+    quad: Optional[bool] = Field(False, description='Whether to apply quad to the model')
+    force_symmetry: Optional[bool] = Field(False, description='Whether to force symmetry')
+    face_limit: Optional[int] = Field(10000, description='The number of faces to limit the conversion to')
+    flatten_bottom: Optional[bool] = Field(False, description='Whether to flatten the bottom of the model')
+    flatten_bottom_threshold: Optional[float] = Field(0.01, description='The threshold for flattening the bottom')
+    texture_size: Optional[int] = Field(4096, description='The size of the texture')
+    texture_format: Optional[TripoTextureFormat] = Field(TripoTextureFormat.JPEG, description='The format of the texture')
+    pivot_to_center_bottom: Optional[bool] = Field(False, description='Whether to pivot to the center bottom')
+
+class TripoTaskRequest(RootModel):
+    root: Union[
+        TripoTextToModelRequest,
+        TripoImageToModelRequest,
+        TripoMultiviewToModelRequest,
+        TripoTextureModelRequest,
+        TripoRefineModelRequest,
+        TripoAnimatePrerigcheckRequest,
+        TripoAnimateRigRequest,
+        TripoAnimateRetargetRequest,
+        TripoStylizeModelRequest,
+        TripoConvertModelRequest
+    ]
+
+class TripoTaskOutput(BaseModel):
+    model: Optional[str] = Field(None, description='URL to the model')
+    base_model: Optional[str] = Field(None, description='URL to the base model')
+    pbr_model: Optional[str] = Field(None, description='URL to the PBR model')
+    rendered_image: Optional[str] = Field(None, description='URL to the rendered image')
+    riggable: Optional[bool] = Field(None, description='Whether the model is riggable')
+
+class TripoTask(BaseModel):
+    task_id: str = Field(..., description='The task ID')
+    type: Optional[str] = Field(None, description='The type of task')
+    status: Optional[TripoTaskStatus] = Field(None, description='The status of the task')
+    input: Optional[Dict[str, Any]] = Field(None, description='The input parameters for the task')
+    output: Optional[TripoTaskOutput] = Field(None, description='The output of the task')
+    progress: Optional[int] = Field(None, description='The progress of the task', ge=0, le=100)
+    create_time: Optional[int] = Field(None, description='The creation time of the task')
+    running_left_time: Optional[int] = Field(None, description='The estimated time left for the task')
+    queue_position: Optional[int] = Field(None, description='The position in the queue')
+
+class TripoTaskResponse(BaseModel):
+    code: int = Field(0, description='The response code')
+    data: TripoTask = Field(..., description='The task data')
+
+class TripoGeneralResponse(BaseModel):
+    code: int = Field(0, description='The response code')
+    data: Dict[str, str] = Field(..., description='The task ID data')
+
+class TripoBalanceData(BaseModel):
+    balance: float = Field(..., description='The account balance')
+    frozen: float = Field(..., description='The frozen balance')
+
+class TripoBalanceResponse(BaseModel):
+    code: int = Field(0, description='The response code')
+    data: TripoBalanceData = Field(..., description='The balance data')
+
+class TripoErrorResponse(BaseModel):
+    code: int = Field(..., description='The error code')
+    message: str = Field(..., description='The error message')
+    suggestion: str = Field(..., description='The suggestion for fixing the error')

comfy_api_nodes/nodes_bfl.py

@@ -1,5 +1,6 @@
 import io
 from inspect import cleandoc
+from typing import Union, Optional
 from comfy.comfy_types.node_typing import IO, ComfyNodeABC
 from comfy_api_nodes.apis.bfl_api import (
     BFLStatus,
@@ -8,6 +9,7 @@ from comfy_api_nodes.apis.bfl_api import (
     BFLFluxCannyImageRequest,
     BFLFluxDepthImageRequest,
     BFLFluxProGenerateRequest,
+    BFLFluxKontextProGenerateRequest,
     BFLFluxProUltraGenerateRequest,
     BFLFluxProGenerateResponse,
 )
@@ -30,6 +32,7 @@ import requests
 import torch
 import base64
 import time
+from server import PromptServer
 
 
 def convert_mask_to_image(mask: torch.Tensor):
@@ -42,14 +45,19 @@ def convert_mask_to_image(mask: torch.Tensor):
 
 
 def handle_bfl_synchronous_operation(
-    operation: SynchronousOperation, timeout_bfl_calls=360
+    operation: SynchronousOperation,
+    timeout_bfl_calls=360,
+    node_id: Union[str, None] = None,
 ):
     response_api: BFLFluxProGenerateResponse = operation.execute()
     return _poll_until_generated(
-        response_api.polling_url, timeout=timeout_bfl_calls
+        response_api.polling_url, timeout=timeout_bfl_calls, node_id=node_id
     )
 
-def _poll_until_generated(polling_url: str, timeout=360):
+
+def _poll_until_generated(
+    polling_url: str, timeout=360, node_id: Union[str, None] = None
+):
     # used bfl-comfy-nodes to verify code implementation:
     # https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main
     start_time = time.time()
@@ -61,11 +69,21 @@ def _poll_until_generated(polling_url: str, timeout=360):
     request = requests.Request(method=HttpMethod.GET, url=polling_url)
     # NOTE: should True loop be replaced with checking if workflow has been interrupted?
     while True:
+        if node_id:
+            time_elapsed = time.time() - start_time
+            PromptServer.instance.send_progress_text(
+                f"Generating ({time_elapsed:.0f}s)", node_id
+            )
+
         response = requests.Session().send(request.prepare())
         if response.status_code == 200:
             result = response.json()
             if result["status"] == BFLStatus.ready:
                 img_url = result["result"]["sample"]
+                if node_id:
+                    PromptServer.instance.send_progress_text(
+                        f"Result URL: {img_url}", node_id
+                    )
                 img_response = requests.get(img_url)
                 return process_image_response(img_response)
             elif result["status"] in [
@@ -180,6 +198,7 @@ class FluxProUltraImageNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -212,6 +231,7 @@ class FluxProUltraImageNode(ComfyNodeABC):
         seed=0,
         image_prompt=None,
         image_prompt_strength=0.1,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
         if image_prompt is None:
@@ -246,10 +266,162 @@ class FluxProUltraImageNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 
+class FluxKontextProImageNode(ComfyNodeABC):
+    """
+    Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio.
+    """
+
+    MINIMUM_RATIO = 1 / 4
+    MAXIMUM_RATIO = 4 / 1
+    MINIMUM_RATIO_STR = "1:4"
+    MAXIMUM_RATIO_STR = "4:1"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": (
+                    IO.STRING,
+                    {
+                        "multiline": True,
+                        "default": "",
+                        "tooltip": "Prompt for the image generation - specify what and how to edit.",
+                    },
+                ),
+                "aspect_ratio": (
+                    IO.STRING,
+                    {
+                        "default": "16:9",
+                        "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.",
+                    },
+                ),
+                "guidance": (
+                    IO.FLOAT,
+                    {
+                        "default": 3.0,
+                        "min": 0.1,
+                        "max": 99.0,
+                        "step": 0.1,
+                        "tooltip": "Guidance strength for the image generation process"
+                    },
+                ),
+                "steps": (
+                    IO.INT,
+                    {
+                        "default": 50,
+                        "min": 1,
+                        "max": 150,
+                        "tooltip": "Number of steps for the image generation process"
+                    },
+                ),
+                "seed": (
+                    IO.INT,
+                    {
+                        "default": 1234,
+                        "min": 0,
+                        "max": 0xFFFFFFFFFFFFFFFF,
+                        "control_after_generate": True,
+                        "tooltip": "The random seed used for creating the noise.",
+                    },
+                ),
+                "prompt_upsampling": (
+                    IO.BOOLEAN,
+                    {
+                        "default": False,
+                        "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+                    },
+                ),
+            },
+            "optional": {
+                "input_image": (IO.IMAGE,),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, aspect_ratio: str):
+        try:
+            validate_aspect_ratio(
+                aspect_ratio,
+                minimum_ratio=cls.MINIMUM_RATIO,
+                maximum_ratio=cls.MAXIMUM_RATIO,
+                minimum_ratio_str=cls.MINIMUM_RATIO_STR,
+                maximum_ratio_str=cls.MAXIMUM_RATIO_STR,
+            )
+        except Exception as e:
+            return str(e)
+        return True
+
+    RETURN_TYPES = (IO.IMAGE,)
+    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
+    FUNCTION = "api_call"
+    API_NODE = True
+    CATEGORY = "api node/image/BFL"
+
+    BFL_PATH = "/proxy/bfl/flux-kontext-pro/generate"
+
+    def api_call(
+        self,
+        prompt: str,
+        aspect_ratio: str,
+        guidance: float,
+        steps: int,
+        input_image: Optional[torch.Tensor]=None,
+        seed=0,
+        prompt_upsampling=False,
+        unique_id: Union[str, None] = None,
+        **kwargs,
+    ):
+        if input_image is None:
+            validate_string(prompt, strip_whitespace=False)
+        operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=self.BFL_PATH,
+                method=HttpMethod.POST,
+                request_model=BFLFluxKontextProGenerateRequest,
+                response_model=BFLFluxProGenerateResponse,
+            ),
+            request=BFLFluxKontextProGenerateRequest(
+                prompt=prompt,
+                prompt_upsampling=prompt_upsampling,
+                guidance=round(guidance, 1),
+                steps=steps,
+                seed=seed,
+                aspect_ratio=validate_aspect_ratio(
+                    aspect_ratio,
+                    minimum_ratio=self.MINIMUM_RATIO,
+                    maximum_ratio=self.MAXIMUM_RATIO,
+                    minimum_ratio_str=self.MINIMUM_RATIO_STR,
+                    maximum_ratio_str=self.MAXIMUM_RATIO_STR,
+                ),
+                input_image=(
+                    input_image
+                    if input_image is None
+                    else convert_image_to_base64(input_image)
+                )
+            ),
+            auth_kwargs=kwargs,
+        )
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
+        return (output_image,)
+
+
+class FluxKontextMaxImageNode(FluxKontextProImageNode):
+    """
+    Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio.
+    """
+
+    DESCRIPTION = cleandoc(__doc__ or "")
+    BFL_PATH = "/proxy/bfl/flux-kontext-max/generate"
+
 
 class FluxProImageNode(ComfyNodeABC):
     """
@@ -320,6 +492,7 @@ class FluxProImageNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -338,6 +511,7 @@ class FluxProImageNode(ComfyNodeABC):
         seed=0,
         image_prompt=None,
         # image_prompt_strength=0.1,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
         image_prompt = (
@@ -363,7 +537,7 @@ class FluxProImageNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 
@@ -457,11 +631,11 @@ class FluxProExpandNode(ComfyNodeABC):
                     },
                 ),
             },
-            "optional": {
-            },
+            "optional": {},
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -483,6 +657,7 @@ class FluxProExpandNode(ComfyNodeABC):
         steps: int,
         guidance: float,
         seed=0,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
         image = convert_image_to_base64(image)
@@ -508,7 +683,7 @@ class FluxProExpandNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 
@@ -568,11 +743,11 @@ class FluxProFillNode(ComfyNodeABC):
                     },
                 ),
             },
-            "optional": {
-            },
+            "optional": {},
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -591,13 +766,14 @@ class FluxProFillNode(ComfyNodeABC):
         steps: int,
         guidance: float,
         seed=0,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
         # prepare mask
         mask = resize_mask_to_image(mask, image)
         mask = convert_image_to_base64(convert_mask_to_image(mask))
         # make sure image will have alpha channel removed
-        image = convert_image_to_base64(image[:,:,:,:3])
+        image = convert_image_to_base64(image[:, :, :, :3])
 
         operation = SynchronousOperation(
             endpoint=ApiEndpoint(
@@ -617,7 +793,7 @@ class FluxProFillNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 
@@ -702,11 +878,11 @@ class FluxProCannyNode(ComfyNodeABC):
                     },
                 ),
             },
-            "optional": {
-            },
+            "optional": {},
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -727,9 +903,10 @@ class FluxProCannyNode(ComfyNodeABC):
         steps: int,
         guidance: float,
         seed=0,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
-        control_image = convert_image_to_base64(control_image[:,:,:,:3])
+        control_image = convert_image_to_base64(control_image[:, :, :, :3])
         preprocessed_image = None
 
         # scale canny threshold between 0-500, to match BFL's API
@@ -765,7 +942,7 @@ class FluxProCannyNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 
@@ -830,11 +1007,11 @@ class FluxProDepthNode(ComfyNodeABC):
                     },
                 ),
             },
-            "optional": {
-            },
+            "optional": {},
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -853,6 +1030,7 @@ class FluxProDepthNode(ComfyNodeABC):
         steps: int,
         guidance: float,
         seed=0,
+        unique_id: Union[str, None] = None,
         **kwargs,
     ):
         control_image = convert_image_to_base64(control_image[:,:,:,:3])
@@ -880,7 +1058,7 @@ class FluxProDepthNode(ComfyNodeABC):
             ),
             auth_kwargs=kwargs,
         )
-        output_image = handle_bfl_synchronous_operation(operation)
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
         return (output_image,)
 
 
@@ -889,6 +1067,8 @@ class FluxProDepthNode(ComfyNodeABC):
 NODE_CLASS_MAPPINGS = {
     "FluxProUltraImageNode": FluxProUltraImageNode,
     # "FluxProImageNode": FluxProImageNode,
+    "FluxKontextProImageNode": FluxKontextProImageNode,
+    "FluxKontextMaxImageNode": FluxKontextMaxImageNode,
     "FluxProExpandNode": FluxProExpandNode,
     "FluxProFillNode": FluxProFillNode,
     "FluxProCannyNode": FluxProCannyNode,
@@ -899,6 +1079,8 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
     "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image",
     # "FluxProImageNode": "Flux 1.1 [pro] Image",
+    "FluxKontextProImageNode": "Flux.1 Kontext [pro] Image",
+    "FluxKontextMaxImageNode": "Flux.1 Kontext [max] Image",
     "FluxProExpandNode": "Flux.1 Expand Image",
     "FluxProFillNode": "Flux.1 Fill Image",
     "FluxProCannyNode": "Flux.1 Canny Control Image",

comfy_api_nodes/nodes_gemini.py

@@ -0,0 +1,446 @@
+"""
+API Nodes for Gemini Multimodal LLM Usage via Remote API
+See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
+"""
+
+import os
+from enum import Enum
+from typing import Optional, Literal
+
+import torch
+
+import folder_paths
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
+from server import PromptServer
+from comfy_api_nodes.apis import (
+    GeminiContent,
+    GeminiGenerateContentRequest,
+    GeminiGenerateContentResponse,
+    GeminiInlineData,
+    GeminiPart,
+    GeminiMimeType,
+)
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+)
+from comfy_api_nodes.apinode_utils import (
+    validate_string,
+    audio_to_base64_string,
+    video_to_base64_string,
+    tensor_to_base64_string,
+)
+
+
+GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini"
+GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024  # 20 MB
+
+
+class GeminiModel(str, Enum):
+    """
+    Gemini Model Names allowed by comfy-api
+    """
+
+    gemini_2_5_pro_preview_05_06 = "gemini-2.5-pro-preview-05-06"
+    gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17"
+
+
+def get_gemini_endpoint(
+    model: GeminiModel,
+) -> ApiEndpoint[GeminiGenerateContentRequest, GeminiGenerateContentResponse]:
+    """
+    Get the API endpoint for a given Gemini model.
+
+    Args:
+        model: The Gemini model to use, either as enum or string value.
+
+    Returns:
+        ApiEndpoint configured for the specific Gemini model.
+    """
+    if isinstance(model, str):
+        model = GeminiModel(model)
+    return ApiEndpoint(
+        path=f"{GEMINI_BASE_ENDPOINT}/{model.value}",
+        method=HttpMethod.POST,
+        request_model=GeminiGenerateContentRequest,
+        response_model=GeminiGenerateContentResponse,
+    )
+
+
+class GeminiNode(ComfyNodeABC):
+    """
+    Node to generate text responses from a Gemini model.
+
+    This node allows users to interact with Google's Gemini AI models, providing
+    multimodal inputs (text, images, audio, video, files) to generate coherent
+    text responses. The node works with the latest Gemini models, handling the
+    API communication and response parsing.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "prompt": (
+                    IO.STRING,
+                    {
+                        "multiline": True,
+                        "default": "",
+                        "tooltip": "Text inputs to the model, used to generate a response. You can include detailed instructions, questions, or context for the model.",
+                    },
+                ),
+                "model": (
+                    IO.COMBO,
+                    {
+                        "tooltip": "The Gemini model to use for generating responses.",
+                        "options": [model.value for model in GeminiModel],
+                        "default": GeminiModel.gemini_2_5_pro_preview_05_06.value,
+                    },
+                ),
+                "seed": (
+                    IO.INT,
+                    {
+                        "default": 42,
+                        "min": 0,
+                        "max": 0xFFFFFFFFFFFFFFFF,
+                        "control_after_generate": True,
+                        "tooltip": "When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used.",
+                    },
+                ),
+            },
+            "optional": {
+                "images": (
+                    IO.IMAGE,
+                    {
+                        "default": None,
+                        "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.",
+                    },
+                ),
+                "audio": (
+                    IO.AUDIO,
+                    {
+                        "tooltip": "Optional audio to use as context for the model.",
+                        "default": None,
+                    },
+                ),
+                "video": (
+                    IO.VIDEO,
+                    {
+                        "tooltip": "Optional video to use as context for the model.",
+                        "default": None,
+                    },
+                ),
+                "files": (
+                    "GEMINI_INPUT_FILES",
+                    {
+                        "default": None,
+                        "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the Gemini Generate Content Input Files node.",
+                    },
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    DESCRIPTION = "Generate text responses with Google's Gemini AI model. You can provide multiple types of inputs (text, images, audio, video) as context for generating more relevant and meaningful responses."
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "api_call"
+    CATEGORY = "api node/text/Gemini"
+    API_NODE = True
+
+    def get_parts_from_response(
+        self, response: GeminiGenerateContentResponse
+    ) -> list[GeminiPart]:
+        """
+        Extract all parts from the Gemini API response.
+
+        Args:
+            response: The API response from Gemini.
+
+        Returns:
+            List of response parts from the first candidate.
+        """
+        return response.candidates[0].content.parts
+
+    def get_parts_by_type(
+        self, response: GeminiGenerateContentResponse, part_type: Literal["text"] | str
+    ) -> list[GeminiPart]:
+        """
+        Filter response parts by their type.
+
+        Args:
+            response: The API response from Gemini.
+            part_type: Type of parts to extract ("text" or a MIME type).
+
+        Returns:
+            List of response parts matching the requested type.
+        """
+        parts = []
+        for part in self.get_parts_from_response(response):
+            if part_type == "text" and hasattr(part, "text") and part.text:
+                parts.append(part)
+            elif (
+                hasattr(part, "inlineData")
+                and part.inlineData
+                and part.inlineData.mimeType == part_type
+            ):
+                parts.append(part)
+            # Skip parts that don't match the requested type
+        return parts
+
+    def get_text_from_response(self, response: GeminiGenerateContentResponse) -> str:
+        """
+        Extract and concatenate all text parts from the response.
+
+        Args:
+            response: The API response from Gemini.
+
+        Returns:
+            Combined text from all text parts in the response.
+        """
+        parts = self.get_parts_by_type(response, "text")
+        return "\n".join([part.text for part in parts])
+
+    def create_video_parts(self, video_input: IO.VIDEO, **kwargs) -> list[GeminiPart]:
+        """
+        Convert video input to Gemini API compatible parts.
+
+        Args:
+            video_input: Video tensor from ComfyUI.
+            **kwargs: Additional arguments to pass to the conversion function.
+
+        Returns:
+            List of GeminiPart objects containing the encoded video.
+        """
+        from comfy_api.util import VideoContainer, VideoCodec
+        base_64_string = video_to_base64_string(
+            video_input,
+            container_format=VideoContainer.MP4,
+            codec=VideoCodec.H264
+        )
+        return [
+            GeminiPart(
+                inlineData=GeminiInlineData(
+                    mimeType=GeminiMimeType.video_mp4,
+                    data=base_64_string,
+                )
+            )
+        ]
+
+    def create_audio_parts(self, audio_input: IO.AUDIO) -> list[GeminiPart]:
+        """
+        Convert audio input to Gemini API compatible parts.
+
+        Args:
+            audio_input: Audio input from ComfyUI, containing waveform tensor and sample rate.
+
+        Returns:
+            List of GeminiPart objects containing the encoded audio.
+        """
+        audio_parts: list[GeminiPart] = []
+        for batch_index in range(audio_input["waveform"].shape[0]):
+            # Recreate an IO.AUDIO object for the given batch dimension index
+            audio_at_index = {
+                "waveform": audio_input["waveform"][batch_index].unsqueeze(0),
+                "sample_rate": audio_input["sample_rate"],
+            }
+            # Convert to MP3 format for compatibility with Gemini API
+            audio_bytes = audio_to_base64_string(
+                audio_at_index,
+                container_format="mp3",
+                codec_name="libmp3lame",
+            )
+            audio_parts.append(
+                GeminiPart(
+                    inlineData=GeminiInlineData(
+                        mimeType=GeminiMimeType.audio_mp3,
+                        data=audio_bytes,
+                    )
+                )
+            )
+        return audio_parts
+
+    def create_image_parts(self, image_input: torch.Tensor) -> list[GeminiPart]:
+        """
+        Convert image tensor input to Gemini API compatible parts.
+
+        Args:
+            image_input: Batch of image tensors from ComfyUI.
+
+        Returns:
+            List of GeminiPart objects containing the encoded images.
+        """
+        image_parts: list[GeminiPart] = []
+        for image_index in range(image_input.shape[0]):
+            image_as_b64 = tensor_to_base64_string(
+                image_input[image_index].unsqueeze(0)
+            )
+            image_parts.append(
+                GeminiPart(
+                    inlineData=GeminiInlineData(
+                        mimeType=GeminiMimeType.image_png,
+                        data=image_as_b64,
+                    )
+                )
+            )
+        return image_parts
+
+    def create_text_part(self, text: str) -> GeminiPart:
+        """
+        Create a text part for the Gemini API request.
+
+        Args:
+            text: The text content to include in the request.
+
+        Returns:
+            A GeminiPart object with the text content.
+        """
+        return GeminiPart(text=text)
+
+    def api_call(
+        self,
+        prompt: str,
+        model: GeminiModel,
+        images: Optional[IO.IMAGE] = None,
+        audio: Optional[IO.AUDIO] = None,
+        video: Optional[IO.VIDEO] = None,
+        files: Optional[list[GeminiPart]] = None,
+        unique_id: Optional[str] = None,
+        **kwargs,
+    ) -> tuple[str]:
+        # Validate inputs
+        validate_string(prompt, strip_whitespace=False)
+
+        # Create parts list with text prompt as the first part
+        parts: list[GeminiPart] = [self.create_text_part(prompt)]
+
+        # Add other modal parts
+        if images is not None:
+            image_parts = self.create_image_parts(images)
+            parts.extend(image_parts)
+        if audio is not None:
+            parts.extend(self.create_audio_parts(audio))
+        if video is not None:
+            parts.extend(self.create_video_parts(video))
+        if files is not None:
+            parts.extend(files)
+
+        # Create response
+        response = SynchronousOperation(
+            endpoint=get_gemini_endpoint(model),
+            request=GeminiGenerateContentRequest(
+                contents=[
+                    GeminiContent(
+                        role="user",
+                        parts=parts,
+                    )
+                ]
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+
+        # Get result output
+        output_text = self.get_text_from_response(response)
+        if unique_id and output_text:
+            PromptServer.instance.send_progress_text(output_text, node_id=unique_id)
+
+        return (output_text or "Empty response from Gemini model...",)
+
+
+class GeminiInputFiles(ComfyNodeABC):
+    """
+    Loads and formats input files for use with the Gemini API.
+
+    This node allows users to include text (.txt) and PDF (.pdf) files as input
+    context for the Gemini model. Files are converted to the appropriate format
+    required by the API and can be chained together to include multiple files
+    in a single request.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        """
+        For details about the supported file input types, see:
+        https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
+        """
+        input_dir = folder_paths.get_input_directory()
+        input_files = [
+            f
+            for f in os.scandir(input_dir)
+            if f.is_file()
+            and (f.name.endswith(".txt") or f.name.endswith(".pdf"))
+            and f.stat().st_size < GEMINI_MAX_INPUT_FILE_SIZE
+        ]
+        input_files = sorted(input_files, key=lambda x: x.name)
+        input_files = [f.name for f in input_files]
+        return {
+            "required": {
+                "file": (
+                    IO.COMBO,
+                    {
+                        "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.",
+                        "options": input_files,
+                        "default": input_files[0] if input_files else None,
+                    },
+                ),
+            },
+            "optional": {
+                "GEMINI_INPUT_FILES": (
+                    "GEMINI_INPUT_FILES",
+                    {
+                        "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.",
+                        "default": None,
+                    },
+                ),
+            },
+        }
+
+    DESCRIPTION = "Loads and prepares input files to include as inputs for Gemini LLM nodes. The files will be read by the Gemini model when generating a response. The contents of the text file count toward the token limit. 🛈 TIP: Can be chained together with other Gemini Input File nodes."
+    RETURN_TYPES = ("GEMINI_INPUT_FILES",)
+    FUNCTION = "prepare_files"
+    CATEGORY = "api node/text/Gemini"
+
+    def create_file_part(self, file_path: str) -> GeminiPart:
+        mime_type = (
+            GeminiMimeType.pdf
+            if file_path.endswith(".pdf")
+            else GeminiMimeType.text_plain
+        )
+        # Use base64 string directly, not the data URI
+        with open(file_path, "rb") as f:
+            file_content = f.read()
+        import base64
+        base64_str = base64.b64encode(file_content).decode("utf-8")
+
+        return GeminiPart(
+            inlineData=GeminiInlineData(
+                mimeType=mime_type,
+                data=base64_str,
+            )
+        )
+
+    def prepare_files(
+        self, file: str, GEMINI_INPUT_FILES: list[GeminiPart] = []
+    ) -> tuple[list[GeminiPart]]:
+        """
+        Loads and formats input files for Gemini API.
+        """
+        file_path = folder_paths.get_annotated_filepath(file)
+        input_file_content = self.create_file_part(file_path)
+        files = [input_file_content] + GEMINI_INPUT_FILES
+        return (files,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "GeminiNode": GeminiNode,
+    "GeminiInputFiles": GeminiInputFiles,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "GeminiNode": "Google Gemini",
+    "GeminiInputFiles": "Gemini Input Files",
+}

comfy_api_nodes/nodes_ideogram.py

@@ -23,6 +23,7 @@ from comfy_api_nodes.apinode_utils import (
     bytesio_to_image_tensor,
     resize_mask_to_image,
 )
+from server import PromptServer
 
 V1_V1_RES_MAP = {
   "Auto":"AUTO",
@@ -232,6 +233,19 @@ def download_and_process_images(image_urls):
     return stacked_tensors
 
 
+def display_image_urls_on_node(image_urls, node_id):
+    if node_id and image_urls:
+        if len(image_urls) == 1:
+            PromptServer.instance.send_progress_text(
+                f"Generated Image URL:\n{image_urls[0]}", node_id
+            )
+        else:
+            urls_text = "Generated Image URLs:\n" + "\n".join(
+                f"{i+1}. {url}" for i, url in enumerate(image_urls)
+            )
+            PromptServer.instance.send_progress_text(urls_text, node_id)
+
+
 class IdeogramV1(ComfyNodeABC):
     """
     Generates images using the Ideogram V1 model.
@@ -304,6 +318,7 @@ class IdeogramV1(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -322,6 +337,7 @@ class IdeogramV1(ComfyNodeABC):
         seed=0,
         negative_prompt="",
         num_images=1,
+        unique_id=None,
         **kwargs,
     ):
         # Determine the model based on turbo setting
@@ -361,6 +377,7 @@ class IdeogramV1(ComfyNodeABC):
         if not image_urls:
             raise Exception("No image URLs were generated in the response")
 
+        display_image_urls_on_node(image_urls, unique_id)
         return (download_and_process_images(image_urls),)
 
 
@@ -460,6 +477,7 @@ class IdeogramV2(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -481,6 +499,7 @@ class IdeogramV2(ComfyNodeABC):
         negative_prompt="",
         num_images=1,
         color_palette="",
+        unique_id=None,
         **kwargs,
     ):
         aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None)
@@ -534,6 +553,7 @@ class IdeogramV2(ComfyNodeABC):
         if not image_urls:
             raise Exception("No image URLs were generated in the response")
 
+        display_image_urls_on_node(image_urls, unique_id)
         return (download_and_process_images(image_urls),)
 
 class IdeogramV3(ComfyNodeABC):
@@ -623,6 +643,7 @@ class IdeogramV3(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -643,6 +664,7 @@ class IdeogramV3(ComfyNodeABC):
         seed=0,
         num_images=1,
         rendering_speed="BALANCED",
+        unique_id=None,
         **kwargs,
     ):
         # Check if both image and mask are provided for editing mode
@@ -762,6 +784,7 @@ class IdeogramV3(ComfyNodeABC):
         if not image_urls:
             raise Exception("No image URLs were generated in the response")
 
+        display_image_urls_on_node(image_urls, unique_id)
         return (download_and_process_images(image_urls),)
 
 
@@ -776,4 +799,3 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "IdeogramV2": "Ideogram V2",
     "IdeogramV3": "Ideogram V3",
 }
-

comfy_api_nodes/nodes_kling.py

@@ -6,6 +6,7 @@ For source of truth on the allowed permutations of request fields, please refere
 
 from __future__ import annotations
 from typing import Optional, TypeVar, Any
+from collections.abc import Callable
 import math
 import logging
 
@@ -64,6 +65,12 @@ from comfy_api_nodes.apinode_utils import (
     download_url_to_image_tensor,
 )
 from comfy_api_nodes.mapper_utils import model_field_to_node_input
+from comfy_api_nodes.util.validation_utils import (
+    validate_image_dimensions,
+    validate_image_aspect_ratio,
+    validate_video_dimensions,
+    validate_video_duration,
+)
 from comfy_api.input.basic_types import AudioInput
 from comfy_api.input.video_types import VideoInput
 from comfy_api.input_impl import VideoFromFile
@@ -79,13 +86,20 @@ PATH_CHARACTER_IMAGE = f"/proxy/kling/{KLING_API_VERSION}/images/generations"
 PATH_VIRTUAL_TRY_ON = f"/proxy/kling/{KLING_API_VERSION}/images/kolors-virtual-try-on"
 PATH_IMAGE_GENERATIONS = f"/proxy/kling/{KLING_API_VERSION}/images/generations"
 
-
 MAX_PROMPT_LENGTH_T2V = 2500
 MAX_PROMPT_LENGTH_I2V = 500
 MAX_PROMPT_LENGTH_IMAGE_GEN = 500
 MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200
 MAX_PROMPT_LENGTH_LIP_SYNC = 120
 
+AVERAGE_DURATION_T2V = 319
+AVERAGE_DURATION_I2V = 164
+AVERAGE_DURATION_LIP_SYNC = 455
+AVERAGE_DURATION_VIRTUAL_TRY_ON = 19
+AVERAGE_DURATION_IMAGE_GEN = 32
+AVERAGE_DURATION_VIDEO_EFFECTS = 320
+AVERAGE_DURATION_VIDEO_EXTEND = 320
+
 R = TypeVar("R")
 
 
@@ -95,7 +109,13 @@ class KlingApiError(Exception):
     pass
 
 
-def poll_until_finished(auth_kwargs: dict[str,str], api_endpoint: ApiEndpoint[Any, R]) -> R:
+def poll_until_finished(
+    auth_kwargs: dict[str, str],
+    api_endpoint: ApiEndpoint[Any, R],
+    result_url_extractor: Optional[Callable[[R], str]] = None,
+    estimated_duration: Optional[int] = None,
+    node_id: Optional[str] = None,
+) -> R:
     """Polls the Kling API endpoint until the task reaches a terminal state, then returns the response."""
     return PollingOperation(
         poll_endpoint=api_endpoint,
@@ -109,6 +129,9 @@ def poll_until_finished(auth_kwargs: dict[str,str], api_endpoint: ApiEndpoint[An
             else None
         ),
         auth_kwargs=auth_kwargs,
+        result_url_extractor=result_url_extractor,
+        estimated_duration=estimated_duration,
+        node_id=node_id,
     ).execute()
 
 
@@ -192,23 +215,8 @@ def validate_input_image(image: torch.Tensor) -> None:
 
     See: https://app.klingai.com/global/dev/document-api/apiReference/model/imageToVideo
     """
-    if len(image.shape) == 4:
-        height, width = image.shape[1], image.shape[2]
-    elif len(image.shape) == 3:
-        height, width = image.shape[0], image.shape[1]
-    else:
-        raise ValueError("Invalid image tensor shape.")
-
-    # Ensure minimum resolution is met
-    if height < 300:
-        raise ValueError("Image height must be at least 300px")
-    if width < 300:
-        raise ValueError("Image width must be at least 300px")
-
-    # Ensure aspect ratio is within acceptable range
-    aspect_ratio = width / height
-    if aspect_ratio < 1 / 2.5 or aspect_ratio > 2.5:
-        raise ValueError("Image aspect ratio must be between 1:2.5 and 2.5:1")
+    validate_image_dimensions(image, min_width=300, min_height=300)
+    validate_image_aspect_ratio(image, min_aspect_ratio=1 / 2.5, max_aspect_ratio=2.5)
 
 
 def get_camera_control_input_config(
@@ -227,7 +235,9 @@ def get_camera_control_input_config(
 
 
 def get_video_from_response(response) -> KlingVideoResult:
-    """Returns the first video object from the Kling video generation task result."""
+    """Returns the first video object from the Kling video generation task result.
+    Will raise an error if the response is not valid.
+    """
     video = response.data.task_result.videos[0]
     logging.info(
         "Kling task %s succeeded. Video URL: %s", response.data.task_id, video.url
@@ -235,12 +245,37 @@ def get_video_from_response(response) -> KlingVideoResult:
     return video
 
 
+def get_video_url_from_response(response) -> Optional[str]:
+    """Returns the first video url from the Kling video generation task result.
+    Will not raise an error if the response is not valid.
+    """
+    if response and is_valid_video_response(response):
+        return str(get_video_from_response(response).url)
+    else:
+        return None
+
+
 def get_images_from_response(response) -> list[KlingImageResult]:
+    """Returns the list of image objects from the Kling image generation task result.
+    Will raise an error if the response is not valid.
+    """
     images = response.data.task_result.images
     logging.info("Kling task %s succeeded. Images: %s", response.data.task_id, images)
     return images
 
 
+def get_images_urls_from_response(response) -> Optional[str]:
+    """Returns the list of image urls from the Kling image generation task result.
+    Will not raise an error if the response is not valid. If there is only one image, returns the url as a string. If there are multiple images, returns a list of urls.
+    """
+    if response and is_valid_image_response(response):
+        images = get_images_from_response(response)
+        image_urls = [str(image.url) for image in images]
+        return "\n".join(image_urls)
+    else:
+        return None
+
+
 def video_result_to_node_output(
     video: KlingVideoResult,
 ) -> tuple[VideoFromFile, str, str]:
@@ -312,6 +347,7 @@ class KlingCameraControls(KlingNodeBase):
     RETURN_TYPES = ("CAMERA_CONTROL",)
     RETURN_NAMES = ("camera_control",)
     FUNCTION = "main"
+    API_NODE = False  # This is just a helper node, it doesn't make an API call
 
     @classmethod
     def VALIDATE_INPUTS(
@@ -421,6 +457,7 @@ class KlingTextToVideoNode(KlingNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -428,7 +465,9 @@ class KlingTextToVideoNode(KlingNodeBase):
     RETURN_NAMES = ("VIDEO", "video_id", "duration")
     DESCRIPTION = "Kling Text to Video Node"
 
-    def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingText2VideoResponse:
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> KlingText2VideoResponse:
         return poll_until_finished(
             auth_kwargs,
             ApiEndpoint(
@@ -437,6 +476,9 @@ class KlingTextToVideoNode(KlingNodeBase):
                 request_model=EmptyRequest,
                 response_model=KlingText2VideoResponse,
             ),
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_T2V,
+            node_id=node_id,
         )
 
     def api_call(
@@ -449,6 +491,7 @@ class KlingTextToVideoNode(KlingNodeBase):
         camera_control: Optional[KlingCameraControl] = None,
         model_name: Optional[str] = None,
         duration: Optional[str] = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ) -> tuple[VideoFromFile, str, str]:
         validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
@@ -478,7 +521,9 @@ class KlingTextToVideoNode(KlingNodeBase):
         validate_task_creation_response(task_creation_response)
 
         task_id = task_creation_response.data.task_id
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_video_result_response(final_response)
 
         video = get_video_from_response(final_response)
@@ -528,6 +573,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -540,6 +586,7 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode):
         cfg_scale: float,
         aspect_ratio: str,
         camera_control: Optional[KlingCameraControl] = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         return super().api_call(
@@ -613,6 +660,7 @@ class KlingImage2VideoNode(KlingNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -620,7 +668,9 @@ class KlingImage2VideoNode(KlingNodeBase):
     RETURN_NAMES = ("VIDEO", "video_id", "duration")
     DESCRIPTION = "Kling Image to Video Node"
 
-    def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingImage2VideoResponse:
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> KlingImage2VideoResponse:
         return poll_until_finished(
             auth_kwargs,
             ApiEndpoint(
@@ -629,6 +679,9 @@ class KlingImage2VideoNode(KlingNodeBase):
                 request_model=KlingImage2VideoRequest,
                 response_model=KlingImage2VideoResponse,
             ),
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_I2V,
+            node_id=node_id,
         )
 
     def api_call(
@@ -643,6 +696,7 @@ class KlingImage2VideoNode(KlingNodeBase):
         duration: str,
         camera_control: Optional[KlingCameraControl] = None,
         end_frame: Optional[torch.Tensor] = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ) -> tuple[VideoFromFile]:
         validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V)
@@ -681,7 +735,9 @@ class KlingImage2VideoNode(KlingNodeBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_video_result_response(final_response)
 
         video = get_video_from_response(final_response)
@@ -734,6 +790,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -747,6 +804,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
         cfg_scale: float,
         aspect_ratio: str,
         camera_control: KlingCameraControl,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         return super().api_call(
@@ -759,6 +817,7 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
             prompt=prompt,
             negative_prompt=negative_prompt,
             camera_control=camera_control,
+            unique_id=unique_id,
             **kwargs,
         )
 
@@ -830,6 +889,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -844,6 +904,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode):
         cfg_scale: float,
         aspect_ratio: str,
         mode: str,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         mode, duration, model_name = KlingStartEndFrameNode.get_mode_string_mapping()[
@@ -859,6 +920,7 @@ class KlingStartEndFrameNode(KlingImage2VideoNode):
             aspect_ratio=aspect_ratio,
             duration=duration,
             end_frame=end_frame,
+            unique_id=unique_id,
             **kwargs,
         )
 
@@ -892,6 +954,7 @@ class KlingVideoExtendNode(KlingNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -899,7 +962,9 @@ class KlingVideoExtendNode(KlingNodeBase):
     RETURN_NAMES = ("VIDEO", "video_id", "duration")
     DESCRIPTION = "Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes."
 
-    def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingVideoExtendResponse:
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> KlingVideoExtendResponse:
         return poll_until_finished(
             auth_kwargs,
             ApiEndpoint(
@@ -908,6 +973,9 @@ class KlingVideoExtendNode(KlingNodeBase):
                 request_model=EmptyRequest,
                 response_model=KlingVideoExtendResponse,
             ),
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_VIDEO_EXTEND,
+            node_id=node_id,
         )
 
     def api_call(
@@ -916,6 +984,7 @@ class KlingVideoExtendNode(KlingNodeBase):
         negative_prompt: str,
         cfg_scale: float,
         video_id: str,
+        unique_id: Optional[str] = None,
         **kwargs,
     ) -> tuple[VideoFromFile, str, str]:
         validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
@@ -939,7 +1008,9 @@ class KlingVideoExtendNode(KlingNodeBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_video_result_response(final_response)
 
         video = get_video_from_response(final_response)
@@ -952,7 +1023,9 @@ class KlingVideoEffectsBase(KlingNodeBase):
     RETURN_TYPES = ("VIDEO", "STRING", "STRING")
     RETURN_NAMES = ("VIDEO", "video_id", "duration")
 
-    def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingVideoEffectsResponse:
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> KlingVideoEffectsResponse:
         return poll_until_finished(
             auth_kwargs,
             ApiEndpoint(
@@ -961,6 +1034,9 @@ class KlingVideoEffectsBase(KlingNodeBase):
                 request_model=EmptyRequest,
                 response_model=KlingVideoEffectsResponse,
             ),
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_VIDEO_EFFECTS,
+            node_id=node_id,
         )
 
     def api_call(
@@ -972,6 +1048,7 @@ class KlingVideoEffectsBase(KlingNodeBase):
         image_1: torch.Tensor,
         image_2: Optional[torch.Tensor] = None,
         mode: Optional[KlingVideoGenMode] = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         if dual_character:
@@ -1009,7 +1086,9 @@ class KlingVideoEffectsBase(KlingNodeBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_video_result_response(final_response)
 
         video = get_video_from_response(final_response)
@@ -1053,6 +1132,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -1068,6 +1148,7 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
         model_name: KlingCharacterEffectModelName,
         mode: KlingVideoGenMode,
         duration: KlingVideoGenDuration,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         video, _, duration = super().api_call(
@@ -1078,10 +1159,12 @@ class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
             duration=duration,
             image_1=image_left,
             image_2=image_right,
+            unique_id=unique_id,
             **kwargs,
         )
         return video, duration
 
+
 class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
     """Kling Single Image Video Effect Node"""
 
@@ -1117,6 +1200,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -1128,6 +1212,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
         effect_scene: KlingSingleImageEffectsScene,
         model_name: KlingSingleImageEffectModelName,
         duration: KlingVideoGenDuration,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         return super().api_call(
@@ -1136,6 +1221,7 @@ class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
             model_name=model_name,
             duration=duration,
             image_1=image,
+            unique_id=unique_id,
             **kwargs,
         )
 
@@ -1146,6 +1232,17 @@ class KlingLipSyncBase(KlingNodeBase):
     RETURN_TYPES = ("VIDEO", "STRING", "STRING")
     RETURN_NAMES = ("VIDEO", "video_id", "duration")
 
+    def validate_lip_sync_video(self, video: VideoInput):
+        """
+        Validates the input video adheres to the expectations of the Kling Lip Sync API:
+        - Video length does not exceed 10s and is not shorter than 2s
+        - Length and width dimensions should both be between 720px and 1920px
+
+        See: https://app.klingai.com/global/dev/document-api/apiReference/model/videoTolip
+        """
+        validate_video_dimensions(video, 720, 1920)
+        validate_video_duration(video, 2, 10)
+
     def validate_text(self, text: str):
         if not text:
             raise ValueError("Text is required")
@@ -1154,7 +1251,9 @@ class KlingLipSyncBase(KlingNodeBase):
                 f"Text is too long. Maximum length is {MAX_PROMPT_LENGTH_LIP_SYNC} characters."
             )
 
-    def get_response(self, task_id: str, auth_kwargs: dict[str,str]) -> KlingLipSyncResponse:
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> KlingLipSyncResponse:
         """Polls the Kling API endpoint until the task reaches a terminal state."""
         return poll_until_finished(
             auth_kwargs,
@@ -1164,6 +1263,9 @@ class KlingLipSyncBase(KlingNodeBase):
                 request_model=EmptyRequest,
                 response_model=KlingLipSyncResponse,
             ),
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_LIP_SYNC,
+            node_id=node_id,
         )
 
     def api_call(
@@ -1175,10 +1277,12 @@ class KlingLipSyncBase(KlingNodeBase):
         text: Optional[str] = None,
         voice_speed: Optional[float] = None,
         voice_id: Optional[str] = None,
-        **kwargs
+        unique_id: Optional[str] = None,
+        **kwargs,
     ) -> tuple[VideoFromFile, str, str]:
         if text:
             self.validate_text(text)
+        self.validate_lip_sync_video(video)
 
         # Upload video to Comfy API and get download URL
         video_url = upload_video_to_comfyapi(video, auth_kwargs=kwargs)
@@ -1217,7 +1321,9 @@ class KlingLipSyncBase(KlingNodeBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_video_result_response(final_response)
 
         video = get_video_from_response(final_response)
@@ -1243,16 +1349,18 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
-    DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file."
+    DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length."
 
     def api_call(
         self,
         video: VideoInput,
         audio: AudioInput,
         voice_language: str,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         return super().api_call(
@@ -1260,6 +1368,7 @@ class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
             audio=audio,
             voice_language=voice_language,
             mode="audio2video",
+            unique_id=unique_id,
             **kwargs,
         )
 
@@ -1352,10 +1461,11 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
-    DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt."
+    DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length."
 
     def api_call(
         self,
@@ -1363,6 +1473,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
         text: str,
         voice: str,
         voice_speed: float,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         voice_id, voice_language = KlingLipSyncTextToVideoNode.get_voice_config()[voice]
@@ -1373,6 +1484,7 @@ class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
             voice_id=voice_id,
             voice_speed=voice_speed,
             mode="text2video",
+            unique_id=unique_id,
             **kwargs,
         )
 
@@ -1413,13 +1525,14 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
-    DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human."
+    DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background."
 
     def get_response(
-        self, task_id: str, auth_kwargs: dict[str,str] = None
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
     ) -> KlingVirtualTryOnResponse:
         return poll_until_finished(
             auth_kwargs,
@@ -1429,6 +1542,9 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
                 request_model=EmptyRequest,
                 response_model=KlingVirtualTryOnResponse,
             ),
+            result_url_extractor=get_images_urls_from_response,
+            estimated_duration=AVERAGE_DURATION_VIRTUAL_TRY_ON,
+            node_id=node_id,
         )
 
     def api_call(
@@ -1436,6 +1552,7 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
         human_image: torch.Tensor,
         cloth_image: torch.Tensor,
         model_name: KlingVirtualTryOnModelName,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         initial_operation = SynchronousOperation(
@@ -1457,7 +1574,9 @@ class KlingVirtualTryOnNode(KlingImageGenerationBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_image_result_response(final_response)
 
         images = get_images_from_response(final_response)
@@ -1528,13 +1647,17 @@ class KlingImageGenerationNode(KlingImageGenerationBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
     DESCRIPTION = "Kling Image Generation Node. Generate an image from a text prompt with an optional reference image."
 
     def get_response(
-        self, task_id: str, auth_kwargs: Optional[dict[str,str]] = None
+        self,
+        task_id: str,
+        auth_kwargs: Optional[dict[str, str]],
+        node_id: Optional[str] = None,
     ) -> KlingImageGenerationsResponse:
         return poll_until_finished(
             auth_kwargs,
@@ -1544,6 +1667,9 @@ class KlingImageGenerationNode(KlingImageGenerationBase):
                 request_model=EmptyRequest,
                 response_model=KlingImageGenerationsResponse,
             ),
+            result_url_extractor=get_images_urls_from_response,
+            estimated_duration=AVERAGE_DURATION_IMAGE_GEN,
+            node_id=node_id,
         )
 
     def api_call(
@@ -1557,6 +1683,7 @@ class KlingImageGenerationNode(KlingImageGenerationBase):
         n: int,
         aspect_ratio: KlingImageGenAspectRatio,
         image: Optional[torch.Tensor] = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         self.validate_prompt(prompt, negative_prompt)
@@ -1589,7 +1716,9 @@ class KlingImageGenerationNode(KlingImageGenerationBase):
         validate_task_creation_response(task_creation_response)
         task_id = task_creation_response.data.task_id
 
-        final_response = self.get_response(task_id, auth_kwargs=kwargs)
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
         validate_image_result_response(final_response)
 
         images = get_images_from_response(final_response)

comfy_api_nodes/nodes_luma.py

@@ -36,11 +36,20 @@ from comfy_api_nodes.apinode_utils import (
     process_image_response,
     validate_string,
 )
+from server import PromptServer
 
 import requests
 import torch
 from io import BytesIO
 
+LUMA_T2V_AVERAGE_DURATION = 105
+LUMA_I2V_AVERAGE_DURATION = 100
+
+def image_result_url_extractor(response: LumaGeneration):
+    return response.assets.image if hasattr(response, "assets") and hasattr(response.assets, "image") else None
+
+def video_result_url_extractor(response: LumaGeneration):
+    return response.assets.video if hasattr(response, "assets") and hasattr(response.assets, "video") else None
 
 class LumaReferenceNode(ComfyNodeABC):
     """
@@ -204,6 +213,7 @@ class LumaImageGenerationNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -217,6 +227,7 @@ class LumaImageGenerationNode(ComfyNodeABC):
         image_luma_ref: LumaReferenceChain = None,
         style_image: torch.Tensor = None,
         character_image: torch.Tensor = None,
+        unique_id: str = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=True, min_length=3)
@@ -271,6 +282,8 @@ class LumaImageGenerationNode(ComfyNodeABC):
             completed_statuses=[LumaState.completed],
             failed_statuses=[LumaState.failed],
             status_extractor=lambda x: x.state,
+            result_url_extractor=image_result_url_extractor,
+            node_id=unique_id,
             auth_kwargs=kwargs,
         )
         response_poll = operation.execute()
@@ -353,6 +366,7 @@ class LumaImageModifyNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -363,6 +377,7 @@ class LumaImageModifyNode(ComfyNodeABC):
         image: torch.Tensor,
         image_weight: float,
         seed,
+        unique_id: str = None,
         **kwargs,
     ):
         # first, upload image
@@ -399,6 +414,8 @@ class LumaImageModifyNode(ComfyNodeABC):
             completed_statuses=[LumaState.completed],
             failed_statuses=[LumaState.failed],
             status_extractor=lambda x: x.state,
+            result_url_extractor=image_result_url_extractor,
+            node_id=unique_id,
             auth_kwargs=kwargs,
         )
         response_poll = operation.execute()
@@ -473,6 +490,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -486,6 +504,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
         loop: bool,
         seed,
         luma_concepts: LumaConceptChain = None,
+        unique_id: str = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False, min_length=3)
@@ -512,6 +531,9 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
         )
         response_api: LumaGeneration = operation.execute()
 
+        if unique_id:
+            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
+
         operation = PollingOperation(
             poll_endpoint=ApiEndpoint(
                 path=f"/proxy/luma/generations/{response_api.id}",
@@ -522,6 +544,9 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
             completed_statuses=[LumaState.completed],
             failed_statuses=[LumaState.failed],
             status_extractor=lambda x: x.state,
+            result_url_extractor=video_result_url_extractor,
+            node_id=unique_id,
+            estimated_duration=LUMA_T2V_AVERAGE_DURATION,
             auth_kwargs=kwargs,
         )
         response_poll = operation.execute()
@@ -597,6 +622,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -611,6 +637,7 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
         first_image: torch.Tensor = None,
         last_image: torch.Tensor = None,
         luma_concepts: LumaConceptChain = None,
+        unique_id: str = None,
         **kwargs,
     ):
         if first_image is None and last_image is None:
@@ -642,6 +669,9 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
         )
         response_api: LumaGeneration = operation.execute()
 
+        if unique_id:
+            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
+
         operation = PollingOperation(
             poll_endpoint=ApiEndpoint(
                 path=f"/proxy/luma/generations/{response_api.id}",
@@ -652,6 +682,9 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
             completed_statuses=[LumaState.completed],
             failed_statuses=[LumaState.failed],
             status_extractor=lambda x: x.state,
+            result_url_extractor=video_result_url_extractor,
+            node_id=unique_id,
+            estimated_duration=LUMA_I2V_AVERAGE_DURATION,
             auth_kwargs=kwargs,
         )
         response_poll = operation.execute()

comfy_api_nodes/nodes_minimax.py

@@ -1,3 +1,7 @@
+from typing import Union
+import logging
+import torch
+
 from comfy.comfy_types.node_typing import IO
 from comfy_api.input_impl.video_types import VideoFromFile
 from comfy_api_nodes.apis import (
@@ -20,16 +24,19 @@ from comfy_api_nodes.apinode_utils import (
     upload_images_to_comfyapi,
     validate_string,
 )
+from server import PromptServer
 
-import torch
-import logging
 
+I2V_AVERAGE_DURATION = 114
+T2V_AVERAGE_DURATION = 234
 
 class MinimaxTextToVideoNode:
     """
     Generates videos synchronously based on a prompt, and optional parameters using MiniMax's API.
     """
 
+    AVERAGE_DURATION = T2V_AVERAGE_DURATION
+
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -68,6 +75,7 @@ class MinimaxTextToVideoNode:
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -85,6 +93,7 @@ class MinimaxTextToVideoNode:
         model="T2V-01",
         image: torch.Tensor=None, # used for ImageToVideo
         subject: torch.Tensor=None, # used for SubjectToVideo
+        unique_id: Union[str, None]=None,
         **kwargs,
     ):
         '''
@@ -138,6 +147,8 @@ class MinimaxTextToVideoNode:
             completed_statuses=["Success"],
             failed_statuses=["Fail"],
             status_extractor=lambda x: x.status.value,
+            estimated_duration=self.AVERAGE_DURATION,
+            node_id=unique_id,
             auth_kwargs=kwargs,
         )
         task_result = video_generate_operation.execute()
@@ -164,6 +175,12 @@ class MinimaxTextToVideoNode:
                 f"No video was found in the response. Full response: {file_result.model_dump()}"
             )
         logging.info(f"Generated video URL: {file_url}")
+        if unique_id:
+            if hasattr(file_result.file, "backup_download_url"):
+                message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}"
+            else:
+                message = f"Result URL: {file_url}"
+            PromptServer.instance.send_progress_text(message, unique_id)
 
         video_io = download_url_to_bytesio(file_url)
         if video_io is None:
@@ -178,6 +195,8 @@ class MinimaxImageToVideoNode(MinimaxTextToVideoNode):
     Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API.
     """
 
+    AVERAGE_DURATION = I2V_AVERAGE_DURATION
+
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -223,6 +242,7 @@ class MinimaxImageToVideoNode(MinimaxTextToVideoNode):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -239,6 +259,8 @@ class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode):
     Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API.
     """
 
+    AVERAGE_DURATION = T2V_AVERAGE_DURATION
+
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -282,6 +304,7 @@ class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 

comfy_api_nodes/nodes_openai.py

@@ -1,29 +1,86 @@
 import io
+from typing import TypedDict, Optional
+import json
+import os
+import time
+import re
+import uuid
+from enum import Enum
 from inspect import cleandoc
 import numpy as np
 import torch
 from PIL import Image
-
 from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
+from server import PromptServer
+import folder_paths
 
 
 from comfy_api_nodes.apis import (
     OpenAIImageGenerationRequest,
     OpenAIImageEditRequest,
     OpenAIImageGenerationResponse,
+    OpenAICreateResponse,
+    OpenAIResponse,
+    CreateModelResponseProperties,
+    Item,
+    Includable,
+    OutputContent,
+    InputImageContent,
+    Detail,
+    InputTextContent,
+    InputMessage,
+    InputMessageContentList,
+    InputContent,
+    InputFileContent,
 )
 
 from comfy_api_nodes.apis.client import (
     ApiEndpoint,
     HttpMethod,
     SynchronousOperation,
+    PollingOperation,
+    EmptyRequest,
 )
 
 from comfy_api_nodes.apinode_utils import (
     downscale_image_tensor,
     validate_and_cast_response,
     validate_string,
+    tensor_to_base64_string,
+    text_filepath_to_data_uri,
 )
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+
+
+RESPONSES_ENDPOINT = "/proxy/openai/v1/responses"
+STARTING_POINT_ID_PATTERN = r"<starting_point_id:(.*)>"
+
+
+class HistoryEntry(TypedDict):
+    """Type definition for a single history entry in the chat."""
+
+    prompt: str
+    response: str
+    response_id: str
+    timestamp: float
+
+
+class ChatHistory(TypedDict):
+    """Type definition for the chat history dictionary."""
+
+    __annotations__: dict[str, list[HistoryEntry]]
+
+
+class SupportedOpenAIModel(str, Enum):
+    o4_mini = "o4-mini"
+    o1 = "o1"
+    o3 = "o3"
+    o1_pro = "o1-pro"
+    gpt_4o = "gpt-4o"
+    gpt_4_1 = "gpt-4.1"
+    gpt_4_1_mini = "gpt-4.1-mini"
+    gpt_4_1_nano = "gpt-4.1-nano"
+
 
 class OpenAIDalle2(ComfyNodeABC):
     """
@@ -96,6 +153,7 @@ class OpenAIDalle2(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -113,7 +171,8 @@ class OpenAIDalle2(ComfyNodeABC):
         mask=None,
         n=1,
         size="1024x1024",
-        **kwargs
+        unique_id=None,
+        **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
         model = "dall-e-2"
@@ -176,7 +235,7 @@ class OpenAIDalle2(ComfyNodeABC):
 
         response = operation.execute()
 
-        img_tensor = validate_and_cast_response(response)
+        img_tensor = validate_and_cast_response(response, node_id=unique_id)
         return (img_tensor,)
 
 
@@ -242,6 +301,7 @@ class OpenAIDalle3(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -258,7 +318,8 @@ class OpenAIDalle3(ComfyNodeABC):
         style="natural",
         quality="standard",
         size="1024x1024",
-        **kwargs
+        unique_id=None,
+        **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
         model = "dall-e-3"
@@ -284,7 +345,7 @@ class OpenAIDalle3(ComfyNodeABC):
 
         response = operation.execute()
 
-        img_tensor = validate_and_cast_response(response)
+        img_tensor = validate_and_cast_response(response, node_id=unique_id)
         return (img_tensor,)
 
 
@@ -375,6 +436,7 @@ class OpenAIGPTImage1(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -394,12 +456,13 @@ class OpenAIGPTImage1(ComfyNodeABC):
         mask=None,
         n=1,
         size="1024x1024",
-        **kwargs
+        unique_id=None,
+        **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
         model = "gpt-image-1"
         path = "/proxy/openai/images/generations"
-        content_type="application/json"
+        content_type = "application/json"
         request_class = OpenAIImageGenerationRequest
         img_binaries = []
         mask_binary = None
@@ -408,7 +471,7 @@ class OpenAIGPTImage1(ComfyNodeABC):
         if image is not None:
             path = "/proxy/openai/images/edits"
             request_class = OpenAIImageEditRequest
-            content_type ="multipart/form-data"
+            content_type = "multipart/form-data"
 
             batch_size = image.shape[0]
 
@@ -476,21 +539,470 @@ class OpenAIGPTImage1(ComfyNodeABC):
 
         response = operation.execute()
 
-        img_tensor = validate_and_cast_response(response)
+        img_tensor = validate_and_cast_response(response, node_id=unique_id)
         return (img_tensor,)
 
 
-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
+class OpenAITextNode(ComfyNodeABC):
+    """
+    Base class for OpenAI text generation nodes.
+    """
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "api_call"
+    CATEGORY = "api node/text/OpenAI"
+    API_NODE = True
+
+
+class OpenAIChatNode(OpenAITextNode):
+    """
+    Node to generate text responses from an OpenAI model.
+    """
+
+    def __init__(self) -> None:
+        """Initialize the chat node with a new session ID and empty history."""
+        self.current_session_id: str = str(uuid.uuid4())
+        self.history: dict[str, list[HistoryEntry]] = {}
+        self.previous_response_id: Optional[str] = None
+
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "prompt": (
+                    IO.STRING,
+                    {
+                        "multiline": True,
+                        "default": "",
+                        "tooltip": "Text inputs to the model, used to generate a response.",
+                    },
+                ),
+                "persist_context": (
+                    IO.BOOLEAN,
+                    {
+                        "default": True,
+                        "tooltip": "Persist chat context between calls (multi-turn conversation)",
+                    },
+                ),
+                "model": model_field_to_node_input(
+                    IO.COMBO,
+                    OpenAICreateResponse,
+                    "model",
+                    enum_type=SupportedOpenAIModel,
+                ),
+            },
+            "optional": {
+                "images": (
+                    IO.IMAGE,
+                    {
+                        "default": None,
+                        "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.",
+                    },
+                ),
+                "files": (
+                    "OPENAI_INPUT_FILES",
+                    {
+                        "default": None,
+                        "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the OpenAI Chat Input Files node.",
+                    },
+                ),
+                "advanced_options": (
+                    "OPENAI_CHAT_CONFIG",
+                    {
+                        "default": None,
+                        "tooltip": "Optional configuration for the model. Accepts inputs from the OpenAI Chat Advanced Options node.",
+                    },
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    DESCRIPTION = "Generate text responses from an OpenAI model."
+
+    def get_result_response(
+        self,
+        response_id: str,
+        include: Optional[list[Includable]] = None,
+        auth_kwargs: Optional[dict[str, str]] = None,
+    ) -> OpenAIResponse:
+        """
+        Retrieve a model response with the given ID from the OpenAI API.
+
+        Args:
+            response_id (str): The ID of the response to retrieve.
+            include (Optional[List[Includable]]): Additional fields to include
+                in the response. See the `include` parameter for Response
+                creation above for more information.
+
+        """
+        return PollingOperation(
+            poll_endpoint=ApiEndpoint(
+                path=f"{RESPONSES_ENDPOINT}/{response_id}",
+                method=HttpMethod.GET,
+                request_model=EmptyRequest,
+                response_model=OpenAIResponse,
+                query_params={"include": include},
+            ),
+            completed_statuses=["completed"],
+            failed_statuses=["failed"],
+            status_extractor=lambda response: response.status,
+            auth_kwargs=auth_kwargs,
+        ).execute()
+
+    def get_message_content_from_response(
+        self, response: OpenAIResponse
+    ) -> list[OutputContent]:
+        """Extract message content from the API response."""
+        for output in response.output:
+            if output.root.type == "message":
+                return output.root.content
+        raise TypeError("No output message found in response")
+
+    def get_text_from_message_content(
+        self, message_content: list[OutputContent]
+    ) -> str:
+        """Extract text content from message content."""
+        for content_item in message_content:
+            if content_item.root.type == "output_text":
+                return str(content_item.root.text)
+        return "No text output found in response"
+
+    def get_history_text(self, session_id: str) -> str:
+        """Convert the entire history for a given session to JSON string."""
+        return json.dumps(self.history[session_id])
+
+    def display_history_on_node(self, session_id: str, node_id: str) -> None:
+        """Display formatted chat history on the node UI."""
+        render_spec = {
+            "node_id": node_id,
+            "component": "ChatHistoryWidget",
+            "props": {
+                "history": self.get_history_text(session_id),
+            },
+        }
+        PromptServer.instance.send_sync(
+            "display_component",
+            render_spec,
+        )
+
+    def add_to_history(
+        self, session_id: str, prompt: str, output_text: str, response_id: str
+    ) -> None:
+        """Add a new entry to the chat history."""
+        if session_id not in self.history:
+            self.history[session_id] = []
+        self.history[session_id].append(
+            {
+                "prompt": prompt,
+                "response": output_text,
+                "response_id": response_id,
+                "timestamp": time.time(),
+            }
+        )
+
+    def parse_output_text_from_response(self, response: OpenAIResponse) -> str:
+        """Extract text output from the API response."""
+        message_contents = self.get_message_content_from_response(response)
+        return self.get_text_from_message_content(message_contents)
+
+    def generate_new_session_id(self) -> str:
+        """Generate a new unique session ID."""
+        return str(uuid.uuid4())
+
+    def get_session_id(self, persist_context: bool) -> str:
+        """Get the current or generate a new session ID based on context persistence."""
+        return (
+            self.current_session_id
+            if persist_context
+            else self.generate_new_session_id()
+        )
+
+    def tensor_to_input_image_content(
+        self, image: torch.Tensor, detail_level: Detail = "auto"
+    ) -> InputImageContent:
+        """Convert a tensor to an input image content object."""
+        return InputImageContent(
+            detail=detail_level,
+            image_url=f"data:image/png;base64,{tensor_to_base64_string(image)}",
+            type="input_image",
+        )
+
+    def create_input_message_contents(
+        self,
+        prompt: str,
+        image: Optional[torch.Tensor] = None,
+        files: Optional[list[InputFileContent]] = None,
+    ) -> InputMessageContentList:
+        """Create a list of input message contents from prompt and optional image."""
+        content_list: list[InputContent] = [
+            InputTextContent(text=prompt, type="input_text"),
+        ]
+        if image is not None:
+            for i in range(image.shape[0]):
+                content_list.append(
+                    self.tensor_to_input_image_content(image[i].unsqueeze(0))
+                )
+        if files is not None:
+            content_list.extend(files)
+
+        return InputMessageContentList(
+            root=content_list,
+        )
+
+    def parse_response_id_from_prompt(self, prompt: str) -> Optional[str]:
+        """Extract response ID from prompt if it exists."""
+        parsed_id = re.search(STARTING_POINT_ID_PATTERN, prompt)
+        return parsed_id.group(1) if parsed_id else None
+
+    def strip_response_tag_from_prompt(self, prompt: str) -> str:
+        """Remove the response ID tag from the prompt."""
+        return re.sub(STARTING_POINT_ID_PATTERN, "", prompt.strip())
+
+    def delete_history_after_response_id(
+        self, new_start_id: str, session_id: str
+    ) -> None:
+        """Delete history entries after a specific response ID."""
+        if session_id not in self.history:
+            return
+
+        new_history = []
+        i = 0
+        while (
+            i < len(self.history[session_id])
+            and self.history[session_id][i]["response_id"] != new_start_id
+        ):
+            new_history.append(self.history[session_id][i])
+            i += 1
+
+        # Since it's the new starting point (not the response being edited), we include it as well
+        if i < len(self.history[session_id]):
+            new_history.append(self.history[session_id][i])
+
+        self.history[session_id] = new_history
+
+    def api_call(
+        self,
+        prompt: str,
+        persist_context: bool,
+        model: SupportedOpenAIModel,
+        unique_id: Optional[str] = None,
+        images: Optional[torch.Tensor] = None,
+        files: Optional[list[InputFileContent]] = None,
+        advanced_options: Optional[CreateModelResponseProperties] = None,
+        **kwargs,
+    ) -> tuple[str]:
+        # Validate inputs
+        validate_string(prompt, strip_whitespace=False)
+
+        session_id = self.get_session_id(persist_context)
+        response_id_override = self.parse_response_id_from_prompt(prompt)
+        if response_id_override:
+            is_starting_from_beginning = response_id_override == "start"
+            if is_starting_from_beginning:
+                self.history[session_id] = []
+                previous_response_id = None
+            else:
+                previous_response_id = response_id_override
+                self.delete_history_after_response_id(response_id_override, session_id)
+            prompt = self.strip_response_tag_from_prompt(prompt)
+        elif persist_context:
+            previous_response_id = self.previous_response_id
+        else:
+            previous_response_id = None
+
+        # Create response
+        create_response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=RESPONSES_ENDPOINT,
+                method=HttpMethod.POST,
+                request_model=OpenAICreateResponse,
+                response_model=OpenAIResponse,
+            ),
+            request=OpenAICreateResponse(
+                input=[
+                    Item(
+                        root=InputMessage(
+                            content=self.create_input_message_contents(
+                                prompt, images, files
+                            ),
+                            role="user",
+                        )
+                    ),
+                ],
+                store=True,
+                stream=False,
+                model=model,
+                previous_response_id=previous_response_id,
+                **(
+                    advanced_options.model_dump(exclude_none=True)
+                    if advanced_options
+                    else {}
+                ),
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        response_id = create_response.id
+
+        # Get result output
+        result_response = self.get_result_response(response_id, auth_kwargs=kwargs)
+        output_text = self.parse_output_text_from_response(result_response)
+
+        # Update history
+        self.add_to_history(session_id, prompt, output_text, response_id)
+        self.display_history_on_node(session_id, unique_id)
+        self.previous_response_id = response_id
+
+        return (output_text,)
+
+
+class OpenAIInputFiles(ComfyNodeABC):
+    """
+    Loads and formats input files for OpenAI API.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        """
+        For details about the supported file input types, see:
+        https://platform.openai.com/docs/guides/pdf-files?api-mode=responses
+        """
+        input_dir = folder_paths.get_input_directory()
+        input_files = [
+            f
+            for f in os.scandir(input_dir)
+            if f.is_file()
+            and (f.name.endswith(".txt") or f.name.endswith(".pdf"))
+            and f.stat().st_size < 32 * 1024 * 1024
+        ]
+        input_files = sorted(input_files, key=lambda x: x.name)
+        input_files = [f.name for f in input_files]
+        return {
+            "required": {
+                "file": (
+                    IO.COMBO,
+                    {
+                        "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.",
+                        "options": input_files,
+                        "default": input_files[0] if input_files else None,
+                    },
+                ),
+            },
+            "optional": {
+                "OPENAI_INPUT_FILES": (
+                    "OPENAI_INPUT_FILES",
+                    {
+                        "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.",
+                        "default": None,
+                    },
+                ),
+            },
+        }
+
+    DESCRIPTION = "Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes."
+    RETURN_TYPES = ("OPENAI_INPUT_FILES",)
+    FUNCTION = "prepare_files"
+    CATEGORY = "api node/text/OpenAI"
+
+    def create_input_file_content(self, file_path: str) -> InputFileContent:
+        return InputFileContent(
+            file_data=text_filepath_to_data_uri(file_path),
+            filename=os.path.basename(file_path),
+            type="input_file",
+        )
+
+    def prepare_files(
+        self, file: str, OPENAI_INPUT_FILES: list[InputFileContent] = []
+    ) -> tuple[list[InputFileContent]]:
+        """
+        Loads and formats input files for OpenAI API.
+        """
+        file_path = folder_paths.get_annotated_filepath(file)
+        input_file_content = self.create_input_file_content(file_path)
+        files = [input_file_content] + OPENAI_INPUT_FILES
+        return (files,)
+
+
+class OpenAIChatConfig(ComfyNodeABC):
+    """Allows setting additional configuration for the OpenAI Chat Node."""
+
+    RETURN_TYPES = ("OPENAI_CHAT_CONFIG",)
+    FUNCTION = "configure"
+    DESCRIPTION = (
+        "Allows specifying advanced configuration options for the OpenAI Chat Nodes."
+    )
+    CATEGORY = "api node/text/OpenAI"
+
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "truncation": (
+                    IO.COMBO,
+                    {
+                        "options": ["auto", "disabled"],
+                        "default": "auto",
+                        "tooltip": "The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error",
+                    },
+                ),
+            },
+            "optional": {
+                "max_output_tokens": model_field_to_node_input(
+                    IO.INT,
+                    OpenAICreateResponse,
+                    "max_output_tokens",
+                    min=16,
+                    default=4096,
+                    max=16384,
+                    tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens",
+                ),
+                "instructions": model_field_to_node_input(
+                    IO.STRING, OpenAICreateResponse, "instructions", multiline=True
+                ),
+            },
+        }
+
+    def configure(
+        self,
+        truncation: bool,
+        instructions: Optional[str] = None,
+        max_output_tokens: Optional[int] = None,
+    ) -> tuple[CreateModelResponseProperties]:
+        """
+        Configure advanced options for the OpenAI Chat Node.
+
+        Note:
+            While `top_p` and `temperature` are listed as properties in the
+            spec, they are not supported for all models (e.g., o4-mini).
+            They are not exposed as inputs at all to avoid having to manually
+            remove depending on model choice.
+        """
+        return (
+            CreateModelResponseProperties(
+                instructions=instructions,
+                truncation=truncation,
+                max_output_tokens=max_output_tokens,
+            ),
+        )
+
+
 NODE_CLASS_MAPPINGS = {
     "OpenAIDalle2": OpenAIDalle2,
     "OpenAIDalle3": OpenAIDalle3,
     "OpenAIGPTImage1": OpenAIGPTImage1,
+    "OpenAIChatNode": OpenAIChatNode,
+    "OpenAIInputFiles": OpenAIInputFiles,
+    "OpenAIChatConfig": OpenAIChatConfig,
 }
 
-# A dictionary that contains the friendly/humanly readable titles for the nodes
 NODE_DISPLAY_NAME_MAPPINGS = {
     "OpenAIDalle2": "OpenAI DALL·E 2",
     "OpenAIDalle3": "OpenAI DALL·E 3",
     "OpenAIGPTImage1": "OpenAI GPT Image 1",
+    "OpenAIChatNode": "OpenAI Chat",
+    "OpenAIInputFiles": "OpenAI Chat Input Files",
+    "OpenAIChatConfig": "OpenAI Chat Advanced Options",
 }

comfy_api_nodes/nodes_pika.py

@@ -6,40 +6,42 @@ Pika API docs: https://pika-827374fb.mintlify.app/api-reference
 from __future__ import annotations
 
 import io
-from typing import Optional, TypeVar
 import logging
-import torch
+from typing import Optional, TypeVar
+
 import numpy as np
+import torch
+
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions
+from comfy_api.input_impl import VideoFromFile
+from comfy_api.input_impl.video_types import VideoCodec, VideoContainer, VideoInput
+from comfy_api_nodes.apinode_utils import (
+    download_url_to_video_output,
+    tensor_to_bytesio,
+)
 from comfy_api_nodes.apis import (
-    PikaBodyGenerate22T2vGenerate22T2vPost,
-    PikaGenerateResponse,
-    PikaBodyGenerate22I2vGenerate22I2vPost,
-    PikaVideoResponse,
-    PikaBodyGenerate22C2vGenerate22PikascenesPost,
     IngredientsMode,
-    PikaDurationEnum,
-    PikaResolutionEnum,
-    PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
-    PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
-    PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
+    PikaBodyGenerate22C2vGenerate22PikascenesPost,
+    PikaBodyGenerate22I2vGenerate22I2vPost,
     PikaBodyGenerate22KeyframeGenerate22PikaframesPost,
+    PikaBodyGenerate22T2vGenerate22T2vPost,
+    PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
+    PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
+    PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
+    PikaDurationEnum,
     Pikaffect,
+    PikaGenerateResponse,
+    PikaResolutionEnum,
+    PikaVideoResponse,
 )
 from comfy_api_nodes.apis.client import (
     ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-    PollingOperation,
     EmptyRequest,
-)
-from comfy_api_nodes.apinode_utils import (
-    tensor_to_bytesio,
-    download_url_to_video_output,
+    HttpMethod,
+    PollingOperation,
+    SynchronousOperation,
 )
 from comfy_api_nodes.mapper_utils import model_field_to_node_input
-from comfy_api.input_impl.video_types import VideoInput, VideoContainer, VideoCodec
-from comfy_api.input_impl import VideoFromFile
-from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions
 
 R = TypeVar("R")
 
@@ -121,7 +123,10 @@ class PikaNodeBase(ComfyNodeABC):
     RETURN_TYPES = ("VIDEO",)
 
     def poll_for_task_status(
-        self, task_id: str, auth_kwargs: Optional[dict[str,str]] = None
+        self,
+        task_id: str,
+        auth_kwargs: Optional[dict[str, str]] = None,
+        node_id: Optional[str] = None,
     ) -> PikaGenerateResponse:
         polling_operation = PollingOperation(
             poll_endpoint=ApiEndpoint(
@@ -141,13 +146,19 @@ class PikaNodeBase(ComfyNodeABC):
                 response.progress if hasattr(response, "progress") else None
             ),
             auth_kwargs=auth_kwargs,
+            result_url_extractor=lambda response: (
+                response.url if hasattr(response, "url") else None
+            ),
+            node_id=node_id,
+            estimated_duration=60
         )
         return polling_operation.execute()
 
     def execute_task(
         self,
         initial_operation: SynchronousOperation[R, PikaGenerateResponse],
-        auth_kwargs: Optional[dict[str,str]] = None,
+        auth_kwargs: Optional[dict[str, str]] = None,
+        node_id: Optional[str] = None,
     ) -> tuple[VideoFromFile]:
         """Executes the initial operation then polls for the task status until it is completed.
 
@@ -195,6 +206,7 @@ class PikaImageToVideoV2_2(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -208,7 +220,8 @@ class PikaImageToVideoV2_2(PikaNodeBase):
         seed: int,
         resolution: str,
         duration: int,
-        **kwargs
+        unique_id: str,
+        **kwargs,
     ) -> tuple[VideoFromFile]:
         # Convert image to BytesIO
         image_bytes_io = tensor_to_bytesio(image)
@@ -238,7 +251,7 @@ class PikaImageToVideoV2_2(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikaTextToVideoNodeV2_2(PikaNodeBase):
@@ -262,6 +275,7 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -275,6 +289,7 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase):
         resolution: str,
         duration: int,
         aspect_ratio: float,
+        unique_id: str,
         **kwargs,
     ) -> tuple[VideoFromFile]:
         initial_operation = SynchronousOperation(
@@ -296,7 +311,7 @@ class PikaTextToVideoNodeV2_2(PikaNodeBase):
             content_type="application/x-www-form-urlencoded",
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikaScenesV2_2(PikaNodeBase):
@@ -340,6 +355,7 @@ class PikaScenesV2_2(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -354,6 +370,7 @@ class PikaScenesV2_2(PikaNodeBase):
         duration: int,
         ingredients_mode: str,
         aspect_ratio: float,
+        unique_id: str,
         image_ingredient_1: Optional[torch.Tensor] = None,
         image_ingredient_2: Optional[torch.Tensor] = None,
         image_ingredient_3: Optional[torch.Tensor] = None,
@@ -403,7 +420,7 @@ class PikaScenesV2_2(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikAdditionsNode(PikaNodeBase):
@@ -439,10 +456,11 @@ class PikAdditionsNode(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
-    DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you’d like to add to create a seamlessly integrated result."
+    DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you'd like to add to create a seamlessly integrated result."
 
     def api_call(
         self,
@@ -451,6 +469,7 @@ class PikAdditionsNode(PikaNodeBase):
         prompt_text: str,
         negative_prompt: str,
         seed: int,
+        unique_id: str,
         **kwargs,
     ) -> tuple[VideoFromFile]:
         # Convert video to BytesIO
@@ -487,7 +506,7 @@ class PikAdditionsNode(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikaSwapsNode(PikaNodeBase):
@@ -532,6 +551,7 @@ class PikaSwapsNode(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -546,6 +566,7 @@ class PikaSwapsNode(PikaNodeBase):
         prompt_text: str,
         negative_prompt: str,
         seed: int,
+        unique_id: str,
         **kwargs,
     ) -> tuple[VideoFromFile]:
         # Convert video to BytesIO
@@ -592,7 +613,7 @@ class PikaSwapsNode(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikaffectsNode(PikaNodeBase):
@@ -637,6 +658,7 @@ class PikaffectsNode(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -649,6 +671,7 @@ class PikaffectsNode(PikaNodeBase):
         prompt_text: str,
         negative_prompt: str,
         seed: int,
+        unique_id: str,
         **kwargs,
     ) -> tuple[VideoFromFile]:
 
@@ -670,7 +693,7 @@ class PikaffectsNode(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 class PikaStartEndFrameNode2_2(PikaNodeBase):
@@ -689,6 +712,7 @@ class PikaStartEndFrameNode2_2(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -703,6 +727,7 @@ class PikaStartEndFrameNode2_2(PikaNodeBase):
         seed: int,
         resolution: str,
         duration: int,
+        unique_id: str,
         **kwargs,
     ) -> tuple[VideoFromFile]:
 
@@ -733,7 +758,7 @@ class PikaStartEndFrameNode2_2(PikaNodeBase):
             auth_kwargs=kwargs,
         )
 
-        return self.execute_task(initial_operation, auth_kwargs=kwargs)
+        return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
 
 
 NODE_CLASS_MAPPINGS = {

comfy_api_nodes/nodes_pixverse.py

@@ -1,5 +1,5 @@
 from inspect import cleandoc
-
+from typing import Optional
 from comfy_api_nodes.apis.pixverse_api import (
     PixverseTextVideoRequest,
     PixverseImageVideoRequest,
@@ -34,11 +34,22 @@ import requests
 from io import BytesIO
 
 
+AVERAGE_DURATION_T2V = 32
+AVERAGE_DURATION_I2V = 30
+AVERAGE_DURATION_T2T = 52
+
+
+def get_video_url_from_response(
+    response: PixverseGenerationStatusResponse,
+) -> Optional[str]:
+    if response.Resp is None or response.Resp.url is None:
+        return None
+    return str(response.Resp.url)
+
+
 def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None):
     # first, upload image to Pixverse and get image id to use in actual generation call
-    files = {
-        "image": tensor_to_bytesio(image)
-    }
+    files = {"image": tensor_to_bytesio(image)}
     operation = SynchronousOperation(
         endpoint=ApiEndpoint(
             path="/proxy/pixverse/image/upload",
@@ -54,7 +65,9 @@ def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None):
     response_upload: PixverseImageUploadResponse = operation.execute()
 
     if response_upload.Resp is None:
-        raise Exception(f"PixVerse image upload request failed: '{response_upload.ErrMsg}'")
+        raise Exception(
+            f"PixVerse image upload request failed: '{response_upload.ErrMsg}'"
+        )
 
     return response_upload.Resp.img_id
 
@@ -73,7 +86,7 @@ class PixverseTemplateNode:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "template": (list(pixverse_templates.keys()), ),
+                "template": (list(pixverse_templates.keys()),),
             }
         }
 
@@ -87,7 +100,7 @@ class PixverseTemplateNode:
 
 class PixverseTextToVideoNode(ComfyNodeABC):
     """
-    Generates videos synchronously based on prompt and output_size.
+    Generates videos based on prompt and output_size.
     """
 
     RETURN_TYPES = (IO.VIDEO,)
@@ -108,9 +121,7 @@ class PixverseTextToVideoNode(ComfyNodeABC):
                         "tooltip": "Prompt for the video generation",
                     },
                 ),
-                "aspect_ratio": (
-                    [ratio.value for ratio in PixverseAspectRatio],
-                ),
+                "aspect_ratio": ([ratio.value for ratio in PixverseAspectRatio],),
                 "quality": (
                     [resolution.value for resolution in PixverseQuality],
                     {
@@ -143,12 +154,13 @@ class PixverseTextToVideoNode(ComfyNodeABC):
                     PixverseIO.TEMPLATE,
                     {
                         "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node."
-                    }
-                )
+                    },
+                ),
             },
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -160,8 +172,9 @@ class PixverseTextToVideoNode(ComfyNodeABC):
         duration_seconds: int,
         motion_mode: str,
         seed,
-        negative_prompt: str=None,
-        pixverse_template: int=None,
+        negative_prompt: str = None,
+        pixverse_template: int = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
@@ -205,19 +218,27 @@ class PixverseTextToVideoNode(ComfyNodeABC):
                 response_model=PixverseGenerationStatusResponse,
             ),
             completed_statuses=[PixverseStatus.successful],
-            failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted],
+            failed_statuses=[
+                PixverseStatus.contents_moderation,
+                PixverseStatus.failed,
+                PixverseStatus.deleted,
+            ],
             status_extractor=lambda x: x.Resp.status,
             auth_kwargs=kwargs,
+            node_id=unique_id,
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_T2V,
         )
         response_poll = operation.execute()
 
         vid_response = requests.get(response_poll.Resp.url)
+
         return (VideoFromFile(BytesIO(vid_response.content)),)
 
 
 class PixverseImageToVideoNode(ComfyNodeABC):
     """
-    Generates videos synchronously based on prompt and output_size.
+    Generates videos based on prompt and output_size.
     """
 
     RETURN_TYPES = (IO.VIDEO,)
@@ -230,9 +251,7 @@ class PixverseImageToVideoNode(ComfyNodeABC):
     def INPUT_TYPES(s):
         return {
             "required": {
-                "image": (
-                    IO.IMAGE,
-                ),
+                "image": (IO.IMAGE,),
                 "prompt": (
                     IO.STRING,
                     {
@@ -273,12 +292,13 @@ class PixverseImageToVideoNode(ComfyNodeABC):
                     PixverseIO.TEMPLATE,
                     {
                         "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node."
-                    }
-                )
+                    },
+                ),
             },
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -290,8 +310,9 @@ class PixverseImageToVideoNode(ComfyNodeABC):
         duration_seconds: int,
         motion_mode: str,
         seed,
-        negative_prompt: str=None,
-        pixverse_template: int=None,
+        negative_prompt: str = None,
+        pixverse_template: int = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
@@ -337,9 +358,16 @@ class PixverseImageToVideoNode(ComfyNodeABC):
                 response_model=PixverseGenerationStatusResponse,
             ),
             completed_statuses=[PixverseStatus.successful],
-            failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted],
+            failed_statuses=[
+                PixverseStatus.contents_moderation,
+                PixverseStatus.failed,
+                PixverseStatus.deleted,
+            ],
             status_extractor=lambda x: x.Resp.status,
             auth_kwargs=kwargs,
+            node_id=unique_id,
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_I2V,
         )
         response_poll = operation.execute()
 
@@ -349,7 +377,7 @@ class PixverseImageToVideoNode(ComfyNodeABC):
 
 class PixverseTransitionVideoNode(ComfyNodeABC):
     """
-    Generates videos synchronously based on prompt and output_size.
+    Generates videos based on prompt and output_size.
     """
 
     RETURN_TYPES = (IO.VIDEO,)
@@ -362,12 +390,8 @@ class PixverseTransitionVideoNode(ComfyNodeABC):
     def INPUT_TYPES(s):
         return {
             "required": {
-                "first_frame": (
-                    IO.IMAGE,
-                ),
-                "last_frame": (
-                    IO.IMAGE,
-                ),
+                "first_frame": (IO.IMAGE,),
+                "last_frame": (IO.IMAGE,),
                 "prompt": (
                     IO.STRING,
                     {
@@ -408,6 +432,7 @@ class PixverseTransitionVideoNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -420,7 +445,8 @@ class PixverseTransitionVideoNode(ComfyNodeABC):
         duration_seconds: int,
         motion_mode: str,
         seed,
-        negative_prompt: str=None,
+        negative_prompt: str = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
@@ -467,9 +493,16 @@ class PixverseTransitionVideoNode(ComfyNodeABC):
                 response_model=PixverseGenerationStatusResponse,
             ),
             completed_statuses=[PixverseStatus.successful],
-            failed_statuses=[PixverseStatus.contents_moderation, PixverseStatus.failed, PixverseStatus.deleted],
+            failed_statuses=[
+                PixverseStatus.contents_moderation,
+                PixverseStatus.failed,
+                PixverseStatus.deleted,
+            ],
             status_extractor=lambda x: x.Resp.status,
             auth_kwargs=kwargs,
+            node_id=unique_id,
+            result_url_extractor=get_video_url_from_response,
+            estimated_duration=AVERAGE_DURATION_T2V,
         )
         response_poll = operation.execute()
 

comfy_api_nodes/nodes_recraft.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 from inspect import cleandoc
+from typing import Optional
 from comfy.utils import ProgressBar
 from comfy_extras.nodes_images import SVG # Added
 from comfy.comfy_types.node_typing import IO
@@ -29,6 +30,8 @@ from comfy_api_nodes.apinode_utils import (
     resize_mask_to_image,
     validate_string,
 )
+from server import PromptServer
+
 import torch
 from io import BytesIO
 from PIL import UnidentifiedImageError
@@ -388,6 +391,7 @@ class RecraftTextToImageNode:
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -400,6 +404,7 @@ class RecraftTextToImageNode:
         recraft_style: RecraftStyle = None,
         negative_prompt: str = None,
         recraft_controls: RecraftControls = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False, max_length=1000)
@@ -436,8 +441,15 @@ class RecraftTextToImageNode:
         )
         response: RecraftImageGenerationResponse = operation.execute()
         images = []
+        urls = []
         for data in response.data:
             with handle_recraft_image_output():
+                if unique_id and data.url:
+                    urls.append(data.url)
+                    urls_string = '\n'.join(urls)
+                    PromptServer.instance.send_progress_text(
+                        f"Result URL: {urls_string}", unique_id
+                    )
                 image = bytesio_to_image_tensor(
                     download_url_to_bytesio(data.url, timeout=1024)
                 )
@@ -763,6 +775,7 @@ class RecraftTextToVectorNode:
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -775,6 +788,7 @@ class RecraftTextToVectorNode:
         seed,
         negative_prompt: str = None,
         recraft_controls: RecraftControls = None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False, max_length=1000)
@@ -809,7 +823,14 @@ class RecraftTextToVectorNode:
         )
         response: RecraftImageGenerationResponse = operation.execute()
         svg_data = []
+        urls = []
         for data in response.data:
+            if unique_id and data.url:
+                urls.append(data.url)
+                # Print result on each iteration in case of error
+                PromptServer.instance.send_progress_text(
+                    f"Result URL: {' '.join(urls)}", unique_id
+                )
             svg_data.append(download_url_to_bytesio(data.url, timeout=1024))
 
         return (SVG(svg_data),)

comfy_api_nodes/nodes_rodin.py

@@ -0,0 +1,462 @@
+"""
+ComfyUI X Rodin3D(Deemos) API Nodes
+
+Rodin API docs: https://developer.hyper3d.ai/
+
+"""
+
+from __future__ import annotations
+from inspect import cleandoc
+from comfy.comfy_types.node_typing import IO
+import folder_paths as comfy_paths
+import requests
+import os
+import datetime
+import shutil
+import time
+import io
+import logging
+import math
+from PIL import Image
+from comfy_api_nodes.apis.rodin_api import (
+    Rodin3DGenerateRequest,
+    Rodin3DGenerateResponse,
+    Rodin3DCheckStatusRequest,
+    Rodin3DCheckStatusResponse,
+    Rodin3DDownloadRequest,
+    Rodin3DDownloadResponse,
+    JobStatus,
+)
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+    PollingOperation,
+)
+
+
+COMMON_PARAMETERS = {
+    "Seed": (
+        IO.INT,
+        {
+            "default":0,
+            "min":0,
+            "max":65535,
+            "display":"number"
+        }
+    ),
+    "Material_Type": (
+        IO.COMBO,
+        {
+            "options": ["PBR", "Shaded"],
+            "default": "PBR"
+        }
+    ),
+    "Polygon_count": (
+        IO.COMBO,
+        {
+            "options": ["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"],
+            "default": "18K-Quad"
+        }
+    )
+}
+
+def create_task_error(response: Rodin3DGenerateResponse):
+    """Check if the response has error"""
+    return hasattr(response, "error")
+
+
+
+class Rodin3DAPI:
+    """
+    Generate 3D Assets using Rodin API
+    """
+    RETURN_TYPES = (IO.STRING,)
+    RETURN_NAMES = ("3D Model Path",)
+    CATEGORY = "api node/3d/Rodin"
+    DESCRIPTION = cleandoc(__doc__ or "")
+    FUNCTION = "api_call"
+    API_NODE = True
+
+    def tensor_to_filelike(self, tensor, max_pixels: int = 2048*2048):
+        """
+        Converts a PyTorch tensor to a file-like object.
+
+        Args:
+        - tensor (torch.Tensor): A tensor representing an image of shape (H, W, C)
+          where C is the number of channels (3 for RGB), H is height, and W is width.
+
+        Returns:
+        - io.BytesIO: A file-like object containing the image data.
+        """
+        array = tensor.cpu().numpy()
+        array = (array * 255).astype('uint8')
+        image = Image.fromarray(array, 'RGB')
+
+        original_width, original_height = image.size
+        original_pixels = original_width * original_height
+        if original_pixels > max_pixels:
+            scale = math.sqrt(max_pixels / original_pixels)
+            new_width = int(original_width * scale)
+            new_height = int(original_height * scale)
+        else:
+            new_width, new_height = original_width, original_height
+
+        if new_width != original_width or new_height != original_height:
+            image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+
+        img_byte_arr = io.BytesIO()
+        image.save(img_byte_arr, format='PNG')  # PNG is used for lossless compression
+        img_byte_arr.seek(0)
+        return img_byte_arr
+
+    def check_rodin_status(self, response: Rodin3DCheckStatusResponse) -> str:
+        has_failed = any(job.status == JobStatus.Failed for job in response.jobs)
+        all_done = all(job.status == JobStatus.Done for job in response.jobs)
+        status_list = [str(job.status) for job in response.jobs]
+        logging.info(f"[ Rodin3D API - CheckStatus ] Generate Status: {status_list}")
+        if has_failed:
+            logging.error(f"[ Rodin3D API - CheckStatus ] Generate Failed: {status_list}, Please try again.")
+            raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.")
+        elif all_done:
+            return "DONE"
+        else:
+            return "Generating"
+
+    def CreateGenerateTask(self, images=None, seed=1, material="PBR", quality="medium", tier="Regular", mesh_mode="Quad", **kwargs):
+        if images == None:
+            raise Exception("Rodin 3D generate requires at least 1 image.")
+        if len(images) >= 5:
+            raise Exception("Rodin 3D generate requires up to 5 image.")
+
+        path = "/proxy/rodin/api/v2/rodin"
+        operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=path,
+                method=HttpMethod.POST,
+                request_model=Rodin3DGenerateRequest,
+                response_model=Rodin3DGenerateResponse,
+            ),
+            request=Rodin3DGenerateRequest(
+                seed=seed,
+                tier=tier,
+                material=material,
+                quality=quality,
+                mesh_mode=mesh_mode
+            ),
+            files=[
+                (
+                    "images",
+                    open(image, "rb") if isinstance(image, str) else self.tensor_to_filelike(image)
+                )
+                for image in images if image is not None
+            ],
+            content_type = "multipart/form-data",
+            auth_kwargs=kwargs,
+        )
+
+        response = operation.execute()
+
+        if create_task_error(response):
+            error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}"
+            logging.error(error_message)
+            raise Exception(error_message)
+
+        logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!")
+        subscription_key = response.jobs.subscription_key
+        task_uuid = response.uuid
+        logging.info(f"[ Rodin3D API - Submit Jobs ] UUID: {task_uuid}")
+        return task_uuid, subscription_key
+
+    def poll_for_task_status(self, subscription_key, **kwargs) -> Rodin3DCheckStatusResponse:
+
+        path = "/proxy/rodin/api/v2/status"
+
+        poll_operation = PollingOperation(
+            poll_endpoint=ApiEndpoint(
+                path = path,
+                method=HttpMethod.POST,
+                request_model=Rodin3DCheckStatusRequest,
+                response_model=Rodin3DCheckStatusResponse,
+            ),
+            request=Rodin3DCheckStatusRequest(
+                subscription_key = subscription_key
+            ),
+            completed_statuses=["DONE"],
+            failed_statuses=["FAILED"],
+            status_extractor=self.check_rodin_status,
+            poll_interval=3.0,
+            auth_kwargs=kwargs,
+        )
+
+        logging.info("[ Rodin3D API - CheckStatus ] Generate Start!")
+
+        return poll_operation.execute()
+
+
+
+    def GetRodinDownloadList(self, uuid, **kwargs) -> Rodin3DDownloadResponse:
+        logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
+
+        path = "/proxy/rodin/api/v2/download"
+        operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=path,
+                method=HttpMethod.POST,
+                request_model=Rodin3DDownloadRequest,
+                response_model=Rodin3DDownloadResponse,
+            ),
+            request=Rodin3DDownloadRequest(
+                task_uuid=uuid
+            ),
+            auth_kwargs=kwargs
+        )
+
+        return operation.execute()
+
+    def GetQualityAndMode(self, PolyCount):
+        if PolyCount == "200K-Triangle":
+            mesh_mode = "Raw"
+            quality = "medium"
+        else:
+            mesh_mode = "Quad"
+            if PolyCount == "4K-Quad":
+                quality = "extra-low"
+            elif PolyCount == "8K-Quad":
+                quality = "low"
+            elif PolyCount == "18K-Quad":
+                quality = "medium"
+            elif PolyCount == "50K-Quad":
+                quality = "high"
+            else:
+                quality = "medium"
+
+        return mesh_mode, quality
+
+    def DownLoadFiles(self, Url_List):
+        Save_path = os.path.join(comfy_paths.get_output_directory(), "Rodin3D", datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
+        os.makedirs(Save_path, exist_ok=True)
+        model_file_path = None
+        for Item in Url_List.list:
+            url = Item.url
+            file_name = Item.name
+            file_path = os.path.join(Save_path, file_name)
+            if file_path.endswith(".glb"):
+                model_file_path = file_path
+            logging.info(f"[ Rodin3D API - download_files ] Downloading file: {file_path}")
+            max_retries = 5
+            for attempt in range(max_retries):
+                try:
+                    with requests.get(url, stream=True) as r:
+                        r.raise_for_status()
+                        with open(file_path, "wb") as f:
+                            shutil.copyfileobj(r.raw, f)
+                    break
+                except Exception as e:
+                    logging.info(f"[ Rodin3D API - download_files ] Error downloading {file_path}:{e}")
+                    if attempt < max_retries - 1:
+                        logging.info("Retrying...")
+                        time.sleep(2)
+                    else:
+                        logging.info(f"[ Rodin3D API - download_files ] Failed to download {file_path} after {max_retries} attempts.")
+
+        return model_file_path
+
+
+class Rodin3D_Regular(Rodin3DAPI):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "Images":
+                (
+                    IO.IMAGE,
+                    {
+                        "forceInput":True,
+                    }
+                )
+            },
+            "optional": {
+                **COMMON_PARAMETERS
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+            },
+        }
+
+    def api_call(
+        self,
+        Images,
+        Seed,
+        Material_Type,
+        Polygon_count,
+        **kwargs
+    ):
+        tier = "Regular"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        mesh_mode, quality = self.GetQualityAndMode(Polygon_count)
+        task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+        self.poll_for_task_status(subscription_key, **kwargs)
+        Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+        model = self.DownLoadFiles(Download_List)
+
+        return (model,)
+
+class Rodin3D_Detail(Rodin3DAPI):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "Images":
+                (
+                    IO.IMAGE,
+                    {
+                        "forceInput":True,
+                    }
+                )
+            },
+            "optional": {
+                **COMMON_PARAMETERS
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+            },
+        }
+
+    def api_call(
+        self,
+        Images,
+        Seed,
+        Material_Type,
+        Polygon_count,
+        **kwargs
+    ):
+        tier = "Detail"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        mesh_mode, quality = self.GetQualityAndMode(Polygon_count)
+        task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+        self.poll_for_task_status(subscription_key, **kwargs)
+        Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+        model = self.DownLoadFiles(Download_List)
+
+        return (model,)
+
+class Rodin3D_Smooth(Rodin3DAPI):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "Images":
+                (
+                    IO.IMAGE,
+                    {
+                        "forceInput":True,
+                    }
+                )
+            },
+            "optional": {
+                **COMMON_PARAMETERS
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+            },
+        }
+
+    def api_call(
+        self,
+        Images,
+        Seed,
+        Material_Type,
+        Polygon_count,
+        **kwargs
+    ):
+        tier = "Smooth"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        mesh_mode, quality = self.GetQualityAndMode(Polygon_count)
+        task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+        self.poll_for_task_status(subscription_key, **kwargs)
+        Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+        model = self.DownLoadFiles(Download_List)
+
+        return (model,)
+
+class Rodin3D_Sketch(Rodin3DAPI):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "Images":
+                (
+                    IO.IMAGE,
+                    {
+                        "forceInput":True,
+                    }
+                )
+            },
+            "optional": {
+                "Seed":
+                (
+                    IO.INT,
+                    {
+                        "default":0,
+                        "min":0,
+                        "max":65535,
+                        "display":"number"
+                    }
+                )
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+            },
+        }
+
+    def api_call(
+        self,
+        Images,
+        Seed,
+        **kwargs
+    ):
+        tier = "Sketch"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        material_type = "PBR"
+        quality = "medium"
+        mesh_mode = "Quad"
+        task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=material_type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+        self.poll_for_task_status(subscription_key, **kwargs)
+        Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+        model = self.DownLoadFiles(Download_List)
+
+        return (model,)
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+    "Rodin3D_Regular": Rodin3D_Regular,
+    "Rodin3D_Detail": Rodin3D_Detail,
+    "Rodin3D_Smooth": Rodin3D_Smooth,
+    "Rodin3D_Sketch": Rodin3D_Sketch,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Rodin3D_Regular": "Rodin 3D Generate - Regular Generate",
+    "Rodin3D_Detail": "Rodin 3D Generate - Detail Generate",
+    "Rodin3D_Smooth": "Rodin 3D Generate - Smooth Generate",
+    "Rodin3D_Sketch": "Rodin 3D Generate - Sketch Generate",
+}

comfy_api_nodes/nodes_runway.py

@@ -0,0 +1,635 @@
+"""Runway API Nodes
+
+API Docs:
+  - https://docs.dev.runwayml.com/api/#tag/Task-management/paths/~1v1~1tasks~1%7Bid%7D/delete
+
+User Guides:
+  - https://help.runwayml.com/hc/en-us/sections/30265301423635-Gen-3-Alpha
+  - https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video
+  - https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo
+  - https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3
+
+"""
+
+from typing import Union, Optional, Any
+from enum import Enum
+
+import torch
+
+from comfy_api_nodes.apis import (
+    RunwayImageToVideoRequest,
+    RunwayImageToVideoResponse,
+    RunwayTaskStatusResponse as TaskStatusResponse,
+    RunwayTaskStatusEnum as TaskStatus,
+    RunwayModelEnum as Model,
+    RunwayDurationEnum as Duration,
+    RunwayAspectRatioEnum as AspectRatio,
+    RunwayPromptImageObject,
+    RunwayPromptImageDetailedObject,
+    RunwayTextToImageRequest,
+    RunwayTextToImageResponse,
+    Model4,
+    ReferenceImage,
+    RunwayTextToImageAspectRatioEnum,
+)
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+    PollingOperation,
+    EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+    upload_images_to_comfyapi,
+    download_url_to_video_output,
+    image_tensor_pair_to_batch,
+    validate_string,
+    download_url_to_image_tensor,
+)
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+from comfy_api.input_impl import VideoFromFile
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC
+
+PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video"
+PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image"
+PATH_GET_TASK_STATUS = "/proxy/runway/tasks"
+
+AVERAGE_DURATION_I2V_SECONDS = 64
+AVERAGE_DURATION_FLF_SECONDS = 256
+AVERAGE_DURATION_T2I_SECONDS = 41
+
+
+class RunwayApiError(Exception):
+    """Base exception for Runway API errors."""
+
+    pass
+
+
+class RunwayGen4TurboAspectRatio(str, Enum):
+    """Aspect ratios supported for Image to Video API when using gen4_turbo model."""
+
+    field_1280_720 = "1280:720"
+    field_720_1280 = "720:1280"
+    field_1104_832 = "1104:832"
+    field_832_1104 = "832:1104"
+    field_960_960 = "960:960"
+    field_1584_672 = "1584:672"
+
+
+class RunwayGen3aAspectRatio(str, Enum):
+    """Aspect ratios supported for Image to Video API when using gen3a_turbo model."""
+
+    field_768_1280 = "768:1280"
+    field_1280_768 = "1280:768"
+
+
+def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]:
+    """Returns the video URL from the task status response if it exists."""
+    if response.output and len(response.output) > 0:
+        return response.output[0]
+    return None
+
+
+# TODO: replace with updated image validation utils (upstream)
+def validate_input_image(image: torch.Tensor) -> bool:
+    """
+    Validate the input image is within the size limits for the Runway API.
+    See: https://docs.dev.runwayml.com/assets/inputs/#common-error-reasons
+    """
+    return image.shape[2] < 8000 and image.shape[1] < 8000
+
+
+def poll_until_finished(
+    auth_kwargs: dict[str, str],
+    api_endpoint: ApiEndpoint[Any, TaskStatusResponse],
+    estimated_duration: Optional[int] = None,
+    node_id: Optional[str] = None,
+) -> TaskStatusResponse:
+    """Polls the Runway API endpoint until the task reaches a terminal state, then returns the response."""
+    return PollingOperation(
+        poll_endpoint=api_endpoint,
+        completed_statuses=[
+            TaskStatus.SUCCEEDED.value,
+        ],
+        failed_statuses=[
+            TaskStatus.FAILED.value,
+            TaskStatus.CANCELLED.value,
+        ],
+        status_extractor=lambda response: (response.status.value),
+        auth_kwargs=auth_kwargs,
+        result_url_extractor=get_video_url_from_task_status,
+        estimated_duration=estimated_duration,
+        node_id=node_id,
+        progress_extractor=extract_progress_from_task_status,
+    ).execute()
+
+
+def extract_progress_from_task_status(
+    response: TaskStatusResponse,
+) -> Union[float, None]:
+    if hasattr(response, "progress") and response.progress is not None:
+        return response.progress * 100
+    return None
+
+
+def get_image_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]:
+    """Returns the image URL from the task status response if it exists."""
+    if response.output and len(response.output) > 0:
+        return response.output[0]
+    return None
+
+
+class RunwayVideoGenNode(ComfyNodeABC):
+    """Runway Video Node Base."""
+
+    RETURN_TYPES = ("VIDEO",)
+    FUNCTION = "api_call"
+    CATEGORY = "api node/video/Runway"
+    API_NODE = True
+
+    def validate_task_created(self, response: RunwayImageToVideoResponse) -> bool:
+        """
+        Validate the task creation response from the Runway API matches
+        expected format.
+        """
+        if not bool(response.id):
+            raise RunwayApiError("Invalid initial response from Runway API.")
+        return True
+
+    def validate_response(self, response: RunwayImageToVideoResponse) -> bool:
+        """
+        Validate the successful task status response from the Runway API
+        matches expected format.
+        """
+        if not response.output or len(response.output) == 0:
+            raise RunwayApiError(
+                "Runway task succeeded but no video data found in response."
+            )
+        return True
+
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> RunwayImageToVideoResponse:
+        """Poll the task status until it is finished then get the response."""
+        return poll_until_finished(
+            auth_kwargs,
+            ApiEndpoint(
+                path=f"{PATH_GET_TASK_STATUS}/{task_id}",
+                method=HttpMethod.GET,
+                request_model=EmptyRequest,
+                response_model=TaskStatusResponse,
+            ),
+            estimated_duration=AVERAGE_DURATION_FLF_SECONDS,
+            node_id=node_id,
+        )
+
+    def generate_video(
+        self,
+        request: RunwayImageToVideoRequest,
+        auth_kwargs: dict[str, str],
+        node_id: Optional[str] = None,
+    ) -> tuple[VideoFromFile]:
+        initial_operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=PATH_IMAGE_TO_VIDEO,
+                method=HttpMethod.POST,
+                request_model=RunwayImageToVideoRequest,
+                response_model=RunwayImageToVideoResponse,
+            ),
+            request=request,
+            auth_kwargs=auth_kwargs,
+        )
+
+        initial_response = initial_operation.execute()
+        self.validate_task_created(initial_response)
+        task_id = initial_response.id
+
+        final_response = self.get_response(task_id, auth_kwargs, node_id)
+        self.validate_response(final_response)
+
+        video_url = get_video_url_from_task_status(final_response)
+        return (download_url_to_video_output(video_url),)
+
+
+class RunwayImageToVideoNodeGen3a(RunwayVideoGenNode):
+    """Runway Image to Video Node using Gen3a Turbo model."""
+
+    DESCRIPTION = "Generate a video from a single starting frame using Gen3a Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo."
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": model_field_to_node_input(
+                    IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True
+                ),
+                "start_frame": (
+                    IO.IMAGE,
+                    {"tooltip": "Start frame to be used for the video"},
+                ),
+                "duration": model_field_to_node_input(
+                    IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration
+                ),
+                "ratio": model_field_to_node_input(
+                    IO.COMBO,
+                    RunwayImageToVideoRequest,
+                    "ratio",
+                    enum_type=RunwayGen3aAspectRatio,
+                ),
+                "seed": model_field_to_node_input(
+                    IO.INT,
+                    RunwayImageToVideoRequest,
+                    "seed",
+                    control_after_generate=True,
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    def api_call(
+        self,
+        prompt: str,
+        start_frame: torch.Tensor,
+        duration: str,
+        ratio: str,
+        seed: int,
+        unique_id: Optional[str] = None,
+        **kwargs,
+    ) -> tuple[VideoFromFile]:
+        # Validate inputs
+        validate_string(prompt, min_length=1)
+        validate_input_image(start_frame)
+
+        # Upload image
+        download_urls = upload_images_to_comfyapi(
+            start_frame,
+            max_images=1,
+            mime_type="image/png",
+            auth_kwargs=kwargs,
+        )
+        if len(download_urls) != 1:
+            raise RunwayApiError("Failed to upload one or more images to comfy api.")
+
+        return self.generate_video(
+            RunwayImageToVideoRequest(
+                promptText=prompt,
+                seed=seed,
+                model=Model("gen3a_turbo"),
+                duration=Duration(duration),
+                ratio=AspectRatio(ratio),
+                promptImage=RunwayPromptImageObject(
+                    root=[
+                        RunwayPromptImageDetailedObject(
+                            uri=str(download_urls[0]), position="first"
+                        )
+                    ]
+                ),
+            ),
+            auth_kwargs=kwargs,
+            node_id=unique_id,
+        )
+
+
+class RunwayImageToVideoNodeGen4(RunwayVideoGenNode):
+    """Runway Image to Video Node using Gen4 Turbo model."""
+
+    DESCRIPTION = "Generate a video from a single starting frame using Gen4 Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video."
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": model_field_to_node_input(
+                    IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True
+                ),
+                "start_frame": (
+                    IO.IMAGE,
+                    {"tooltip": "Start frame to be used for the video"},
+                ),
+                "duration": model_field_to_node_input(
+                    IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration
+                ),
+                "ratio": model_field_to_node_input(
+                    IO.COMBO,
+                    RunwayImageToVideoRequest,
+                    "ratio",
+                    enum_type=RunwayGen4TurboAspectRatio,
+                ),
+                "seed": model_field_to_node_input(
+                    IO.INT,
+                    RunwayImageToVideoRequest,
+                    "seed",
+                    control_after_generate=True,
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    def api_call(
+        self,
+        prompt: str,
+        start_frame: torch.Tensor,
+        duration: str,
+        ratio: str,
+        seed: int,
+        unique_id: Optional[str] = None,
+        **kwargs,
+    ) -> tuple[VideoFromFile]:
+        # Validate inputs
+        validate_string(prompt, min_length=1)
+        validate_input_image(start_frame)
+
+        # Upload image
+        download_urls = upload_images_to_comfyapi(
+            start_frame,
+            max_images=1,
+            mime_type="image/png",
+            auth_kwargs=kwargs,
+        )
+        if len(download_urls) != 1:
+            raise RunwayApiError("Failed to upload one or more images to comfy api.")
+
+        return self.generate_video(
+            RunwayImageToVideoRequest(
+                promptText=prompt,
+                seed=seed,
+                model=Model("gen4_turbo"),
+                duration=Duration(duration),
+                ratio=AspectRatio(ratio),
+                promptImage=RunwayPromptImageObject(
+                    root=[
+                        RunwayPromptImageDetailedObject(
+                            uri=str(download_urls[0]), position="first"
+                        )
+                    ]
+                ),
+            ),
+            auth_kwargs=kwargs,
+            node_id=unique_id,
+        )
+
+
+class RunwayFirstLastFrameNode(RunwayVideoGenNode):
+    """Runway First-Last Frame Node."""
+
+    DESCRIPTION = "Upload first and last keyframes, draft a prompt, and generate a video. More complex transitions, such as cases where the Last frame is completely different from the First frame, may benefit from the longer 10s duration. This would give the generation more time to smoothly transition between the two inputs. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3."
+
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> RunwayImageToVideoResponse:
+        return poll_until_finished(
+            auth_kwargs,
+            ApiEndpoint(
+                path=f"{PATH_GET_TASK_STATUS}/{task_id}",
+                method=HttpMethod.GET,
+                request_model=EmptyRequest,
+                response_model=TaskStatusResponse,
+            ),
+            estimated_duration=AVERAGE_DURATION_FLF_SECONDS,
+            node_id=node_id,
+        )
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": model_field_to_node_input(
+                    IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True
+                ),
+                "start_frame": (
+                    IO.IMAGE,
+                    {"tooltip": "Start frame to be used for the video"},
+                ),
+                "end_frame": (
+                    IO.IMAGE,
+                    {
+                        "tooltip": "End frame to be used for the video. Supported for gen3a_turbo only."
+                    },
+                ),
+                "duration": model_field_to_node_input(
+                    IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration
+                ),
+                "ratio": model_field_to_node_input(
+                    IO.COMBO,
+                    RunwayImageToVideoRequest,
+                    "ratio",
+                    enum_type=RunwayGen3aAspectRatio,
+                ),
+                "seed": model_field_to_node_input(
+                    IO.INT,
+                    RunwayImageToVideoRequest,
+                    "seed",
+                    control_after_generate=True,
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+            },
+        }
+
+    def api_call(
+        self,
+        prompt: str,
+        start_frame: torch.Tensor,
+        end_frame: torch.Tensor,
+        duration: str,
+        ratio: str,
+        seed: int,
+        unique_id: Optional[str] = None,
+        **kwargs,
+    ) -> tuple[VideoFromFile]:
+        # Validate inputs
+        validate_string(prompt, min_length=1)
+        validate_input_image(start_frame)
+        validate_input_image(end_frame)
+
+        # Upload images
+        stacked_input_images = image_tensor_pair_to_batch(start_frame, end_frame)
+        download_urls = upload_images_to_comfyapi(
+            stacked_input_images,
+            max_images=2,
+            mime_type="image/png",
+            auth_kwargs=kwargs,
+        )
+        if len(download_urls) != 2:
+            raise RunwayApiError("Failed to upload one or more images to comfy api.")
+
+        return self.generate_video(
+            RunwayImageToVideoRequest(
+                promptText=prompt,
+                seed=seed,
+                model=Model("gen3a_turbo"),
+                duration=Duration(duration),
+                ratio=AspectRatio(ratio),
+                promptImage=RunwayPromptImageObject(
+                    root=[
+                        RunwayPromptImageDetailedObject(
+                            uri=str(download_urls[0]), position="first"
+                        ),
+                        RunwayPromptImageDetailedObject(
+                            uri=str(download_urls[1]), position="last"
+                        ),
+                    ]
+                ),
+            ),
+            auth_kwargs=kwargs,
+            node_id=unique_id,
+        )
+
+
+class RunwayTextToImageNode(ComfyNodeABC):
+    """Runway Text to Image Node."""
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "api_call"
+    CATEGORY = "api node/image/Runway"
+    API_NODE = True
+    DESCRIPTION = "Generate an image from a text prompt using Runway's Gen 4 model. You can also include reference images to guide the generation."
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": model_field_to_node_input(
+                    IO.STRING, RunwayTextToImageRequest, "promptText", multiline=True
+                ),
+                "ratio": model_field_to_node_input(
+                    IO.COMBO,
+                    RunwayTextToImageRequest,
+                    "ratio",
+                    enum_type=RunwayTextToImageAspectRatioEnum,
+                ),
+            },
+            "optional": {
+                "reference_image": (
+                    IO.IMAGE,
+                    {"tooltip": "Optional reference image to guide the generation"},
+                )
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    def validate_task_created(self, response: RunwayTextToImageResponse) -> bool:
+        """
+        Validate the task creation response from the Runway API matches
+        expected format.
+        """
+        if not bool(response.id):
+            raise RunwayApiError("Invalid initial response from Runway API.")
+        return True
+
+    def validate_response(self, response: TaskStatusResponse) -> bool:
+        """
+        Validate the successful task status response from the Runway API
+        matches expected format.
+        """
+        if not response.output or len(response.output) == 0:
+            raise RunwayApiError(
+                "Runway task succeeded but no image data found in response."
+            )
+        return True
+
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> TaskStatusResponse:
+        """Poll the task status until it is finished then get the response."""
+        return poll_until_finished(
+            auth_kwargs,
+            ApiEndpoint(
+                path=f"{PATH_GET_TASK_STATUS}/{task_id}",
+                method=HttpMethod.GET,
+                request_model=EmptyRequest,
+                response_model=TaskStatusResponse,
+            ),
+            estimated_duration=AVERAGE_DURATION_T2I_SECONDS,
+            node_id=node_id,
+        )
+
+    def api_call(
+        self,
+        prompt: str,
+        ratio: str,
+        reference_image: Optional[torch.Tensor] = None,
+        unique_id: Optional[str] = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor]:
+        # Validate inputs
+        validate_string(prompt, min_length=1)
+
+        # Prepare reference images if provided
+        reference_images = None
+        if reference_image is not None:
+            validate_input_image(reference_image)
+            download_urls = upload_images_to_comfyapi(
+                reference_image,
+                max_images=1,
+                mime_type="image/png",
+                auth_kwargs=kwargs,
+            )
+            if len(download_urls) != 1:
+                raise RunwayApiError("Failed to upload reference image to comfy api.")
+
+            reference_images = [ReferenceImage(uri=str(download_urls[0]))]
+
+        # Create request
+        request = RunwayTextToImageRequest(
+            promptText=prompt,
+            model=Model4.gen4_image,
+            ratio=ratio,
+            referenceImages=reference_images,
+        )
+
+        # Execute initial request
+        initial_operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=PATH_TEXT_TO_IMAGE,
+                method=HttpMethod.POST,
+                request_model=RunwayTextToImageRequest,
+                response_model=RunwayTextToImageResponse,
+            ),
+            request=request,
+            auth_kwargs=kwargs,
+        )
+
+        initial_response = initial_operation.execute()
+        self.validate_task_created(initial_response)
+        task_id = initial_response.id
+
+        # Poll for completion
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
+        self.validate_response(final_response)
+
+        # Download and return image
+        image_url = get_image_url_from_task_status(final_response)
+        return (download_url_to_image_tensor(image_url),)
+
+
+NODE_CLASS_MAPPINGS = {
+    "RunwayFirstLastFrameNode": RunwayFirstLastFrameNode,
+    "RunwayImageToVideoNodeGen3a": RunwayImageToVideoNodeGen3a,
+    "RunwayImageToVideoNodeGen4": RunwayImageToVideoNodeGen4,
+    "RunwayTextToImageNode": RunwayTextToImageNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "RunwayFirstLastFrameNode": "Runway First-Last-Frame to Video",
+    "RunwayImageToVideoNodeGen3a": "Runway Image to Video (Gen3a Turbo)",
+    "RunwayImageToVideoNodeGen4": "Runway Image to Video (Gen4 Turbo)",
+    "RunwayTextToImageNode": "Runway Text to Image",
+}

comfy_api_nodes/nodes_tripo.py

@@ -0,0 +1,574 @@
+import os
+from folder_paths import get_output_directory
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+from comfy.comfy_types.node_typing import IO
+from comfy_api_nodes.apis import (
+    TripoOrientation,
+    TripoModelVersion,
+)
+from comfy_api_nodes.apis.tripo_api import (
+    TripoTaskType,
+    TripoStyle,
+    TripoFileReference,
+    TripoFileEmptyReference,
+    TripoUrlReference,
+    TripoTaskResponse,
+    TripoTaskStatus,
+    TripoTextToModelRequest,
+    TripoImageToModelRequest,
+    TripoMultiviewToModelRequest,
+    TripoTextureModelRequest,
+    TripoRefineModelRequest,
+    TripoAnimateRigRequest,
+    TripoAnimateRetargetRequest,
+    TripoConvertModelRequest,
+)
+
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+    PollingOperation,
+    EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+    upload_images_to_comfyapi,
+    download_url_to_bytesio,
+)
+
+
+def upload_image_to_tripo(image, **kwargs):
+    urls = upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs)
+    return TripoFileReference(TripoUrlReference(url=urls[0], type="jpeg"))
+
+def get_model_url_from_response(response: TripoTaskResponse) -> str:
+    if response.data is not None:
+        for key in ["pbr_model", "model", "base_model"]:
+            if getattr(response.data.output, key, None) is not None:
+                return getattr(response.data.output, key)
+    raise RuntimeError(f"Failed to get model url from response: {response}")
+
+
+def poll_until_finished(
+    kwargs: dict[str, str],
+    response: TripoTaskResponse,
+) -> tuple[str, str]:
+    """Polls the Tripo API endpoint until the task reaches a terminal state, then returns the response."""
+    if response.code != 0:
+        raise RuntimeError(f"Failed to generate mesh: {response.error}")
+    task_id = response.data.task_id
+    response_poll = PollingOperation(
+        poll_endpoint=ApiEndpoint(
+            path=f"/proxy/tripo/v2/openapi/task/{task_id}",
+            method=HttpMethod.GET,
+            request_model=EmptyRequest,
+            response_model=TripoTaskResponse,
+        ),
+        completed_statuses=[TripoTaskStatus.SUCCESS],
+        failed_statuses=[
+            TripoTaskStatus.FAILED,
+            TripoTaskStatus.CANCELLED,
+            TripoTaskStatus.UNKNOWN,
+            TripoTaskStatus.BANNED,
+            TripoTaskStatus.EXPIRED,
+        ],
+        status_extractor=lambda x: x.data.status,
+        auth_kwargs=kwargs,
+        node_id=kwargs["unique_id"],
+        result_url_extractor=get_model_url_from_response,
+        progress_extractor=lambda x: x.data.progress,
+    ).execute()
+    if response_poll.data.status == TripoTaskStatus.SUCCESS:
+        url = get_model_url_from_response(response_poll)
+        bytesio = download_url_to_bytesio(url)
+        # Save the downloaded model file
+        model_file = f"tripo_model_{task_id}.glb"
+        with open(os.path.join(get_output_directory(), model_file), "wb") as f:
+            f.write(bytesio.getvalue())
+        return model_file, task_id
+    raise RuntimeError(f"Failed to generate mesh: {response_poll}")
+
+class TripoTextToModelNode:
+    """
+    Generates 3D models synchronously based on a text prompt using Tripo's API.
+    """
+    AVERAGE_DURATION = 80
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": ("STRING", {"multiline": True}),
+            },
+            "optional": {
+                "negative_prompt": ("STRING", {"multiline": True}),
+                "model_version": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "model_version", enum_type=TripoModelVersion),
+                "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"),
+                "texture": ("BOOLEAN", {"default": True}),
+                "pbr": ("BOOLEAN", {"default": True}),
+                "image_seed": ("INT", {"default": 42}),
+                "model_seed": ("INT", {"default": 42}),
+                "texture_seed": ("INT", {"default": 42}),
+                "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+                "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+                "quad": ("BOOLEAN", {"default": False})
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+    RETURN_NAMES = ("model_file", "model task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+
+    def generate_mesh(self, prompt, negative_prompt=None, model_version=None, style=None, texture=None, pbr=None, image_seed=None, model_seed=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs):
+        style_enum = None if style == "None" else style
+        if not prompt:
+            raise RuntimeError("Prompt is required")
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoTextToModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoTextToModelRequest(
+                type=TripoTaskType.TEXT_TO_MODEL,
+                prompt=prompt,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                model_version=model_version,
+                style=style_enum,
+                texture=texture,
+                pbr=pbr,
+                image_seed=image_seed,
+                model_seed=model_seed,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                face_limit=face_limit,
+                auto_size=True,
+                quad=quad
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+class TripoImageToModelNode:
+    """
+    Generates 3D models synchronously based on a single image using Tripo's API.
+    """
+    AVERAGE_DURATION = 80
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+            "optional": {
+                "model_version": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "model_version", enum_type=TripoModelVersion),
+                "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"),
+                "texture": ("BOOLEAN", {"default": True}),
+                "pbr": ("BOOLEAN", {"default": True}),
+                "model_seed": ("INT", {"default": 42}),
+                "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation),
+                "texture_seed": ("INT", {"default": 42}),
+                "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+                "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}),
+                "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+                "quad": ("BOOLEAN", {"default": False})
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+    RETURN_NAMES = ("model_file", "model task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+
+    def generate_mesh(self, image, model_version=None, style=None, texture=None, pbr=None, model_seed=None, orientation=None, texture_alignment=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs):
+        style_enum = None if style == "None" else style
+        if image is None:
+            raise RuntimeError("Image is required")
+        tripo_file = upload_image_to_tripo(image, **kwargs)
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoImageToModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoImageToModelRequest(
+                type=TripoTaskType.IMAGE_TO_MODEL,
+                file=tripo_file,
+                model_version=model_version,
+                style=style_enum,
+                texture=texture,
+                pbr=pbr,
+                model_seed=model_seed,
+                orientation=orientation,
+                texture_alignment=texture_alignment,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                face_limit=face_limit,
+                auto_size=True,
+                quad=quad
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+class TripoMultiviewToModelNode:
+    """
+    Generates 3D models synchronously based on up to four images (front, left, back, right) using Tripo's API.
+    """
+    AVERAGE_DURATION = 80
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+            "optional": {
+                "image_left": ("IMAGE",),
+                "image_back": ("IMAGE",),
+                "image_right": ("IMAGE",),
+                "model_version": model_field_to_node_input(IO.COMBO, TripoMultiviewToModelRequest, "model_version", enum_type=TripoModelVersion),
+                "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation),
+                "texture": ("BOOLEAN", {"default": True}),
+                "pbr": ("BOOLEAN", {"default": True}),
+                "model_seed": ("INT", {"default": 42}),
+                "texture_seed": ("INT", {"default": 42}),
+                "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+                "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}),
+                "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+                "quad": ("BOOLEAN", {"default": False})
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+    RETURN_NAMES = ("model_file", "model task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+
+    def generate_mesh(self, image, image_left=None, image_back=None, image_right=None, model_version=None, orientation=None, texture=None, pbr=None, model_seed=None, texture_seed=None, texture_quality=None, texture_alignment=None, face_limit=None, quad=None, **kwargs):
+        if image is None:
+            raise RuntimeError("front image for multiview is required")
+        images = []
+        image_dict = {
+            "image": image,
+            "image_left": image_left,
+            "image_back": image_back,
+            "image_right": image_right
+        }
+        if image_left is None and image_back is None and image_right is None:
+            raise RuntimeError("At least one of left, back, or right image must be provided for multiview")
+        for image_name in ["image", "image_left", "image_back", "image_right"]:
+            image_ = image_dict[image_name]
+            if image_ is not None:
+                tripo_file = upload_image_to_tripo(image_, **kwargs)
+                images.append(tripo_file)
+            else:
+                images.append(TripoFileEmptyReference())
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoMultiviewToModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoMultiviewToModelRequest(
+                type=TripoTaskType.MULTIVIEW_TO_MODEL,
+                files=images,
+                model_version=model_version,
+                orientation=orientation,
+                texture=texture,
+                pbr=pbr,
+                model_seed=model_seed,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                texture_alignment=texture_alignment,
+                face_limit=face_limit,
+                quad=quad,
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+class TripoTextureNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model_task_id": ("MODEL_TASK_ID",),
+            },
+            "optional": {
+                "texture": ("BOOLEAN", {"default": True}),
+                "pbr": ("BOOLEAN", {"default": True}),
+                "texture_seed": ("INT", {"default": 42}),
+                "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+                "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+    RETURN_NAMES = ("model_file", "model task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+    AVERAGE_DURATION = 80
+
+    def generate_mesh(self, model_task_id, texture=None, pbr=None, texture_seed=None, texture_quality=None, texture_alignment=None, **kwargs):
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoTextureModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoTextureModelRequest(
+                original_model_task_id=model_task_id,
+                texture=texture,
+                pbr=pbr,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                texture_alignment=texture_alignment
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+
+class TripoRefineNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model_task_id": ("MODEL_TASK_ID", {
+                    "tooltip": "Must be a v1.4 Tripo model"
+                }),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    DESCRIPTION = "Refine a draft model created by v1.4 Tripo models only."
+
+    RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+    RETURN_NAMES = ("model_file", "model task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+    AVERAGE_DURATION = 240
+
+    def generate_mesh(self, model_task_id, **kwargs):
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoRefineModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoRefineModelRequest(
+                draft_model_task_id=model_task_id
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+
+class TripoRigNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "original_model_task_id": ("MODEL_TASK_ID",),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "RIG_TASK_ID")
+    RETURN_NAMES = ("model_file", "rig task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+    AVERAGE_DURATION = 180
+
+    def generate_mesh(self, original_model_task_id, **kwargs):
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoAnimateRigRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoAnimateRigRequest(
+                original_model_task_id=original_model_task_id,
+                out_format="glb",
+                spec="tripo"
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+class TripoRetargetNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "original_model_task_id": ("RIG_TASK_ID",),
+                "animation": ([
+                    "preset:idle",
+                    "preset:walk",
+                    "preset:climb",
+                    "preset:jump",
+                    "preset:slash",
+                    "preset:shoot",
+                    "preset:hurt",
+                    "preset:fall",
+                    "preset:turn",
+                ],),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "RETARGET_TASK_ID")
+    RETURN_NAMES = ("model_file", "retarget task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+    AVERAGE_DURATION = 30
+
+    def generate_mesh(self, animation, original_model_task_id, **kwargs):
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoAnimateRetargetRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoAnimateRetargetRequest(
+                original_model_task_id=original_model_task_id,
+                animation=animation,
+                out_format="glb",
+                bake_animation=True
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+class TripoConversionNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "original_model_task_id": ("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID",),
+                "format": (["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"],),
+            },
+            "optional": {
+                "quad": ("BOOLEAN", {"default": False}),
+                "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+                "texture_size": ("INT", {"min": 128, "max": 4096, "default": 4096}),
+                "texture_format": (["BMP", "DPX", "HDR", "JPEG", "OPEN_EXR", "PNG", "TARGA", "TIFF", "WEBP"], {"default": "JPEG"})
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, input_types):
+        # The min and max of input1 and input2 are still validated because
+        # we didn't take `input1` or `input2` as arguments
+        if input_types["original_model_task_id"] not in ("MODEL_TASK_ID", "RIG_TASK_ID", "RETARGET_TASK_ID"):
+            return "original_model_task_id must be MODEL_TASK_ID, RIG_TASK_ID or RETARGET_TASK_ID type"
+        return True
+
+    RETURN_TYPES = ()
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+    AVERAGE_DURATION = 30
+
+    def generate_mesh(self, original_model_task_id, format, quad, face_limit, texture_size, texture_format, **kwargs):
+        if not original_model_task_id:
+            raise RuntimeError("original_model_task_id is required")
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoConvertModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoConvertModelRequest(
+                original_model_task_id=original_model_task_id,
+                format=format,
+                quad=quad if quad else None,
+                face_limit=face_limit if face_limit != -1 else None,
+                texture_size=texture_size if texture_size != 4096 else None,
+                texture_format=texture_format if texture_format != "JPEG" else None
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+NODE_CLASS_MAPPINGS = {
+    "TripoTextToModelNode": TripoTextToModelNode,
+    "TripoImageToModelNode": TripoImageToModelNode,
+    "TripoMultiviewToModelNode": TripoMultiviewToModelNode,
+    "TripoTextureNode": TripoTextureNode,
+    "TripoRefineNode": TripoRefineNode,
+    "TripoRigNode": TripoRigNode,
+    "TripoRetargetNode": TripoRetargetNode,
+    "TripoConversionNode": TripoConversionNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "TripoTextToModelNode": "Tripo: Text to Model",
+    "TripoImageToModelNode": "Tripo: Image to Model",
+    "TripoMultiviewToModelNode": "Tripo: Multiview to Model",
+    "TripoTextureNode": "Tripo: Texture model",
+    "TripoRefineNode": "Tripo: Refine Draft model",
+    "TripoRigNode": "Tripo: Rig model",
+    "TripoRetargetNode": "Tripo: Retarget rigged model",
+    "TripoConversionNode": "Tripo: Convert model",
+}

comfy_api_nodes/nodes_veo2.py

@@ -3,6 +3,7 @@ import logging
 import base64
 import requests
 import torch
+from typing import Optional
 
 from comfy.comfy_types.node_typing import IO, ComfyNodeABC
 from comfy_api.input_impl.video_types import VideoFromFile
@@ -24,6 +25,8 @@ from comfy_api_nodes.apinode_utils import (
     tensor_to_base64_string
 )
 
+AVERAGE_DURATION_VIDEO_GEN = 32
+
 def convert_image_to_base64(image: torch.Tensor):
     if image is None:
         return None
@@ -31,6 +34,22 @@ def convert_image_to_base64(image: torch.Tensor):
     scaled_image = downscale_image_tensor(image, total_pixels=2048*2048)
     return tensor_to_base64_string(scaled_image)
 
+
+def get_video_url_from_response(poll_response: Veo2GenVidPollResponse) -> Optional[str]:
+    if (
+        poll_response.response
+        and hasattr(poll_response.response, "videos")
+        and poll_response.response.videos
+        and len(poll_response.response.videos) > 0
+    ):
+        video = poll_response.response.videos[0]
+    else:
+        return None
+    if hasattr(video, "gcsUri") and video.gcsUri:
+        return str(video.gcsUri)
+    return None
+
+
 class VeoVideoGenerationNode(ComfyNodeABC):
     """
     Generates videos from text prompts using Google's Veo API.
@@ -115,6 +134,7 @@ class VeoVideoGenerationNode(ComfyNodeABC):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -134,6 +154,7 @@ class VeoVideoGenerationNode(ComfyNodeABC):
         person_generation="ALLOW",
         seed=0,
         image=None,
+        unique_id: Optional[str] = None,
         **kwargs,
     ):
         # Prepare the instances for the request
@@ -215,7 +236,10 @@ class VeoVideoGenerationNode(ComfyNodeABC):
                 operationName=operation_name
             ),
             auth_kwargs=kwargs,
-            poll_interval=5.0
+            poll_interval=5.0,
+            result_url_extractor=get_video_url_from_response,
+            node_id=unique_id,
+            estimated_duration=AVERAGE_DURATION_VIDEO_GEN,
         )
 
         # Execute the polling operation

comfy_api_nodes/util/validation_utils.py

@@ -0,0 +1,100 @@
+import logging
+from typing import Optional
+
+import torch
+from comfy_api.input.video_types import VideoInput
+
+
+def get_image_dimensions(image: torch.Tensor) -> tuple[int, int]:
+    if len(image.shape) == 4:
+        return image.shape[1], image.shape[2]
+    elif len(image.shape) == 3:
+        return image.shape[0], image.shape[1]
+    else:
+        raise ValueError("Invalid image tensor shape.")
+
+
+def validate_image_dimensions(
+    image: torch.Tensor,
+    min_width: Optional[int] = None,
+    max_width: Optional[int] = None,
+    min_height: Optional[int] = None,
+    max_height: Optional[int] = None,
+):
+    height, width = get_image_dimensions(image)
+
+    if min_width is not None and width < min_width:
+        raise ValueError(f"Image width must be at least {min_width}px, got {width}px")
+    if max_width is not None and width > max_width:
+        raise ValueError(f"Image width must be at most {max_width}px, got {width}px")
+    if min_height is not None and height < min_height:
+        raise ValueError(
+            f"Image height must be at least {min_height}px, got {height}px"
+        )
+    if max_height is not None and height > max_height:
+        raise ValueError(f"Image height must be at most {max_height}px, got {height}px")
+
+
+def validate_image_aspect_ratio(
+    image: torch.Tensor,
+    min_aspect_ratio: Optional[float] = None,
+    max_aspect_ratio: Optional[float] = None,
+):
+    width, height = get_image_dimensions(image)
+    aspect_ratio = width / height
+
+    if min_aspect_ratio is not None and aspect_ratio < min_aspect_ratio:
+        raise ValueError(
+            f"Image aspect ratio must be at least {min_aspect_ratio}, got {aspect_ratio}"
+        )
+    if max_aspect_ratio is not None and aspect_ratio > max_aspect_ratio:
+        raise ValueError(
+            f"Image aspect ratio must be at most {max_aspect_ratio}, got {aspect_ratio}"
+        )
+
+
+def validate_video_dimensions(
+    video: VideoInput,
+    min_width: Optional[int] = None,
+    max_width: Optional[int] = None,
+    min_height: Optional[int] = None,
+    max_height: Optional[int] = None,
+):
+    try:
+        width, height = video.get_dimensions()
+    except Exception as e:
+        logging.error("Error getting dimensions of video: %s", e)
+        return
+
+    if min_width is not None and width < min_width:
+        raise ValueError(f"Video width must be at least {min_width}px, got {width}px")
+    if max_width is not None and width > max_width:
+        raise ValueError(f"Video width must be at most {max_width}px, got {width}px")
+    if min_height is not None and height < min_height:
+        raise ValueError(
+            f"Video height must be at least {min_height}px, got {height}px"
+        )
+    if max_height is not None and height > max_height:
+        raise ValueError(f"Video height must be at most {max_height}px, got {height}px")
+
+
+def validate_video_duration(
+    video: VideoInput,
+    min_duration: Optional[float] = None,
+    max_duration: Optional[float] = None,
+):
+    try:
+        duration = video.get_duration()
+    except Exception as e:
+        logging.error("Error getting duration of video: %s", e)
+        return
+
+    epsilon = 0.0001
+    if min_duration is not None and min_duration - epsilon > duration:
+        raise ValueError(
+            f"Video duration must be at least {min_duration}s, got {duration}s"
+        )
+    if max_duration is not None and duration > max_duration + epsilon:
+        raise ValueError(
+            f"Video duration must be at most {max_duration}s, got {duration}s"
+        )

comfy_extras/nodes_apg.py

@@ -0,0 +1,76 @@
+import torch
+
+def project(v0, v1):
+    v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
+    v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
+    v0_orthogonal = v0 - v0_parallel
+    return v0_parallel, v0_orthogonal
+
+class APG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "eta": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "tooltip": "Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1."}),
+                "norm_threshold": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Normalize guidance vector to this value, normalization disable at a setting of 0."}),
+                "momentum": ("FLOAT", {"default": 0.0, "min": -5.0, "max": 1.0, "step": 0.01, "tooltip":"Controls a running average of guidance during diffusion, disabled at a setting of 0."}),
+            }
+        }
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "sampling/custom_sampling"
+
+    def patch(self, model, eta, norm_threshold, momentum):
+        running_avg = 0
+        prev_sigma = None
+
+        def pre_cfg_function(args):
+            nonlocal running_avg, prev_sigma
+
+            if len(args["conds_out"]) == 1: return args["conds_out"]
+
+            cond = args["conds_out"][0]
+            uncond = args["conds_out"][1]
+            sigma = args["sigma"][0]
+            cond_scale = args["cond_scale"]
+
+            if prev_sigma is not None and sigma > prev_sigma:
+                running_avg = 0
+            prev_sigma = sigma
+
+            guidance = cond - uncond
+
+            if momentum != 0:
+                if not torch.is_tensor(running_avg):
+                    running_avg = guidance
+                else:
+                    running_avg = momentum * running_avg + guidance
+                guidance = running_avg
+
+            if norm_threshold > 0:
+                guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True)
+                scale = torch.minimum(
+                    torch.ones_like(guidance_norm),
+                    norm_threshold / guidance_norm
+                )
+                guidance = guidance * scale
+
+            guidance_parallel, guidance_orthogonal = project(guidance, cond)
+            modified_guidance = guidance_orthogonal + eta * guidance_parallel
+
+            modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale
+
+            return [modified_cond, uncond] + args["conds_out"][2:]
+
+        m = model.clone()
+        m.set_model_sampler_pre_cfg_function(pre_cfg_function)
+        return (m,)
+
+NODE_CLASS_MAPPINGS = {
+    "APG": APG,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "APG": "Adaptive Projected Guidance",
+}

comfy_extras/nodes_audio.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import av
 import torchaudio
 import torch
 import comfy.model_management
@@ -7,7 +8,6 @@ import folder_paths
 import os
 import io
 import json
-import struct
 import random
 import hashlib
 import node_helpers
@@ -90,60 +90,118 @@ class VAEDecodeAudio:
         return ({"waveform": audio, "sample_rate": 44100}, )
 
 
-def create_vorbis_comment_block(comment_dict, last_block):
-    vendor_string = b'ComfyUI'
-    vendor_length = len(vendor_string)
+def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None, quality="128k"):
 
-    comments = []
-    for key, value in comment_dict.items():
-        comment = f"{key}={value}".encode('utf-8')
-        comments.append(struct.pack('<I', len(comment)) + comment)
+    filename_prefix += self.prefix_append
+    full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+    results: list[FileLocator] = []
 
-    user_comment_list_length = len(comments)
-    user_comments = b''.join(comments)
+    # Prepare metadata dictionary
+    metadata = {}
+    if not args.disable_metadata:
+        if prompt is not None:
+            metadata["prompt"] = json.dumps(prompt)
+        if extra_pnginfo is not None:
+            for x in extra_pnginfo:
+                metadata[x] = json.dumps(extra_pnginfo[x])
 
-    comment_data = struct.pack('<I', vendor_length) + vendor_string + struct.pack('<I', user_comment_list_length) + user_comments
-    if last_block:
-        id = b'\x84'
-    else:
-        id = b'\x04'
-    comment_block = id + struct.pack('>I', len(comment_data))[1:] + comment_data
+    # Opus supported sample rates
+    OPUS_RATES = [8000, 12000, 16000, 24000, 48000]
 
-    return comment_block
+    for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
+        filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+        file = f"{filename_with_batch_num}_{counter:05}_.{format}"
+        output_path = os.path.join(full_output_folder, file)
 
-def insert_or_replace_vorbis_comment(flac_io, comment_dict):
-    if len(comment_dict) == 0:
-        return flac_io
+        # Use original sample rate initially
+        sample_rate = audio["sample_rate"]
 
-    flac_io.seek(4)
+        # Handle Opus sample rate requirements
+        if format == "opus":
+            if sample_rate > 48000:
+                sample_rate = 48000
+            elif sample_rate not in OPUS_RATES:
+                # Find the next highest supported rate
+                for rate in sorted(OPUS_RATES):
+                    if rate > sample_rate:
+                        sample_rate = rate
+                        break
+                if sample_rate not in OPUS_RATES:  # Fallback if still not supported
+                    sample_rate = 48000
 
-    blocks = []
-    last_block = False
+            # Resample if necessary
+            if sample_rate != audio["sample_rate"]:
+                waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate)
 
-    while not last_block:
-        header = flac_io.read(4)
-        last_block = (header[0] & 0x80) != 0
-        block_type = header[0] & 0x7F
-        block_length = struct.unpack('>I', b'\x00' + header[1:])[0]
-        block_data = flac_io.read(block_length)
+        # Create in-memory WAV buffer
+        wav_buffer = io.BytesIO()
+        torchaudio.save(wav_buffer, waveform, sample_rate, format="WAV")
+        wav_buffer.seek(0)  # Rewind for reading
 
-        if block_type == 4 or block_type == 1:
-            pass
-        else:
-            header = bytes([(header[0] & (~0x80))]) + header[1:]
-            blocks.append(header + block_data)
+        # Use PyAV to convert and add metadata
+        input_container = av.open(wav_buffer)
 
-    blocks.append(create_vorbis_comment_block(comment_dict, last_block=True))
+        # Create output with specified format
+        output_buffer = io.BytesIO()
+        output_container = av.open(output_buffer, mode='w', format=format)
 
-    new_flac_io = io.BytesIO()
-    new_flac_io.write(b'fLaC')
-    for block in blocks:
-        new_flac_io.write(block)
+        # Set metadata on the container
+        for key, value in metadata.items():
+            output_container.metadata[key] = value
 
-    new_flac_io.write(flac_io.read())
-    return new_flac_io
+        # Set up the output stream with appropriate properties
+        input_container.streams.audio[0]
+        if format == "opus":
+            out_stream = output_container.add_stream("libopus", rate=sample_rate)
+            if quality == "64k":
+                out_stream.bit_rate = 64000
+            elif quality == "96k":
+                out_stream.bit_rate = 96000
+            elif quality == "128k":
+                out_stream.bit_rate = 128000
+            elif quality == "192k":
+                out_stream.bit_rate = 192000
+            elif quality == "320k":
+                out_stream.bit_rate = 320000
+        elif format == "mp3":
+            out_stream = output_container.add_stream("libmp3lame", rate=sample_rate)
+            if quality == "V0":
+                #TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
+                out_stream.codec_context.qscale = 1
+            elif quality == "128k":
+                out_stream.bit_rate = 128000
+            elif quality == "320k":
+                out_stream.bit_rate = 320000
+        else: #format == "flac":
+            out_stream = output_container.add_stream("flac", rate=sample_rate)
 
 
+        # Copy frames from input to output
+        for frame in input_container.decode(audio=0):
+            frame.pts = None  # Let PyAV handle timestamps
+            output_container.mux(out_stream.encode(frame))
+
+        # Flush encoder
+        output_container.mux(out_stream.encode(None))
+
+        # Close containers
+        output_container.close()
+        input_container.close()
+
+        # Write the output to file
+        output_buffer.seek(0)
+        with open(output_path, 'wb') as f:
+            f.write(output_buffer.getbuffer())
+
+        results.append({
+            "filename": file,
+            "subfolder": subfolder,
+            "type": self.type
+        })
+        counter += 1
+
+    return { "ui": { "audio": results } }
+
 class SaveAudio:
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
@@ -153,50 +211,70 @@ class SaveAudio:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "audio": ("AUDIO", ),
-                              "filename_prefix": ("STRING", {"default": "audio/ComfyUI"})},
+                            "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
+                            },
                 "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                 }
 
     RETURN_TYPES = ()
-    FUNCTION = "save_audio"
+    FUNCTION = "save_flac"
 
     OUTPUT_NODE = True
 
     CATEGORY = "audio"
 
-    def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
-        filename_prefix += self.prefix_append
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
-        results: list[FileLocator] = []
+    def save_flac(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None):
+        return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo)
 
-        metadata = {}
-        if not args.disable_metadata:
-            if prompt is not None:
-                metadata["prompt"] = json.dumps(prompt)
-            if extra_pnginfo is not None:
-                for x in extra_pnginfo:
-                    metadata[x] = json.dumps(extra_pnginfo[x])
+class SaveAudioMP3:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
 
-        for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
-            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
-            file = f"{filename_with_batch_num}_{counter:05}_.flac"
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "audio": ("AUDIO", ),
+                            "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
+                            "quality": (["V0", "128k", "320k"], {"default": "V0"}),
+                            },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
 
-            buff = io.BytesIO()
-            torchaudio.save(buff, waveform, audio["sample_rate"], format="FLAC")
+    RETURN_TYPES = ()
+    FUNCTION = "save_mp3"
 
-            buff = insert_or_replace_vorbis_comment(buff, metadata)
+    OUTPUT_NODE = True
 
-            with open(os.path.join(full_output_folder, file), 'wb') as f:
-                f.write(buff.getbuffer())
+    CATEGORY = "audio"
 
-            results.append({
-                "filename": file,
-                "subfolder": subfolder,
-                "type": self.type
-            })
-            counter += 1
+    def save_mp3(self, audio, filename_prefix="ComfyUI", format="mp3", prompt=None, extra_pnginfo=None, quality="128k"):
+        return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality)
 
-        return { "ui": { "audio": results } }
+class SaveAudioOpus:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "audio": ("AUDIO", ),
+                            "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
+                            "quality": (["64k", "96k", "128k", "192k", "320k"], {"default": "128k"}),
+                            },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_opus"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "audio"
+
+    def save_opus(self, audio, filename_prefix="ComfyUI", format="opus", prompt=None, extra_pnginfo=None, quality="V3"):
+        return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality)
 
 class PreviewAudio(SaveAudio):
     def __init__(self):
@@ -248,7 +326,20 @@ NODE_CLASS_MAPPINGS = {
     "VAEEncodeAudio": VAEEncodeAudio,
     "VAEDecodeAudio": VAEDecodeAudio,
     "SaveAudio": SaveAudio,
+    "SaveAudioMP3": SaveAudioMP3,
+    "SaveAudioOpus": SaveAudioOpus,
     "LoadAudio": LoadAudio,
     "PreviewAudio": PreviewAudio,
     "ConditioningStableAudio": ConditioningStableAudio,
 }
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "EmptyLatentAudio": "Empty Latent Audio",
+    "VAEEncodeAudio": "VAE Encode Audio",
+    "VAEDecodeAudio": "VAE Decode Audio",
+    "PreviewAudio": "Preview Audio",
+    "LoadAudio": "Load Audio",
+    "SaveAudio": "Save Audio (FLAC)",
+    "SaveAudioMP3": "Save Audio (MP3)",
+    "SaveAudioOpus": "Save Audio (Opus)",
+}

comfy_extras/nodes_camera_trajectory.py

@@ -0,0 +1,218 @@
+import nodes
+import torch
+import numpy as np
+from einops import rearrange
+import comfy.model_management
+
+
+
+MAX_RESOLUTION = nodes.MAX_RESOLUTION
+
+CAMERA_DICT = {
+    "base_T_norm": 1.5,
+    "base_angle": np.pi/3,
+    "Static": {     "angle":[0., 0., 0.],   "T":[0., 0., 0.]},
+    "Pan Up": {     "angle":[0., 0., 0.],   "T":[0., -1., 0.]},
+    "Pan Down": {   "angle":[0., 0., 0.],   "T":[0.,1.,0.]},
+    "Pan Left": {   "angle":[0., 0., 0.],   "T":[-1.,0.,0.]},
+    "Pan Right": {  "angle":[0., 0., 0.],   "T": [1.,0.,0.]},
+    "Zoom In": {    "angle":[0., 0., 0.],   "T": [0.,0.,2.]},
+    "Zoom Out": {   "angle":[0., 0., 0.],   "T": [0.,0.,-2.]},
+    "Anti Clockwise (ACW)": {        "angle": [0., 0., -1.],  "T":[0., 0., 0.]},
+    "ClockWise (CW)": {         "angle": [0., 0., 1.], "T":[0., 0., 0.]},
+}
+
+
+def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'):
+
+    def get_relative_pose(cam_params):
+        """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+        """
+        abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
+        abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
+        cam_to_origin = 0
+        target_cam_c2w = np.array([
+            [1, 0, 0, 0],
+            [0, 1, 0, -cam_to_origin],
+            [0, 0, 1, 0],
+            [0, 0, 0, 1]
+        ])
+        abs2rel = target_cam_c2w @ abs_w2cs[0]
+        ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
+        ret_poses = np.array(ret_poses, dtype=np.float32)
+        return ret_poses
+
+    """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    cam_params = [Camera(cam_param) for cam_param in cam_params]
+
+    sample_wh_ratio = width / height
+    pose_wh_ratio = original_pose_width / original_pose_height  # Assuming placeholder ratios, change as needed
+
+    if pose_wh_ratio > sample_wh_ratio:
+        resized_ori_w = height * pose_wh_ratio
+        for cam_param in cam_params:
+            cam_param.fx = resized_ori_w * cam_param.fx / width
+    else:
+        resized_ori_h = width / pose_wh_ratio
+        for cam_param in cam_params:
+            cam_param.fy = resized_ori_h * cam_param.fy / height
+
+    intrinsic = np.asarray([[cam_param.fx * width,
+                            cam_param.fy * height,
+                            cam_param.cx * width,
+                            cam_param.cy * height]
+                            for cam_param in cam_params], dtype=np.float32)
+
+    K = torch.as_tensor(intrinsic)[None]  # [1, 1, 4]
+    c2ws = get_relative_pose(cam_params)  # Assuming this function is defined elsewhere
+    c2ws = torch.as_tensor(c2ws)[None]  # [1, n_frame, 4, 4]
+    plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous()  # V, 6, H, W
+    plucker_embedding = plucker_embedding[None]
+    plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
+    return plucker_embedding
+
+class Camera(object):
+    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    def __init__(self, entry):
+        fx, fy, cx, cy = entry[1:5]
+        self.fx = fx
+        self.fy = fy
+        self.cx = cx
+        self.cy = cy
+        c2w_mat = np.array(entry[7:]).reshape(4, 4)
+        self.c2w_mat = c2w_mat
+        self.w2c_mat = np.linalg.inv(c2w_mat)
+
+def ray_condition(K, c2w, H, W, device):
+    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    # c2w: B, V, 4, 4
+    # K: B, V, 4
+
+    B = K.shape[0]
+
+    j, i = torch.meshgrid(
+        torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+        torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
+        indexing='ij'
+    )
+    i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]
+    j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]
+
+    fx, fy, cx, cy = K.chunk(4, dim=-1)  # B,V, 1
+
+    zs = torch.ones_like(i)  # [B, HxW]
+    xs = (i - cx) / fx * zs
+    ys = (j - cy) / fy * zs
+    zs = zs.expand_as(ys)
+
+    directions = torch.stack((xs, ys, zs), dim=-1)  # B, V, HW, 3
+    directions = directions / directions.norm(dim=-1, keepdim=True)  # B, V, HW, 3
+
+    rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2)  # B, V, 3, HW
+    rays_o = c2w[..., :3, 3]  # B, V, 3
+    rays_o = rays_o[:, :, None].expand_as(rays_d)  # B, V, 3, HW
+    # c2w @ dirctions
+    rays_dxo = torch.cross(rays_o, rays_d)
+    plucker = torch.cat([rays_dxo, rays_d], dim=-1)
+    plucker = plucker.reshape(B, c2w.shape[1], H, W, 6)  # B, V, H, W, 6
+    # plucker = plucker.permute(0, 1, 4, 2, 3)
+    return plucker
+
+def get_camera_motion(angle, T, speed, n=81):
+    def compute_R_form_rad_angle(angles):
+        theta_x, theta_y, theta_z = angles
+        Rx = np.array([[1, 0, 0],
+                    [0, np.cos(theta_x), -np.sin(theta_x)],
+                    [0, np.sin(theta_x), np.cos(theta_x)]])
+
+        Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)],
+                    [0, 1, 0],
+                    [-np.sin(theta_y), 0, np.cos(theta_y)]])
+
+        Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0],
+                    [np.sin(theta_z), np.cos(theta_z), 0],
+                    [0, 0, 1]])
+
+        R = np.dot(Rz, np.dot(Ry, Rx))
+        return R
+    RT = []
+    for i in range(n):
+        _angle = (i/n)*speed*(CAMERA_DICT["base_angle"])*angle
+        R = compute_R_form_rad_angle(_angle)
+        _T=(i/n)*speed*(CAMERA_DICT["base_T_norm"])*(T.reshape(3,1))
+        _RT = np.concatenate([R,_T], axis=1)
+        RT.append(_RT)
+    RT = np.stack(RT)
+    return RT
+
+class WanCameraEmbedding:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "camera_pose":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","Anti Clockwise (ACW)", "ClockWise (CW)"],{"default":"Static"}),
+                "width": ("INT", {"default": 832, "min": 16, "max": MAX_RESOLUTION, "step": 16}),
+                "height": ("INT", {"default": 480, "min": 16, "max": MAX_RESOLUTION, "step": 16}),
+                "length": ("INT", {"default": 81, "min": 1, "max": MAX_RESOLUTION, "step": 4}),
+            },
+            "optional":{
+                "speed":("FLOAT",{"default":1.0, "min": 0, "max": 10.0, "step": 0.1}),
+                "fx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}),
+                "fy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}),
+                "cx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}),
+                "cy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}),
+            }
+
+        }
+
+    RETURN_TYPES = ("WAN_CAMERA_EMBEDDING","INT","INT","INT")
+    RETURN_NAMES = ("camera_embedding","width","height","length")
+    FUNCTION = "run"
+    CATEGORY = "camera"
+
+    def run(self, camera_pose, width, height, length, speed=1.0,  fx=0.5, fy=0.5, cx=0.5, cy=0.5):
+        """
+        Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021)
+        Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py
+        """
+        motion_list = [camera_pose]
+        speed = speed
+        angle = np.array(CAMERA_DICT[motion_list[0]]["angle"])
+        T = np.array(CAMERA_DICT[motion_list[0]]["T"])
+        RT = get_camera_motion(angle, T, speed, length)
+
+        trajs=[]
+        for cp in RT.tolist():
+            traj=[fx,fy,cx,cy,0,0]
+            traj.extend(cp[0])
+            traj.extend(cp[1])
+            traj.extend(cp[2])
+            traj.extend([0,0,0,1])
+            trajs.append(traj)
+
+        cam_params = np.array([[float(x) for x in pose] for pose in trajs])
+        cam_params = np.concatenate([np.zeros_like(cam_params[:, :1]), cam_params], 1)
+        control_camera_video = process_pose_params(cam_params, width=width, height=height)
+        control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0).to(device=comfy.model_management.intermediate_device())
+
+        control_camera_video = torch.concat(
+            [
+                torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2),
+                control_camera_video[:, :, 1:]
+            ], dim=2
+        ).transpose(1, 2)
+
+        # Reshape, transpose, and view into desired shape
+        b, f, c, h, w = control_camera_video.shape
+        control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
+        control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
+
+        return (control_camera_video, width, height, length)
+
+
+NODE_CLASS_MAPPINGS = {
+    "WanCameraEmbedding": WanCameraEmbedding,
+}

comfy_extras/nodes_cond.py

@@ -31,6 +31,7 @@ class T5TokenizerOptions:
             }
         }
 
+    CATEGORY = "_for_testing/conditioning"
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "set_options"
 

comfy_extras/nodes_hunyuan.py

@@ -77,7 +77,7 @@ class HunyuanImageToVideo:
                              "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                              "length": ("INT", {"default": 53, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                             "guidance_type": (["v1 (concat)", "v2 (replace)"], )
+                             "guidance_type": (["v1 (concat)", "v2 (replace)", "custom"], )
                 },
                 "optional": {"start_image": ("IMAGE", ),
                 }}
@@ -101,10 +101,12 @@ class HunyuanImageToVideo:
 
             if guidance_type == "v1 (concat)":
                 cond = {"concat_latent_image": concat_latent_image, "concat_mask": mask}
-            else:
+            elif guidance_type == "v2 (replace)":
                 cond = {'guiding_frame_index': 0}
                 latent[:, :, :concat_latent_image.shape[2]] = concat_latent_image
                 out_latent["noise_mask"] = mask
+            elif guidance_type == "custom":
+                cond = {"ref_latent": concat_latent_image}
 
             positive = node_helpers.conditioning_set_values(positive, cond)
 

comfy_extras/nodes_images.py

@@ -13,6 +13,8 @@ import os
 import re
 from io import BytesIO
 from inspect import cleandoc
+import torch
+import comfy.utils
 
 from comfy.comfy_types import FileLocator
 
@@ -74,6 +76,24 @@ class ImageFromBatch:
         s = s_in[batch_index:batch_index + length].clone()
         return (s,)
 
+
+class ImageAddNoise:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": ("IMAGE",),
+                              "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}),
+                              "strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "repeat"
+
+    CATEGORY = "image"
+
+    def repeat(self, image, seed, strength):
+        generator = torch.manual_seed(seed)
+        s = torch.clip((image + strength * torch.randn(image.size(), generator=generator, device="cpu").to(image)), min=0.0, max=1.0)
+        return (s,)
+
 class SaveAnimatedWEBP:
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
@@ -210,6 +230,186 @@ class SVG:
             all_svgs_list.extend(svg_item.data)
         return SVG(all_svgs_list)
 
+
+class ImageStitch:
+    """Upstreamed from https://github.com/kijai/ComfyUI-KJNodes"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image1": ("IMAGE",),
+                "direction": (["right", "down", "left", "up"], {"default": "right"}),
+                "match_image_size": ("BOOLEAN", {"default": True}),
+                "spacing_width": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 1024, "step": 2},
+                ),
+                "spacing_color": (
+                    ["white", "black", "red", "green", "blue"],
+                    {"default": "white"},
+                ),
+            },
+            "optional": {
+                "image2": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "stitch"
+    CATEGORY = "image/transform"
+    DESCRIPTION = """
+Stitches image2 to image1 in the specified direction.
+If image2 is not provided, returns image1 unchanged.
+Optional spacing can be added between images.
+"""
+
+    def stitch(
+        self,
+        image1,
+        direction,
+        match_image_size,
+        spacing_width,
+        spacing_color,
+        image2=None,
+    ):
+        if image2 is None:
+            return (image1,)
+
+        # Handle batch size differences
+        if image1.shape[0] != image2.shape[0]:
+            max_batch = max(image1.shape[0], image2.shape[0])
+            if image1.shape[0] < max_batch:
+                image1 = torch.cat(
+                    [image1, image1[-1:].repeat(max_batch - image1.shape[0], 1, 1, 1)]
+                )
+            if image2.shape[0] < max_batch:
+                image2 = torch.cat(
+                    [image2, image2[-1:].repeat(max_batch - image2.shape[0], 1, 1, 1)]
+                )
+
+        # Match image sizes if requested
+        if match_image_size:
+            h1, w1 = image1.shape[1:3]
+            h2, w2 = image2.shape[1:3]
+            aspect_ratio = w2 / h2
+
+            if direction in ["left", "right"]:
+                target_h, target_w = h1, int(h1 * aspect_ratio)
+            else:  # up, down
+                target_w, target_h = w1, int(w1 / aspect_ratio)
+
+            image2 = comfy.utils.common_upscale(
+                image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled"
+            ).movedim(1, -1)
+
+        # When not matching sizes, pad to align non-concat dimensions
+        if not match_image_size:
+            h1, w1 = image1.shape[1:3]
+            h2, w2 = image2.shape[1:3]
+
+            if direction in ["left", "right"]:
+                # For horizontal concat, pad heights to match
+                if h1 != h2:
+                    target_h = max(h1, h2)
+                    if h1 < target_h:
+                        pad_h = target_h - h1
+                        pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
+                        image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0)
+                    if h2 < target_h:
+                        pad_h = target_h - h2
+                        pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
+                        image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0)
+            else:  # up, down
+                # For vertical concat, pad widths to match
+                if w1 != w2:
+                    target_w = max(w1, w2)
+                    if w1 < target_w:
+                        pad_w = target_w - w1
+                        pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
+                        image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=0.0)
+                    if w2 < target_w:
+                        pad_w = target_w - w2
+                        pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
+                        image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=0.0)
+
+        # Ensure same number of channels
+        if image1.shape[-1] != image2.shape[-1]:
+            max_channels = max(image1.shape[-1], image2.shape[-1])
+            if image1.shape[-1] < max_channels:
+                image1 = torch.cat(
+                    [
+                        image1,
+                        torch.ones(
+                            *image1.shape[:-1],
+                            max_channels - image1.shape[-1],
+                            device=image1.device,
+                        ),
+                    ],
+                    dim=-1,
+                )
+            if image2.shape[-1] < max_channels:
+                image2 = torch.cat(
+                    [
+                        image2,
+                        torch.ones(
+                            *image2.shape[:-1],
+                            max_channels - image2.shape[-1],
+                            device=image2.device,
+                        ),
+                    ],
+                    dim=-1,
+                )
+
+        # Add spacing if specified
+        if spacing_width > 0:
+            spacing_width = spacing_width + (spacing_width % 2)  # Ensure even
+
+            color_map = {
+                "white": 1.0,
+                "black": 0.0,
+                "red": (1.0, 0.0, 0.0),
+                "green": (0.0, 1.0, 0.0),
+                "blue": (0.0, 0.0, 1.0),
+            }
+            color_val = color_map[spacing_color]
+
+            if direction in ["left", "right"]:
+                spacing_shape = (
+                    image1.shape[0],
+                    max(image1.shape[1], image2.shape[1]),
+                    spacing_width,
+                    image1.shape[-1],
+                )
+            else:
+                spacing_shape = (
+                    image1.shape[0],
+                    spacing_width,
+                    max(image1.shape[2], image2.shape[2]),
+                    image1.shape[-1],
+                )
+
+            spacing = torch.full(spacing_shape, 0.0, device=image1.device)
+            if isinstance(color_val, tuple):
+                for i, c in enumerate(color_val):
+                    if i < spacing.shape[-1]:
+                        spacing[..., i] = c
+                if spacing.shape[-1] == 4:  # Add alpha
+                    spacing[..., 3] = 1.0
+            else:
+                spacing[..., : min(3, spacing.shape[-1])] = color_val
+                if spacing.shape[-1] == 4:
+                    spacing[..., 3] = 1.0
+
+        # Concatenate images
+        images = [image2, image1] if direction in ["left", "up"] else [image1, image2]
+        if spacing_width > 0:
+            images.insert(1, spacing)
+
+        concat_dim = 2 if direction in ["left", "right"] else 1
+        return (torch.cat(images, dim=concat_dim),)
+
+
 class SaveSVGNode:
     """
     Save SVG files on disk.
@@ -295,7 +495,9 @@ NODE_CLASS_MAPPINGS = {
     "ImageCrop": ImageCrop,
     "RepeatImageBatch": RepeatImageBatch,
     "ImageFromBatch": ImageFromBatch,
+    "ImageAddNoise": ImageAddNoise,
     "SaveAnimatedWEBP": SaveAnimatedWEBP,
     "SaveAnimatedPNG": SaveAnimatedPNG,
     "SaveSVGNode": SaveSVGNode,
+    "ImageStitch": ImageStitch,
 }

comfy_extras/nodes_load_3d.py

@@ -2,6 +2,10 @@ import nodes
 import folder_paths
 import os
 
+from comfy.comfy_types import IO
+from comfy_api.input_impl import VideoFromFile
+
+
 def normalize_path(path):
     return path.replace('\\', '/')
 
@@ -12,7 +16,7 @@ class Load3D():
 
         os.makedirs(input_dir, exist_ok=True)
 
-        files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.obj', '.mtl', '.fbx', '.stl'))]
+        files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.obj', '.fbx', '.stl'))]
 
         return {"required": {
             "model_file": (sorted(files), {"file_upload": True}),
@@ -21,8 +25,8 @@ class Load3D():
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
         }}
 
-    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA")
-    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info")
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO)
+    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info", "recording_video")
 
     FUNCTION = "process"
     EXPERIMENTAL = True
@@ -41,7 +45,14 @@ class Load3D():
         normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
         lineart_image, ignore_mask3 = load_image_node.load_image(image=lineart_path)
 
-        return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info']
+        video = None
+
+        if image['recording'] != "":
+            recording_video_path = folder_paths.get_annotated_filepath(image['recording'])
+
+            video = VideoFromFile(recording_video_path)
+
+        return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info'], video
 
 class Load3DAnimation():
     @classmethod
@@ -59,8 +70,8 @@ class Load3DAnimation():
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
         }}
 
-    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA")
-    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info")
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO)
+    RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info", "recording_video")
 
     FUNCTION = "process"
     EXPERIMENTAL = True
@@ -77,7 +88,14 @@ class Load3DAnimation():
         ignore_image, output_mask = load_image_node.load_image(image=mask_path)
         normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
 
-        return output_image, output_mask, model_file, normal_image, image['camera_info']
+        video = None
+
+        if image['recording'] != "":
+            recording_video_path = folder_paths.get_annotated_filepath(image['recording'])
+
+            video = VideoFromFile(recording_video_path)
+
+        return output_image, output_mask, model_file, normal_image, image['camera_info'], video
 
 class Preview3D():
     @classmethod

comfy_extras/nodes_string.py

@@ -0,0 +1,360 @@
+import re
+
+from comfy.comfy_types.node_typing import IO
+
+class StringConcatenate():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string_a": (IO.STRING, {"multiline": True}),
+                "string_b": (IO.STRING, {"multiline": True}),
+                "delimiter": (IO.STRING, {"multiline": False, "default": ""})
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string_a, string_b, delimiter, **kwargs):
+        return delimiter.join((string_a, string_b)),
+
+class StringSubstring():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "start": (IO.INT, {}),
+                "end": (IO.INT, {}),
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, start, end, **kwargs):
+        return string[start:end],
+
+class StringLength():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES = (IO.INT,)
+    RETURN_NAMES = ("length",)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, **kwargs):
+        length = len(string)
+
+        return length,
+
+class CaseConverter():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "mode": (IO.COMBO, {"options": ["UPPERCASE", "lowercase", "Capitalize", "Title Case"]})
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, mode, **kwargs):
+        if mode == "UPPERCASE":
+            result = string.upper()
+        elif mode == "lowercase":
+            result = string.lower()
+        elif mode == "Capitalize":
+            result = string.capitalize()
+        elif mode == "Title Case":
+            result = string.title()
+        else:
+            result = string
+
+        return result,
+
+
+class StringTrim():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "mode": (IO.COMBO, {"options": ["Both", "Left", "Right"]})
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, mode, **kwargs):
+        if mode == "Both":
+            result = string.strip()
+        elif mode == "Left":
+            result = string.lstrip()
+        elif mode == "Right":
+            result = string.rstrip()
+        else:
+            result = string
+
+        return result,
+
+class StringReplace():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "find": (IO.STRING, {"multiline": True}),
+                "replace": (IO.STRING, {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, find, replace, **kwargs):
+        result = string.replace(find, replace)
+        return result,
+
+
+class StringContains():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "substring": (IO.STRING, {"multiline": True}),
+                "case_sensitive": (IO.BOOLEAN, {"default": True})
+            }
+        }
+
+    RETURN_TYPES = (IO.BOOLEAN,)
+    RETURN_NAMES = ("contains",)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, substring, case_sensitive, **kwargs):
+        if case_sensitive:
+            contains = substring in string
+        else:
+            contains = substring.lower() in string.lower()
+
+        return contains,
+
+
+class StringCompare():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string_a": (IO.STRING, {"multiline": True}),
+                "string_b": (IO.STRING, {"multiline": True}),
+                "mode": (IO.COMBO, {"options": ["Starts With", "Ends With", "Equal"]}),
+                "case_sensitive": (IO.BOOLEAN, {"default": True})
+            }
+        }
+
+    RETURN_TYPES = (IO.BOOLEAN,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string_a, string_b, mode, case_sensitive, **kwargs):
+        if case_sensitive:
+            a = string_a
+            b = string_b
+        else:
+            a = string_a.lower()
+            b = string_b.lower()
+
+        if mode == "Equal":
+            return a == b,
+        elif mode == "Starts With":
+            return a.startswith(b),
+        elif mode == "Ends With":
+            return a.endswith(b),
+
+class RegexMatch():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "regex_pattern": (IO.STRING, {"multiline": True}),
+                "case_insensitive": (IO.BOOLEAN, {"default": True}),
+                "multiline": (IO.BOOLEAN, {"default": False}),
+                "dotall": (IO.BOOLEAN, {"default": False})
+            }
+        }
+
+    RETURN_TYPES = (IO.BOOLEAN,)
+    RETURN_NAMES = ("matches",)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, regex_pattern, case_insensitive, multiline, dotall, **kwargs):
+        flags = 0
+
+        if case_insensitive:
+            flags |= re.IGNORECASE
+        if multiline:
+            flags |= re.MULTILINE
+        if dotall:
+            flags |= re.DOTALL
+
+        try:
+            match = re.search(regex_pattern, string, flags)
+            result = match is not None
+
+        except re.error:
+            result = False
+
+        return result,
+
+
+class RegexExtract():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "regex_pattern": (IO.STRING, {"multiline": True}),
+                "mode": (IO.COMBO, {"options": ["First Match", "All Matches", "First Group", "All Groups"]}),
+                "case_insensitive": (IO.BOOLEAN, {"default": True}),
+                "multiline": (IO.BOOLEAN, {"default": False}),
+                "dotall": (IO.BOOLEAN, {"default": False}),
+                "group_index": (IO.INT, {"default": 1, "min": 0, "max": 100})
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, regex_pattern, mode, case_insensitive, multiline, dotall, group_index, **kwargs):
+        join_delimiter = "\n"
+
+        flags = 0
+        if case_insensitive:
+            flags |= re.IGNORECASE
+        if multiline:
+            flags |= re.MULTILINE
+        if dotall:
+            flags |= re.DOTALL
+
+        try:
+            if mode == "First Match":
+                match = re.search(regex_pattern, string, flags)
+                if match:
+                    result = match.group(0)
+                else:
+                    result = ""
+
+            elif mode == "All Matches":
+                matches = re.findall(regex_pattern, string, flags)
+                if matches:
+                    if isinstance(matches[0], tuple):
+                        result = join_delimiter.join([m[0] for m in matches])
+                    else:
+                        result = join_delimiter.join(matches)
+                else:
+                    result = ""
+
+            elif mode == "First Group":
+                match = re.search(regex_pattern, string, flags)
+                if match and len(match.groups()) >= group_index:
+                    result = match.group(group_index)
+                else:
+                    result = ""
+
+            elif mode == "All Groups":
+                matches = re.finditer(regex_pattern, string, flags)
+                results = []
+                for match in matches:
+                    if match.groups() and len(match.groups()) >= group_index:
+                        results.append(match.group(group_index))
+                result = join_delimiter.join(results)
+            else:
+                result = ""
+
+        except re.error:
+            result = ""
+
+        return result,
+
+
+class RegexReplace():
+    DESCRIPTION = "Find and replace text using regex patterns."
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "regex_pattern": (IO.STRING, {"multiline": True}),
+                "replace": (IO.STRING, {"multiline": True}),
+            },
+            "optional": {
+                "case_insensitive": (IO.BOOLEAN, {"default": True}),
+                "multiline": (IO.BOOLEAN, {"default": False}),
+                "dotall": (IO.BOOLEAN, {"default": False, "tooltip": "When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."}),
+                "count": (IO.INT, {"default": 0, "min": 0, "max": 100, "tooltip": "Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."}),
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, regex_pattern, replace, case_insensitive=True, multiline=False, dotall=False, count=0, **kwargs):
+        flags = 0
+
+        if case_insensitive:
+            flags |= re.IGNORECASE
+        if multiline:
+            flags |= re.MULTILINE
+        if dotall:
+            flags |= re.DOTALL
+        result = re.sub(regex_pattern, replace, string, count=count, flags=flags)
+        return result,
+
+NODE_CLASS_MAPPINGS = {
+    "StringConcatenate": StringConcatenate,
+    "StringSubstring": StringSubstring,
+    "StringLength": StringLength,
+    "CaseConverter": CaseConverter,
+    "StringTrim": StringTrim,
+    "StringReplace": StringReplace,
+    "StringContains": StringContains,
+    "StringCompare": StringCompare,
+    "RegexMatch": RegexMatch,
+    "RegexExtract": RegexExtract,
+    "RegexReplace": RegexReplace,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "StringConcatenate": "Concatenate",
+    "StringSubstring": "Substring",
+    "StringLength": "Length",
+    "CaseConverter": "Case Converter",
+    "StringTrim": "Trim",
+    "StringReplace": "Replace",
+    "StringContains": "Contains",
+    "StringCompare": "Compare",
+    "RegexMatch": "Regex Match",
+    "RegexExtract": "Regex Extract",
+    "RegexReplace": "Regex Replace",
+}

comfy_extras/nodes_torch_compile.py

@@ -1,4 +1,5 @@
-import torch
+from comfy_api.torch_helpers import set_torch_compile_wrapper
+
 
 class TorchCompileModel:
     @classmethod
@@ -14,7 +15,7 @@ class TorchCompileModel:
 
     def patch(self, model, backend):
         m = model.clone()
-        m.add_object_patch("diffusion_model", torch.compile(model=m.get_model_object("diffusion_model"), backend=backend))
+        set_torch_compile_wrapper(model=m, backend=backend)
         return (m, )
 
 NODE_CLASS_MAPPINGS = {

comfy_extras/nodes_wan.py

@@ -268,8 +268,9 @@ class WanVaceToVideo:
             trim_latent = reference_image.shape[2]
 
         mask = mask.unsqueeze(0)
-        positive = node_helpers.conditioning_set_values(positive, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength})
-        negative = node_helpers.conditioning_set_values(negative, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength})
+
+        positive = node_helpers.conditioning_set_values(positive, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
+        negative = node_helpers.conditioning_set_values(negative, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
 
         latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device())
         out_latent = {}
@@ -297,6 +298,90 @@ class TrimVideoLatent:
         samples_out["samples"] = s1[:, :, trim_amount:]
         return (samples_out,)
 
+class WanCameraImageToVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "vae": ("VAE", ),
+                             "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                },
+                "optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
+                             "start_image": ("IMAGE", ),
+                             "camera_conditions": ("WAN_CAMERA_EMBEDDING", ),
+                }}
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/video_models"
+
+    def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, camera_conditions=None):
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent)
+
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            concat_latent_image = vae.encode(start_image[:, :, :, :3])
+            concat_latent[:,:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]]
+
+            positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent})
+            negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent})
+
+        if camera_conditions is not None:
+            positive = node_helpers.conditioning_set_values(positive, {'camera_conditions': camera_conditions})
+            negative = node_helpers.conditioning_set_values(negative, {'camera_conditions': camera_conditions})
+
+        if clip_vision_output is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
+            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return (positive, negative, out_latent)
+
+class WanPhantomSubjectToVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "vae": ("VAE", ),
+                             "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                },
+                "optional": {"images": ("IMAGE", ),
+                }}
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative_text", "negative_img_text", "latent")
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/video_models"
+
+    def encode(self, positive, negative, vae, width, height, length, batch_size, images):
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        cond2 = negative
+        if images is not None:
+            images = comfy.utils.common_upscale(images[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            latent_images = []
+            for i in images:
+                latent_images += [vae.encode(i.unsqueeze(0)[:, :, :, :3])]
+            concat_latent_image = torch.cat(latent_images, dim=2)
+
+            positive = node_helpers.conditioning_set_values(positive, {"time_dim_concat": concat_latent_image})
+            cond2 = node_helpers.conditioning_set_values(negative, {"time_dim_concat": concat_latent_image})
+            negative = node_helpers.conditioning_set_values(negative, {"time_dim_concat": comfy.latent_formats.Wan21().process_out(torch.zeros_like(concat_latent_image))})
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return (positive, cond2, negative, out_latent)
 
 NODE_CLASS_MAPPINGS = {
     "WanImageToVideo": WanImageToVideo,
@@ -305,4 +390,6 @@ NODE_CLASS_MAPPINGS = {
     "WanFirstLastFrameToVideo": WanFirstLastFrameToVideo,
     "WanVaceToVideo": WanVaceToVideo,
     "TrimVideoLatent": TrimVideoLatent,
+    "WanCameraImageToVideo": WanCameraImageToVideo,
+    "WanPhantomSubjectToVideo": WanPhantomSubjectToVideo,
 }

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.34"
+__version__ = "0.3.39"

execution.py

@@ -909,7 +909,6 @@ class PromptQueue:
         self.currently_running = {}
         self.history = {}
         self.flags = {}
-        server.prompt_queue = self
 
     def put(self, item):
         with self.mutex:
@@ -954,6 +953,7 @@ class PromptQueue:
             self.history[prompt[1]].update(history_result)
             self.server.queue_updated()
 
+    # Note: slow
     def get_current_queue(self):
         with self.mutex:
             out = []
@@ -961,6 +961,13 @@ class PromptQueue:
                 out += [x]
             return (out, copy.deepcopy(self.queue))
 
+    # read-safe as long as queue items are immutable
+    def get_current_queue_volatile(self):
+        with self.mutex:
+            running = [x for x in self.currently_running.values()]
+            queued = copy.copy(self.queue)
+            return (running, queued)
+
     def get_tasks_remaining(self):
         with self.mutex:
             return len(self.queue) + len(self.currently_running)

fix_torch.py

@@ -1,28 +0,0 @@
-import importlib.util
-import shutil
-import os
-import ctypes
-import logging
-
-
-def fix_pytorch_libomp():
-    """
-    Fix PyTorch libomp DLL issue on Windows by copying the correct DLL file if needed.
-    """
-    torch_spec = importlib.util.find_spec("torch")
-    for folder in torch_spec.submodule_search_locations:
-        lib_folder = os.path.join(folder, "lib")
-        test_file = os.path.join(lib_folder, "fbgemm.dll")
-        dest = os.path.join(lib_folder, "libomp140.x86_64.dll")
-        if os.path.exists(dest):
-            break
-
-        with open(test_file, "rb") as f:
-            contents = f.read()
-            if b"libomp140.x86_64.dll" not in contents:
-                break
-        try:
-            ctypes.cdll.LoadLibrary(test_file)
-        except FileNotFoundError:
-            logging.warning("Detected pytorch version with libomp issue, patching.")
-            shutil.copyfile(os.path.join(lib_folder, "libiomp5md.dll"), dest)

main.py

@@ -125,13 +125,6 @@ if __name__ == "__main__":
 
     import cuda_malloc
 
-if args.windows_standalone_build:
-    try:
-        from fix_torch import fix_pytorch_libomp
-        fix_pytorch_libomp()
-    except:
-        pass
-
 import comfy.utils
 
 import execution
@@ -267,7 +260,6 @@ def start_comfyui(asyncio_loop=None):
         asyncio_loop = asyncio.new_event_loop()
         asyncio.set_event_loop(asyncio_loop)
     prompt_server = server.PromptServer(asyncio_loop)
-    q = execution.PromptQueue(prompt_server)
 
     hook_breaker_ac10a0.save_functions()
     nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes, init_api_nodes=not args.disable_api_nodes)
@@ -278,7 +270,7 @@ def start_comfyui(asyncio_loop=None):
     prompt_server.add_routes()
     hijack_progress(prompt_server)
 
-    threading.Thread(target=prompt_worker, daemon=True, args=(q, prompt_server,)).start()
+    threading.Thread(target=prompt_worker, daemon=True, args=(prompt_server.prompt_queue, prompt_server,)).start()
 
     if args.quick_test_for_ci:
         exit(0)

node_helpers.py

@@ -5,12 +5,18 @@ from comfy.cli_args import args
 
 from PIL import ImageFile, UnidentifiedImageError
 
-def conditioning_set_values(conditioning, values={}):
+def conditioning_set_values(conditioning, values={}, append=False):
     c = []
     for t in conditioning:
         n = [t[0], t[1].copy()]
         for k in values:
-            n[1][k] = values[k]
+            val = values[k]
+            if append:
+                old_val = n[1].get(k, None)
+                if old_val is not None:
+                    val = old_val + val
+
+            n[1][k] = val
         c.append(n)
 
     return c

nodes.py

@@ -1103,16 +1103,7 @@ class unCLIPConditioning:
         if strength == 0:
             return (conditioning, )
 
-        c = []
-        for t in conditioning:
-            o = t[1].copy()
-            x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}
-            if "unclip_conditioning" in o:
-                o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x]
-            else:
-                o["unclip_conditioning"] = [x]
-            n = [t[0], o]
-            c.append(n)
+        c = node_helpers.conditioning_set_values(conditioning, {"unclip_conditioning": [{"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}]}, append=True)
         return (c, )
 
 class GLIGENLoader:
@@ -1940,7 +1931,7 @@ class ImagePadForOutpaint:
 
         mask[top:top + d2, left:left + d3] = t
 
-        return (new_image, mask)
+        return (new_image, mask.unsqueeze(0))
 
 
 NODE_CLASS_MAPPINGS = {
@@ -2070,6 +2061,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImagePadForOutpaint": "Pad Image for Outpainting",
     "ImageBatch": "Batch Images",
     "ImageCrop": "Image Crop",
+    "ImageStitch": "Image Stitch",
     "ImageBlend": "Image Blend",
     "ImageBlur": "Image Blur",
     "ImageQuantize": "Image Quantize",
@@ -2261,8 +2253,11 @@ def init_builtin_extra_nodes():
         "nodes_optimalsteps.py",
         "nodes_hidream.py",
         "nodes_fresca.py",
+        "nodes_apg.py",
         "nodes_preview_any.py",
         "nodes_ace.py",
+        "nodes_string.py",
+        "nodes_camera_trajectory.py",
     ]
 
     import_failed = []
@@ -2287,6 +2282,10 @@ def init_builtin_api_nodes():
         "nodes_pixverse.py",
         "nodes_stability.py",
         "nodes_pika.py",
+        "nodes_runway.py",
+        "nodes_tripo.py",
+        "nodes_rodin.py",
+        "nodes_gemini.py",
     ]
 
     if not load_custom_node(os.path.join(api_nodes_dir, "canary.py"), module_parent="comfy_api_nodes"):

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.34"
+version = "0.3.39"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,5 +1,6 @@
-comfyui-frontend-package==1.18.10
-comfyui-workflow-templates==0.1.14
+comfyui-frontend-package==1.21.6
+comfyui-workflow-templates==0.1.25
+comfyui-embedded-docs==0.2.0
 torch
 torchsde
 torchvision

script_examples/basic_api_example.py

@@ -101,6 +101,14 @@ prompt_text = """
 
 def queue_prompt(prompt):
     p = {"prompt": prompt}
+
+    # If the workflow contains API nodes, you can add a Comfy API key to the `extra_data`` field of the payload.
+    # p["extra_data"] = {
+    #     "api_key_comfy_org": "comfyui-87d01e28d*******************************************************"  # replace with real key
+    # }
+    # See: https://docs.comfy.org/tutorials/api-nodes/overview
+    # Generate a key here: https://platform.comfy.org/login
+
     data = json.dumps(p).encode('utf-8')
     req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
     request.urlopen(req)

server.py

@@ -29,6 +29,7 @@ import comfy.model_management
 import node_helpers
 from comfyui_version import __version__
 from app.frontend_management import FrontendManager
+
 from app.user_manager import UserManager
 from app.model_manager import ModelFileManager
 from app.custom_node_manager import CustomNodeManager
@@ -159,7 +160,7 @@ class PromptServer():
         self.custom_node_manager = CustomNodeManager()
         self.internal_routes = InternalRoutes(self)
         self.supports = ["custom_nodes_from_web"]
-        self.prompt_queue = None
+        self.prompt_queue = execution.PromptQueue(self)
         self.loop = loop
         self.messages = asyncio.Queue()
         self.client_session:Optional[aiohttp.ClientSession] = None
@@ -226,7 +227,7 @@ class PromptServer():
             return response
 
         @routes.get("/embeddings")
-        def get_embeddings(self):
+        def get_embeddings(request):
             embeddings = folder_paths.get_filename_list("embeddings")
             return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings)))
 
@@ -282,7 +283,6 @@ class PromptServer():
                     a.update(f.read())
                     b.update(image.file.read())
                     image.file.seek(0)
-                    f.close()
                 return a.hexdigest() == b.hexdigest()
             return False
 
@@ -390,7 +390,7 @@ class PromptServer():
         async def view_image(request):
             if "filename" in request.rel_url.query:
                 filename = request.rel_url.query["filename"]
-                filename,output_dir = folder_paths.annotated_filepath(filename)
+                filename, output_dir = folder_paths.annotated_filepath(filename)
 
                 if not filename:
                     return web.Response(status=400)
@@ -476,9 +476,8 @@ class PromptServer():
                         # Get content type from mimetype, defaulting to 'application/octet-stream'
                         content_type = mimetypes.guess_type(filename)[0] or 'application/octet-stream'
 
-                        # For security, force certain extensions to download instead of display
-                        file_extension = os.path.splitext(filename)[1].lower()
-                        if file_extension in {'.html', '.htm', '.js', '.css'}:
+                        # For security, force certain mimetypes to download instead of display
+                        if content_type in {'text/html', 'text/html-sandboxed', 'application/xhtml+xml', 'text/javascript', 'text/css'}:
                             content_type = 'application/octet-stream'  # Forces download
 
                         return web.FileResponse(
@@ -621,7 +620,7 @@ class PromptServer():
         @routes.get("/queue")
         async def get_queue(request):
             queue_info = {}
-            current_queue = self.prompt_queue.get_current_queue()
+            current_queue = self.prompt_queue.get_current_queue_volatile()
             queue_info['queue_running'] = current_queue[0]
             queue_info['queue_pending'] = current_queue[1]
             return web.json_response(queue_info)
@@ -746,6 +745,13 @@ class PromptServer():
                 web.static('/templates', workflow_templates_path)
             ])
 
+        # Serve embedded documentation from the package
+        embedded_docs_path = FrontendManager.embedded_docs_path()
+        if embedded_docs_path:
+            self.app.add_routes([
+                web.static('/docs', embedded_docs_path)
+            ])
+
         self.app.add_routes([
             web.static('/', self.web_root),
         ])

tests-unit/comfy_api_test/video_types_test.py

@@ -0,0 +1,239 @@
+import pytest
+import torch
+import tempfile
+import os
+import av
+import io
+from fractions import Fraction
+from comfy_api.input_impl.video_types import VideoFromFile, VideoFromComponents
+from comfy_api.util.video_types import VideoComponents
+from comfy_api.input.basic_types import AudioInput
+from av.error import InvalidDataError
+
+EPSILON = 0.0001
+
+
+@pytest.fixture
+def sample_images():
+    """3-frame 2x2 RGB video tensor"""
+    return torch.rand(3, 2, 2, 3)
+
+
+@pytest.fixture
+def sample_audio():
+    """Stereo audio with 44.1kHz sample rate"""
+    return AudioInput(
+        {
+            "waveform": torch.rand(1, 2, 1000),
+            "sample_rate": 44100,
+        }
+    )
+
+
+@pytest.fixture
+def video_components(sample_images, sample_audio):
+    """VideoComponents with images, audio, and metadata"""
+    return VideoComponents(
+        images=sample_images,
+        audio=sample_audio,
+        frame_rate=Fraction(30),
+        metadata={"test": "metadata"},
+    )
+
+
+def create_test_video(width=4, height=4, frames=3, fps=30):
+    """Helper to create a temporary video file"""
+    tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+    with av.open(tmp.name, mode="w") as container:
+        stream = container.add_stream("h264", rate=fps)
+        stream.width = width
+        stream.height = height
+        stream.pix_fmt = "yuv420p"
+
+        for i in range(frames):
+            frame = av.VideoFrame.from_ndarray(
+                torch.ones(height, width, 3, dtype=torch.uint8).numpy() * (i * 85),
+                format="rgb24",
+            )
+            frame = frame.reformat(format="yuv420p")
+            packet = stream.encode(frame)
+            container.mux(packet)
+
+        # Flush
+        packet = stream.encode(None)
+        container.mux(packet)
+
+    return tmp.name
+
+
+@pytest.fixture
+def simple_video_file():
+    """4x4 video with 3 frames at 30fps"""
+    file_path = create_test_video()
+    yield file_path
+    os.unlink(file_path)
+
+
+def test_video_from_components_get_duration(video_components):
+    """Duration calculated correctly from frame count and frame rate"""
+    video = VideoFromComponents(video_components)
+    duration = video.get_duration()
+
+    expected_duration = 3.0 / 30.0
+    assert duration == pytest.approx(expected_duration)
+
+
+def test_video_from_components_get_duration_different_frame_rates(sample_images):
+    """Duration correct for different frame rates including fractional"""
+    # Test with 60 fps
+    components_60fps = VideoComponents(images=sample_images, frame_rate=Fraction(60))
+    video_60fps = VideoFromComponents(components_60fps)
+    assert video_60fps.get_duration() == pytest.approx(3.0 / 60.0)
+
+    # Test with fractional frame rate (23.976fps)
+    components_frac = VideoComponents(
+        images=sample_images, frame_rate=Fraction(24000, 1001)
+    )
+    video_frac = VideoFromComponents(components_frac)
+    expected_frac = 3.0 / (24000.0 / 1001.0)
+    assert video_frac.get_duration() == pytest.approx(expected_frac)
+
+
+def test_video_from_components_get_duration_empty_video():
+    """Duration is zero for empty video"""
+    empty_components = VideoComponents(
+        images=torch.zeros(0, 2, 2, 3), frame_rate=Fraction(30)
+    )
+    video = VideoFromComponents(empty_components)
+    assert video.get_duration() == 0.0
+
+
+def test_video_from_components_get_dimensions(video_components):
+    """Dimensions returned correctly from image tensor shape"""
+    video = VideoFromComponents(video_components)
+    width, height = video.get_dimensions()
+    assert width == 2
+    assert height == 2
+
+
+def test_video_from_file_get_duration(simple_video_file):
+    """Duration extracted from file metadata"""
+    video = VideoFromFile(simple_video_file)
+    duration = video.get_duration()
+    assert duration == pytest.approx(0.1, abs=0.01)
+
+
+def test_video_from_file_get_dimensions(simple_video_file):
+    """Dimensions read from stream without decoding frames"""
+    video = VideoFromFile(simple_video_file)
+    width, height = video.get_dimensions()
+    assert width == 4
+    assert height == 4
+
+
+def test_video_from_file_bytesio_input():
+    """VideoFromFile works with BytesIO input"""
+    buffer = io.BytesIO()
+    with av.open(buffer, mode="w", format="mp4") as container:
+        stream = container.add_stream("h264", rate=30)
+        stream.width = 2
+        stream.height = 2
+        stream.pix_fmt = "yuv420p"
+
+        frame = av.VideoFrame.from_ndarray(
+            torch.zeros(2, 2, 3, dtype=torch.uint8).numpy(), format="rgb24"
+        )
+        frame = frame.reformat(format="yuv420p")
+        packet = stream.encode(frame)
+        container.mux(packet)
+        packet = stream.encode(None)
+        container.mux(packet)
+
+    buffer.seek(0)
+    video = VideoFromFile(buffer)
+
+    assert video.get_dimensions() == (2, 2)
+    assert video.get_duration() == pytest.approx(1 / 30, abs=0.01)
+
+
+def test_video_from_file_invalid_file_error():
+    """InvalidDataError raised for non-video files"""
+    with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as tmp:
+        tmp.write(b"not a video file")
+        tmp.flush()
+        tmp_name = tmp.name
+
+    try:
+        with pytest.raises(InvalidDataError):
+            video = VideoFromFile(tmp_name)
+            video.get_dimensions()
+    finally:
+        os.unlink(tmp_name)
+
+
+def test_video_from_file_audio_only_error():
+    """ValueError raised for audio-only files"""
+    with tempfile.NamedTemporaryFile(suffix=".m4a", delete=False) as tmp:
+        tmp_name = tmp.name
+
+    try:
+        with av.open(tmp_name, mode="w") as container:
+            stream = container.add_stream("aac", rate=44100)
+            stream.sample_rate = 44100
+            stream.format = "fltp"
+
+            audio_data = torch.zeros(1, 1024).numpy()
+            audio_frame = av.AudioFrame.from_ndarray(
+                audio_data, format="fltp", layout="mono"
+            )
+            audio_frame.sample_rate = 44100
+            audio_frame.pts = 0
+            packet = stream.encode(audio_frame)
+            container.mux(packet)
+
+            for packet in stream.encode(None):
+                container.mux(packet)
+
+        with pytest.raises(ValueError, match="No video stream found"):
+            video = VideoFromFile(tmp_name)
+            video.get_dimensions()
+    finally:
+        os.unlink(tmp_name)
+
+
+def test_single_frame_video():
+    """Single frame video has correct duration"""
+    components = VideoComponents(
+        images=torch.rand(1, 10, 10, 3), frame_rate=Fraction(1)
+    )
+    video = VideoFromComponents(components)
+    assert video.get_duration() == 1.0
+
+
+@pytest.mark.parametrize(
+    "frame_rate,expected_fps",
+    [
+        (Fraction(24000, 1001), 24000 / 1001),
+        (Fraction(30000, 1001), 30000 / 1001),
+        (Fraction(25, 1), 25.0),
+        (Fraction(50, 2), 25.0),
+    ],
+)
+def test_fractional_frame_rates(frame_rate, expected_fps):
+    """Duration calculated correctly for various fractional frame rates"""
+    components = VideoComponents(images=torch.rand(100, 4, 4, 3), frame_rate=frame_rate)
+    video = VideoFromComponents(components)
+    duration = video.get_duration()
+    expected_duration = 100.0 / expected_fps
+    assert duration == pytest.approx(expected_duration)
+
+
+def test_duration_consistency(video_components):
+    """get_duration() consistent with manual calculation from components"""
+    video = VideoFromComponents(video_components)
+
+    duration = video.get_duration()
+    components = video.get_components()
+    manual_duration = float(components.images.shape[0] / components.frame_rate)
+
+    assert duration == pytest.approx(manual_duration)

tests-unit/comfy_extras_test/image_stitch_test.py

@@ -0,0 +1,240 @@
+import torch
+from unittest.mock import patch, MagicMock
+
+# Mock nodes module to prevent CUDA initialization during import
+mock_nodes = MagicMock()
+mock_nodes.MAX_RESOLUTION = 16384
+
+with patch.dict('sys.modules', {'nodes': mock_nodes}):
+    from comfy_extras.nodes_images import ImageStitch
+
+
+class TestImageStitch:
+
+    def create_test_image(self, batch_size=1, height=64, width=64, channels=3):
+        """Helper to create test images with specific dimensions"""
+        return torch.rand(batch_size, height, width, channels)
+
+    def test_no_image2_passthrough(self):
+        """Test that when image2 is None, image1 is returned unchanged"""
+        node = ImageStitch()
+        image1 = self.create_test_image()
+
+        result = node.stitch(image1, "right", True, 0, "white", image2=None)
+
+        assert len(result) == 1
+        assert torch.equal(result[0], image1)
+
+    def test_basic_horizontal_stitch_right(self):
+        """Test basic horizontal stitching to the right"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=24)
+
+        result = node.stitch(image1, "right", False, 0, "white", image2)
+
+        assert result[0].shape == (1, 32, 56, 3)  # 32 + 24 width
+
+    def test_basic_horizontal_stitch_left(self):
+        """Test basic horizontal stitching to the left"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=24)
+
+        result = node.stitch(image1, "left", False, 0, "white", image2)
+
+        assert result[0].shape == (1, 32, 56, 3)  # 24 + 32 width
+
+    def test_basic_vertical_stitch_down(self):
+        """Test basic vertical stitching downward"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=24, width=32)
+
+        result = node.stitch(image1, "down", False, 0, "white", image2)
+
+        assert result[0].shape == (1, 56, 32, 3)  # 32 + 24 height
+
+    def test_basic_vertical_stitch_up(self):
+        """Test basic vertical stitching upward"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=24, width=32)
+
+        result = node.stitch(image1, "up", False, 0, "white", image2)
+
+        assert result[0].shape == (1, 56, 32, 3)  # 24 + 32 height
+
+    def test_size_matching_horizontal(self):
+        """Test size matching for horizontal concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=64, width=64)
+        image2 = self.create_test_image(height=32, width=32)  # Different aspect ratio
+
+        result = node.stitch(image1, "right", True, 0, "white", image2)
+
+        # image2 should be resized to match image1's height (64) with preserved aspect ratio
+        expected_width = 64 + 64  # original + resized (32*64/32 = 64)
+        assert result[0].shape == (1, 64, expected_width, 3)
+
+    def test_size_matching_vertical(self):
+        """Test size matching for vertical concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=64, width=64)
+        image2 = self.create_test_image(height=32, width=32)
+
+        result = node.stitch(image1, "down", True, 0, "white", image2)
+
+        # image2 should be resized to match image1's width (64) with preserved aspect ratio
+        expected_height = 64 + 64  # original + resized (32*64/32 = 64)
+        assert result[0].shape == (1, expected_height, 64, 3)
+
+    def test_padding_for_mismatched_heights_horizontal(self):
+        """Test padding when heights don't match in horizontal concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=64, width=32)
+        image2 = self.create_test_image(height=48, width=24)  # Shorter height
+
+        result = node.stitch(image1, "right", False, 0, "white", image2)
+
+        # Both images should be padded to height 64
+        assert result[0].shape == (1, 64, 56, 3)  # 32 + 24 width, max(64,48) height
+
+    def test_padding_for_mismatched_widths_vertical(self):
+        """Test padding when widths don't match in vertical concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=64)
+        image2 = self.create_test_image(height=24, width=48)  # Narrower width
+
+        result = node.stitch(image1, "down", False, 0, "white", image2)
+
+        # Both images should be padded to width 64
+        assert result[0].shape == (1, 56, 64, 3)  # 32 + 24 height, max(64,48) width
+
+    def test_spacing_horizontal(self):
+        """Test spacing addition in horizontal concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=24)
+        spacing_width = 16
+
+        result = node.stitch(image1, "right", False, spacing_width, "white", image2)
+
+        # Expected width: 32 + 16 (spacing) + 24 = 72
+        assert result[0].shape == (1, 32, 72, 3)
+
+    def test_spacing_vertical(self):
+        """Test spacing addition in vertical concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=24, width=32)
+        spacing_width = 16
+
+        result = node.stitch(image1, "down", False, spacing_width, "white", image2)
+
+        # Expected height: 32 + 16 (spacing) + 24 = 72
+        assert result[0].shape == (1, 72, 32, 3)
+
+    def test_spacing_color_values(self):
+        """Test that spacing colors are applied correctly"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=32)
+
+        # Test white spacing
+        result_white = node.stitch(image1, "right", False, 16, "white", image2)
+        # Check that spacing region contains white values (close to 1.0)
+        spacing_region = result_white[0][:, :, 32:48, :]  # Middle 16 pixels
+        assert torch.all(spacing_region >= 0.9)  # Should be close to white
+
+        # Test black spacing
+        result_black = node.stitch(image1, "right", False, 16, "black", image2)
+        spacing_region = result_black[0][:, :, 32:48, :]
+        assert torch.all(spacing_region <= 0.1)  # Should be close to black
+
+    def test_odd_spacing_width_made_even(self):
+        """Test that odd spacing widths are made even"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=32)
+
+        # Use odd spacing width
+        result = node.stitch(image1, "right", False, 15, "white", image2)
+
+        # Should be made even (16), so total width = 32 + 16 + 32 = 80
+        assert result[0].shape == (1, 32, 80, 3)
+
+    def test_batch_size_matching(self):
+        """Test that different batch sizes are handled correctly"""
+        node = ImageStitch()
+        image1 = self.create_test_image(batch_size=2, height=32, width=32)
+        image2 = self.create_test_image(batch_size=1, height=32, width=32)
+
+        result = node.stitch(image1, "right", False, 0, "white", image2)
+
+        # Should match larger batch size
+        assert result[0].shape == (2, 32, 64, 3)
+
+    def test_channel_matching_rgb_to_rgba(self):
+        """Test that channel differences are handled (RGB + alpha)"""
+        node = ImageStitch()
+        image1 = self.create_test_image(channels=3)  # RGB
+        image2 = self.create_test_image(channels=4)  # RGBA
+
+        result = node.stitch(image1, "right", False, 0, "white", image2)
+
+        # Should have 4 channels (RGBA)
+        assert result[0].shape[-1] == 4
+
+    def test_channel_matching_rgba_to_rgb(self):
+        """Test that channel differences are handled (RGBA + RGB)"""
+        node = ImageStitch()
+        image1 = self.create_test_image(channels=4)  # RGBA
+        image2 = self.create_test_image(channels=3)  # RGB
+
+        result = node.stitch(image1, "right", False, 0, "white", image2)
+
+        # Should have 4 channels (RGBA)
+        assert result[0].shape[-1] == 4
+
+    def test_all_color_options(self):
+        """Test all available color options"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=32)
+
+        colors = ["white", "black", "red", "green", "blue"]
+
+        for color in colors:
+            result = node.stitch(image1, "right", False, 16, color, image2)
+            assert result[0].shape == (1, 32, 80, 3)  # Basic shape check
+
+    def test_all_directions(self):
+        """Test all direction options"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=32)
+
+        directions = ["right", "left", "up", "down"]
+
+        for direction in directions:
+            result = node.stitch(image1, direction, False, 0, "white", image2)
+            assert result[0].shape == (1, 32, 64, 3) if direction in ["right", "left"] else (1, 64, 32, 3)
+
+    def test_batch_size_channel_spacing_integration(self):
+        """Test integration of batch matching, channel matching, size matching, and spacings"""
+        node = ImageStitch()
+        image1 = self.create_test_image(batch_size=2, height=64, width=48, channels=3)
+        image2 = self.create_test_image(batch_size=1, height=32, width=32, channels=4)
+
+        result = node.stitch(image1, "right", True, 8, "red", image2)
+
+        # Should handle: batch matching, size matching, channel matching, spacing
+        assert result[0].shape[0] == 2  # Batch size matched
+        assert result[0].shape[-1] == 4  # Channels matched to max
+        assert result[0].shape[1] == 64  # Height from image1 (size matching)
+        # Width should be: 48 + 8 (spacing) + resized_image2_width
+        expected_image2_width = int(64 * (32/32))  # Resized to height 64
+        expected_total_width = 48 + 8 + expected_image2_width
+        assert result[0].shape[2] == expected_total_width
+