Merge branch 'master' into v3-definition

* [feat] Add ImageStitch node for concatenating images with borders

Add ImageStitch node that concatenates images in four directions with optional borders and intelligent size handling. Features include optional second image input, configurable borders with color selection, automatic batch size matching, and dimension alignment via padding or resizing.

Upstreamed from https://github.com/kijai/ComfyUI-KJNodes with enhancements for better error handling and comprehensive test coverage.

* [fix] Fix CI issues with CUDA dependencies and linting

- Mock CUDA-dependent modules in tests to avoid CI failures on CPU-only runners
- Fix ruff linting issues for code style compliance

* [fix] Improve CI compatibility by mocking nodes module import

Prevent CUDA initialization chain by mocking the nodes module at import time,
which is cleaner than deep mocking of CUDA-specific functions.

* [refactor] Clean up ImageStitch tests

- Remove unnecessary sys.path manipulation (pythonpath set in pytest.ini)
- Remove metadata tests that test framework internals rather than functionality
- Rename complex scenario test to be more descriptive of what it tests

* [refactor] Rename 'border' to 'spacing' for semantic accuracy

- Change border_width/border_color to spacing_width/spacing_color in API
- Update all tests to use spacing terminology
- Update comments and variable names throughout
- More accurately describes the gap/separator between images

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

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/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/wan/model.py

@@ -539,13 +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)

comfy/lora.py

@@ -283,8 +283,9 @@ 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:

comfy/model_base.py

@@ -135,6 +135,7 @@ 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(
@@ -167,6 +168,11 @@ class BaseModel(torch.nn.Module):
             if hasattr(extra, "dtype"):
                 if extra.dtype != torch.int and extra.dtype != torch.long:
                     extra = extra.to(dtype)
+            if isinstance(extra, list):
+                ex = []
+                for ext in extra:
+                    ex.append(ext.to(dtype))
+                extra = ex
             extra_conds[o] = extra
 
         t = self.process_timestep(t, x=x, **extra_conds)
@@ -325,19 +331,28 @@ 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 = []
@@ -1047,6 +1062,11 @@ 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
 
 

comfy/model_management.py

@@ -297,8 +297,13 @@ 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", "gfx1151"]):  # TODO: more arches

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/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_api/v3/io.py

@@ -0,0 +1,855 @@
+from __future__ import annotations
+from typing import Any, Literal
+from enum import Enum
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, asdict
+from comfy.comfy_types.node_typing import IO
+
+
+class InputBehavior(str, Enum):
+    required = "required"
+    optional = "optional"
+
+
+def is_class(obj):
+    '''
+    Returns True if is a class type.
+    Returns False if is a class instance.
+    '''
+    return isinstance(obj, type)
+
+
+class NumberDisplay(str, Enum):
+    number = "number"
+    slider = "slider"
+
+
+class IO_V3:
+    '''
+    Base class for V3 Inputs and Outputs.
+    '''
+    def __init__(self):
+        pass
+
+    def __init_subclass__(cls, io_type: IO | str, **kwargs):
+        cls.io_type = io_type
+        super().__init_subclass__(**kwargs)
+
+class InputV3(IO_V3, io_type=None):
+    '''
+    Base class for a V3 Input.
+    '''
+    def __init__(self, id: str, display_name: str=None, behavior=InputBehavior.required, tooltip: str=None, lazy: bool=None):
+        super().__init__()
+        self.id = id
+        self.display_name = display_name
+        self.behavior = behavior
+        self.tooltip = tooltip
+        self.lazy = lazy
+    
+    def as_dict_V1(self):
+        return prune_dict({
+            "display_name": self.display_name,
+            "tooltip": self.tooltip,
+            "lazy": self.lazy
+        })
+    
+    def get_io_type_V1(self):
+        return self.io_type
+
+class WidgetInputV3(InputV3, io_type=None):
+    '''
+    Base class for a V3 Input with widget.
+    '''
+    def __init__(self, id: str, display_name: str=None, behavior=InputBehavior.required, tooltip: str=None, lazy: bool=None,
+                 default: Any=None,
+                 socketless: bool=None, widgetType: str=None):
+        super().__init__(id, display_name, behavior, tooltip, lazy)
+        self.default = default
+        self.socketless = socketless
+        self.widgetType = widgetType
+    
+    def as_dict_V1(self):
+        return super().as_dict_V1() | prune_dict({
+            "default": self.default,
+            "socketless": self.socketless,
+            "widgetType": self.widgetType,
+        })
+
+def CustomType(io_type: IO | str) -> type[IO_V3]:
+    name = f"{io_type}_IO_V3"
+    return type(name, (IO_V3,), {}, io_type=io_type)
+
+def CustomInput(id: str, io_type: IO | str, display_name: str=None, behavior=InputBehavior.required, tooltip: str=None, lazy: bool=None) -> InputV3:
+    '''
+    Defines input for 'io_type'. Can be used to stand in for non-core types.
+    '''
+    input_kwargs = {
+        "id": id,
+        "display_name": display_name,
+        "behavior": behavior,
+        "tooltip": tooltip,
+        "lazy": lazy,
+    }
+    return type(f"{io_type}Input", (InputV3,), {}, io_type=io_type)(**input_kwargs)
+
+def CustomOutput(id: str, io_type: IO | str, display_name: str=None, tooltip: str=None) -> OutputV3:
+    '''
+    Defines output for 'io_type'. Can be used to stand in for non-core types.
+    '''
+    input_kwargs = {
+        "id": id,
+        "display_name": display_name,
+        "tooltip": tooltip,
+    }
+    return type(f"{io_type}Output", (OutputV3,), {}, io_type=io_type)(**input_kwargs)
+
+
+class BooleanInput(WidgetInputV3, io_type=IO.BOOLEAN):
+    '''
+    Boolean input.
+    '''
+    def __init__(self, id: str, display_name: str=None, behavior=InputBehavior.required, tooltip: str=None, lazy: bool=None,
+                 default: bool=None, label_on: str=None, label_off: str=None,
+                 socketless: bool=None, widgetType: str=None):
+        super().__init__(id, display_name, behavior, tooltip, lazy, default, socketless, widgetType)
+        self.label_on = label_on
+        self.label_off = label_off
+        self.default: bool
+    
+    def as_dict_V1(self):
+        return super().as_dict_V1() | prune_dict({
+            "label_on": self.label_on,
+            "label_off": self.label_off,
+        })
+
+class IntegerInput(WidgetInputV3, io_type=IO.INT):
+    '''
+    Integer input.
+    '''
+    def __init__(self, id: str, display_name: str=None, behavior=InputBehavior.required, tooltip: str=None, lazy: bool=None,
+                 default: int=None, min: int=None, max: int=None, step: int=None, control_after_generate: bool=None,
+                 display_mode: NumberDisplay=None, socketless: bool=None, widgetType: str=None):
+        super().__init__(id, display_name, behavior, tooltip, lazy, default, socketless, widgetType)
+        self.min = min
+        self.max = max
+        self.step = step
+        self.control_after_generate = control_after_generate
+        self.display_mode = display_mode
+        self.default: int
+    
+    def as_dict_V1(self):
+        return super().as_dict_V1() | prune_dict({
+            "min": self.min,
+            "max": self.max,
+            "step": self.step,
+            "control_after_generate": self.control_after_generate,
+            "display": self.display_mode, # NOTE: in frontend, the parameter is called "display"
+        })
+
+class FloatInput(WidgetInputV3, io_type=IO.FLOAT):
+    '''
+    Float input.
+    '''
+    def __init__(self, id: str, display_name: str=None, behavior=InputBehavior.required, tooltip: str=None, lazy: bool=None,
+                 default: float=None, min: float=None, max: float=None, step: float=None, round: float=None,
+                 display_mode: NumberDisplay=None, socketless: bool=None, widgetType: str=None):
+        super().__init__(id, display_name, behavior, tooltip, lazy, default, socketless, widgetType)
+        self.default = default
+        self.min = min
+        self.max = max
+        self.step = step
+        self.round = round
+        self.display_mode = display_mode
+        self.default: float
+    
+    def as_dict_V1(self):
+        return super().as_dict_V1() | prune_dict({
+            "min": self.min,
+            "max": self.max,
+            "step": self.step,
+            "round": self.round,
+            "display": self.display_mode, # NOTE: in frontend, the parameter is called "display"
+        })
+
+class StringInput(WidgetInputV3, io_type=IO.STRING):
+    '''
+    String input.
+    '''
+    def __init__(self, id: str, display_name: str=None, behavior=InputBehavior.required, tooltip: str=None, lazy: bool=None,
+                 multiline=False, placeholder: str=None, default: int=None,
+                 socketless: bool=None, widgetType: str=None):
+        super().__init__(id, display_name, behavior, tooltip, lazy, default, socketless, widgetType)
+        self.multiline = multiline
+        self.placeholder = placeholder
+        self.default: str
+    
+    def as_dict_V1(self):
+        return super().as_dict_V1() | prune_dict({
+            "multiline": self.multiline,
+            "placeholder": self.placeholder,
+        })
+
+class ComboInput(WidgetInputV3, io_type=IO.COMBO):
+    '''Combo input (dropdown).'''
+    def __init__(self, id: str, options: list[str], display_name: str=None, behavior=InputBehavior.required, tooltip: str=None, lazy: bool=None,
+                 default: str=None, control_after_generate: bool=None,
+                 socketless: bool=None, widgetType: str=None):
+        super().__init__(id, display_name, behavior, tooltip, lazy, default, socketless, widgetType)
+        self.multiselect = False
+        self.options = options
+        self.control_after_generate = control_after_generate
+        self.default: str
+    
+    def as_dict_V1(self):
+        return super().as_dict_V1() | prune_dict({
+            "multiselect": self.multiselect,
+            "options": self.options,
+            "control_after_generate": self.control_after_generate,
+        })
+
+class MultiselectComboWidget(ComboInput, io_type=IO.COMBO):
+    '''Multiselect Combo input (dropdown for selecting potentially more than one value).'''
+    def __init__(self, id: str, options: list[str], display_name: str=None, behavior=InputBehavior.required, tooltip: str=None, lazy: bool=None,
+                 default: list[str]=None, placeholder: str=None, chip: bool=None, control_after_generate: bool=None,
+                 socketless: bool=None, widgetType: str=None):
+        super().__init__(id, options, display_name, behavior, tooltip, lazy, default, control_after_generate, socketless, widgetType)
+        self.multiselect = True
+        self.placeholder = placeholder
+        self.chip = chip
+        self.default: list[str]
+    
+    def as_dict_V1(self):
+        return super().as_dict_V1() | prune_dict({
+            "multiselect": self.multiselect,
+            "placeholder": self.placeholder,
+            "chip": self.chip,
+        })
+
+class ImageInput(InputV3, io_type=IO.IMAGE):
+    '''
+    Image input.
+    '''
+    def __init__(self, id: str, display_name: str=None, behavior=InputBehavior.required, tooltip: str=None):
+        super().__init__(id, display_name, behavior, tooltip)
+
+class MaskInput(InputV3, io_type=IO.MASK):
+    '''
+    Mask input.
+    '''
+    def __init__(self, id: str, display_name: str=None, behavior=InputBehavior.required, tooltip: str=None):
+        super().__init__(id, display_name, behavior, tooltip)
+
+class LatentInput(InputV3, io_type=IO.LATENT):
+    '''
+    Latent input.
+    '''
+    def __init__(self, id: str, display_name: str=None, behavior=InputBehavior.required, tooltip: str=None):
+        super().__init__(id, display_name, behavior, tooltip)
+
+class MultitypedInput(InputV3, io_type="COMFY_MULTITYPED_V3"):
+    '''
+    Input that permits more than one input type.
+    '''
+    def __init__(self, id: str, io_types: list[type[IO_V3] | InputV3 | IO |str], display_name: str=None, behavior=InputBehavior.required, tooltip: str=None,):
+        super().__init__(id, display_name, behavior, tooltip)
+        self._io_types = io_types
+    
+    @property
+    def io_types(self) -> list[type[InputV3]]:
+        '''
+        Returns list of InputV3 class types permitted.
+        '''
+        io_types = []
+        for x in self._io_types:
+            if not is_class(x):
+                io_types.append(type(x))
+            else:
+                io_types.append(x)
+        return io_types
+    
+    def get_io_type_V1(self):
+        return ",".join(x.io_type for x in self.io_types)
+
+
+class OutputV3:
+    def __init__(self, id: str, display_name: str=None, tooltip: str=None,
+                 is_output_list=False):
+        self.id = id
+        self.display_name = display_name
+        self.tooltip = tooltip
+        self.is_output_list = is_output_list
+    
+    def __init_subclass__(cls, io_type, **kwargs):
+        cls.io_type = io_type
+        super().__init_subclass__(**kwargs)
+
+class IntegerOutput(OutputV3, io_type=IO.INT):
+    pass
+
+class FloatOutput(OutputV3, io_type=IO.FLOAT):
+    pass
+
+class StringOutput(OutputV3, io_type=IO.STRING):
+    pass
+    # def __init__(self, id: str, display_name: str=None, tooltip: str=None):
+    #     super().__init__(id, display_name, tooltip)
+
+class ImageOutput(OutputV3, io_type=IO.IMAGE):
+    pass
+
+class MaskOutput(OutputV3, io_type=IO.MASK):
+    pass
+
+class LatentOutput(OutputV3, io_type=IO.LATENT):
+    pass
+
+
+class DynamicInput(InputV3, io_type=None):
+    '''
+    Abstract class for dynamic input registration.
+    '''
+    def __init__(self, io_type: str, id: str, display_name: str=None):
+        super().__init__(io_type, id, display_name)
+
+class DynamicOutput(OutputV3, io_type=None):
+    '''
+    Abstract class for dynamic output registration.
+    '''
+    def __init__(self, io_type: str, id: str, display_name: str=None):
+        super().__init__(io_type, id, display_name)
+
+class AutoGrowDynamicInput(DynamicInput, io_type="COMFY_MULTIGROW_V3"):
+    '''
+    Dynamic Input that adds another template_input each time one is provided.
+
+    Additional inputs are forced to have 'InputBehavior.optional'.
+    '''
+    def __init__(self, id: str, template_input: InputV3, min: int=1, max: int=None):
+        super().__init__("AutoGrowDynamicInput", id)
+        self.template_input = template_input
+        if min is not None:
+            assert(min >= 1)
+        if max is not None:
+            assert(max >= 1)
+        self.min = min
+        self.max = max
+
+class ComboDynamicInput(DynamicInput, io_type="COMFY_COMBODYNAMIC_V3"):
+    def __init__(self, id: str):
+        pass
+
+AutoGrowDynamicInput(id="dynamic", template_input=ImageInput(id="image"))
+
+
+class Hidden(str, Enum):
+    '''
+    Enumerator for requesting hidden variables in nodes.
+    '''
+    
+    unique_id = "UNIQUE_ID"
+    """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages)."""
+    prompt = "PROMPT"
+    """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description."""
+    extra_pnginfo = "EXTRA_PNGINFO"
+    """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node)."""
+    dynprompt = "DYNPROMPT"
+    """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion."""
+    auth_token_comfy_org = "AUTH_TOKEN_COMFY_ORG"
+    """AUTH_TOKEN_COMFY_ORG is a token acquired from signing into a ComfyOrg account on frontend."""
+    api_key_comfy_org = "API_KEY_COMFY_ORG"
+    """API_KEY_COMFY_ORG is an API Key generated by ComfyOrg that allows skipping signing into a ComfyOrg account on frontend."""
+
+
+@dataclass
+class NodeInfoV1:
+    input: dict=None
+    input_order: dict[str, list[str]]=None
+    output: list[str]=None
+    output_is_list: list[bool]=None
+    output_name: list[str]=None
+    output_tooltips: list[str]=None
+    name: str=None
+    display_name: str=None
+    description: str=None
+    python_module: Any=None
+    category: str=None
+    output_node: bool=None
+    deprecated: bool=None
+    experimental: bool=None
+    api_node: bool=None
+
+
+def as_pruned_dict(dataclass_obj):
+    '''Return dict of dataclass object with pruned None values.'''
+    return prune_dict(asdict(dataclass_obj))
+
+def prune_dict(d: dict):
+    return {k: v for k,v in d.items() if v is not None}
+
+
+@dataclass
+class SchemaV3:
+    """Definition of V3 node properties."""
+
+    node_id: str
+    """ID of node - should be globally unique. If this is a custom node, add a prefix or postfix to avoid name clashes."""
+    display_name: str = None
+    """Display name of node."""
+    category: str = "sd"
+    """The category of the node, as per the "Add Node" menu."""
+    inputs: list[InputV3]=None
+    outputs: list[OutputV3]=None
+    hidden: list[Hidden]=None
+    description: str=""
+    """Node description, shown as a tooltip when hovering over the node."""
+    is_input_list: bool = False
+    """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes.
+
+    All inputs of ``type`` will become ``list[type]``, regardless of how many items are passed in.  This also affects ``check_lazy_status``.
+
+    From the docs:
+
+    A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``.
+
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing
+    """
+    is_output_node: bool=False
+    """Flags this node as an output node, causing any inputs it requires to be executed.
+
+    If a node is not connected to any output nodes, that node will not be executed.  Usage::
+
+        OUTPUT_NODE = True
+
+    From the docs:
+
+    By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is.
+
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node
+    """
+    is_deprecated: bool=False
+    """Flags a node as deprecated, indicating to users that they should find alternatives to this node."""
+    is_experimental: bool=False
+    """Flags a node as experimental, informing users that it may change or not work as expected."""
+    is_api_node: bool=False
+    """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview."""
+
+# class SchemaV3Class:
+#     def __init__(self,
+#             node_id: str,
+#             node_name: str,
+#             category: str,
+#             inputs: list[InputV3],
+#             outputs: list[OutputV3]=None,
+#             hidden: list[Hidden]=None,
+#             description: str="",
+#             is_input_list: bool = False,
+#             is_output_node: bool=False,
+#             is_deprecated: bool=False,
+#             is_experimental: bool=False,
+#             is_api_node: bool=False,
+#     ):
+#         self.node_id = node_id
+#         """ID of node - should be globally unique. If this is a custom node, add a prefix or postfix to avoid name clashes."""
+#         self.node_name = node_name
+#         """Display name of node."""
+#         self.category = category
+#         """The category of the node, as per the "Add Node" menu."""
+#         self.inputs = inputs
+#         self.outputs = outputs
+#         self.hidden = hidden
+#         self.description = description
+#         """Node description, shown as a tooltip when hovering over the node."""
+#         self.is_input_list = is_input_list
+#         """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes.
+
+#     All inputs of ``type`` will become ``list[type]``, regardless of how many items are passed in.  This also affects ``check_lazy_status``.
+
+#     From the docs:
+
+#     A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``.
+
+#     Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing
+#     """
+#         self.is_output_node = is_output_node
+#         """Flags this node as an output node, causing any inputs it requires to be executed.
+
+#     If a node is not connected to any output nodes, that node will not be executed.  Usage::
+
+#         OUTPUT_NODE = True
+
+#     From the docs:
+
+#     By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is.
+
+#     Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node
+#     """
+#         self.is_deprecated = is_deprecated
+#         """Flags a node as deprecated, indicating to users that they should find alternatives to this node."""
+#         self.is_experimental = is_experimental
+#         """Flags a node as experimental, informing users that it may change or not work as expected."""
+#         self.is_api_node = is_api_node
+#         """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview."""
+
+
+class classproperty(object):
+    def __init__(self, f):
+        self.f = f
+    def __get__(self, obj, owner):
+        return self.f(owner)
+
+
+class ComfyNodeV3(ABC):
+    """Common base class for all V3 nodes."""
+
+    RELATIVE_PYTHON_MODULE = None
+    #############################################
+    # V1 Backwards Compatibility code
+    #--------------------------------------------
+    _DESCRIPTION = None
+    @classproperty
+    def DESCRIPTION(cls):
+        if cls._DESCRIPTION is None:
+            cls.GET_SCHEMA()
+        return cls._DESCRIPTION
+
+    _CATEGORY = None
+    @classproperty
+    def CATEGORY(cls):
+        if cls._CATEGORY is None:
+            cls.GET_SCHEMA()
+        return cls._CATEGORY
+
+    _EXPERIMENTAL = None
+    @classproperty
+    def EXPERIMENTAL(cls):
+        if cls._EXPERIMENTAL is None:
+            cls.GET_SCHEMA()
+        return cls._EXPERIMENTAL
+
+    _DEPRECATED = None
+    @classproperty
+    def DEPRECATED(cls):
+        if cls._DEPRECATED is None:
+            cls.GET_SCHEMA()
+        return cls._DEPRECATED
+
+    _API_NODE = None
+    @classproperty
+    def API_NODE(cls):
+        if cls._API_NODE is None:
+            cls.GET_SCHEMA()
+        return cls._API_NODE
+
+    _OUTPUT_NODE = None
+    @classproperty
+    def OUTPUT_NODE(cls):
+        if cls._OUTPUT_NODE is None:
+            cls.GET_SCHEMA()
+        return cls._OUTPUT_NODE
+
+    _INPUT_IS_LIST = None
+    @classproperty
+    def INPUT_IS_LIST(cls):
+        if cls._INPUT_IS_LIST is None:
+            cls.GET_SCHEMA()
+        return cls._INPUT_IS_LIST
+    _OUTPUT_IS_LIST = None
+
+    @classproperty
+    def OUTPUT_IS_LIST(cls):
+        if cls._OUTPUT_IS_LIST is None:
+            cls.GET_SCHEMA()
+        return cls._OUTPUT_IS_LIST
+
+    _RETURN_TYPES = None
+    @classproperty
+    def RETURN_TYPES(cls):
+        if cls._RETURN_TYPES is None:
+            cls.GET_SCHEMA()
+        return cls._RETURN_TYPES
+
+    _RETURN_NAMES = None
+    @classproperty
+    def RETURN_NAMES(cls):
+        if cls._RETURN_NAMES is None:
+            cls.GET_SCHEMA()
+        return cls._RETURN_NAMES
+
+    _OUTPUT_TOOLTIPS = None
+    @classproperty
+    def OUTPUT_TOOLTIPS(cls):
+        if cls._OUTPUT_TOOLTIPS is None:
+            cls.GET_SCHEMA()
+        return cls._OUTPUT_TOOLTIPS
+
+    FUNCTION = "execute"
+
+    @classmethod
+    def INPUT_TYPES(cls) -> dict[str, dict]:
+        schema = cls.DEFINE_SCHEMA()
+        # for V1, make inputs be a dict with potential keys {required, optional, hidden}
+        input = {
+            "required": {}
+        }
+        if schema.inputs:
+            for i in schema.inputs:
+                input.setdefault(i.behavior.value, {})[i.id] = (i.get_io_type_V1(), i.as_dict_V1())
+        if schema.hidden:
+            for hidden in schema.hidden:
+                input.setdefault("hidden", {})[hidden.name] = (hidden.value,)
+        return input
+
+    @classmethod
+    def GET_SCHEMA(cls) -> SchemaV3:
+        schema = cls.DEFINE_SCHEMA()
+        if cls._DESCRIPTION is None:
+            cls._DESCRIPTION = schema.description
+        if cls._CATEGORY is None:
+            cls._CATEGORY = schema.category
+        if cls._EXPERIMENTAL is None:
+            cls._EXPERIMENTAL = schema.is_experimental
+        if cls._DEPRECATED is None:
+            cls._DEPRECATED = schema.is_deprecated
+        if cls._API_NODE is None:
+            cls._API_NODE = schema.is_api_node
+        if cls._OUTPUT_NODE is None:
+            cls._OUTPUT_NODE = schema.is_output_node
+        if cls._INPUT_IS_LIST is None:
+            cls._INPUT_IS_LIST = schema.is_input_list
+        
+        if cls._RETURN_TYPES is None:
+            output = []
+            output_name = []
+            output_is_list = []
+            output_tooltips = []
+            if schema.outputs:
+                for o in schema.outputs:
+                    output.append(o.io_type)
+                    output_name.append(o.display_name if o.display_name else o.io_type)
+                    output_is_list.append(o.is_output_list)
+                    output_tooltips.append(o.tooltip if o.tooltip else None)
+            
+            cls._RETURN_TYPES = output
+            cls._RETURN_NAMES = output_name
+            cls._OUTPUT_IS_LIST = output_is_list
+            cls._OUTPUT_TOOLTIPS = output_tooltips
+        
+        return schema
+
+    @classmethod
+    def GET_NODE_INFO_V1(cls) -> dict[str, Any]:
+        schema = cls.GET_SCHEMA()
+        # get V1 inputs
+        input = cls.INPUT_TYPES()
+
+        # create separate lists from output fields
+        output = []
+        output_is_list = []
+        output_name = [] 
+        output_tooltips = []
+        if schema.outputs:
+            for o in schema.outputs:
+                output.append(o.io_type)
+                output_is_list.append(o.is_output_list)
+                output_name.append(o.display_name if o.display_name else o.io_type)
+                output_tooltips.append(o.tooltip if o.tooltip else None)
+
+        info = NodeInfoV1(
+            input=input,
+            input_order={key: list(value.keys()) for (key, value) in input.items()},
+            output=output,
+            output_is_list=output_is_list,
+            output_name=output_name,
+            output_tooltips=output_tooltips,
+            name=schema.node_id,
+            display_name=schema.display_name,
+            category=schema.category,
+            description=schema.description,
+            output_node=schema.is_output_node,
+            deprecated=schema.is_deprecated,
+            experimental=schema.is_experimental,
+            api_node=schema.is_api_node,
+            python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes")
+        )
+        return asdict(info)
+    #--------------------------------------------
+    #############################################
+
+    @classmethod
+    def GET_NODE_INFO_V3(cls) -> dict[str, Any]:
+        schema = cls.GET_SCHEMA()
+        # TODO: finish
+        return None
+
+
+    @classmethod
+    @abstractmethod
+    def DEFINE_SCHEMA(cls) -> SchemaV3:
+        """
+        Override this function with one that returns a SchemaV3 instance.
+        """
+        return None
+    DEFINE_SCHEMA = None
+
+    def __init__(self):
+        if self.DEFINE_SCHEMA is None:
+            raise Exception("No DEFINE_SCHEMA function was defined for this node.")
+
+    @abstractmethod
+    def execute(self, **kwargs) -> NodeOutput:
+        pass
+
+
+# class ReturnedInputs:
+#     def __init__(self):
+#         pass
+
+# class ReturnedOutputs:
+#     def __init__(self):
+#         pass
+
+
+class NodeOutput:
+    '''
+    Standardized output of a node; can pass in any number of args and/or a UIOutput into 'ui' kwarg.
+    '''
+    def __init__(self, *args: Any, ui: UIOutput | dict=None, expand: dict=None, block_execution: str=None, **kwargs):
+        self.args = args
+        self.ui = ui
+        self.expand = expand
+        self.block_execution = block_execution
+
+    @property
+    def result(self):
+        return self.args if len(self.args) > 0 else None
+
+
+class SavedResult:
+    def __init__(self, filename: str, subfolder: str, type: Literal["input", "output", "temp"]):
+        self.filename = filename
+        self.subfolder = subfolder
+        self.type = type
+    
+    def as_dict(self):
+        return {
+            "filename": self.filename,
+            "subfolder": self.subfolder,
+            "type": self.type
+        }
+
+class UIOutput(ABC):
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def as_dict(self) -> dict:
+        ... # TODO: finish
+
+class UIImages(UIOutput):
+    def __init__(self, values: list[SavedResult | dict], animated=False, **kwargs):
+        self.values = values
+        self.animated = animated
+    
+    def as_dict(self):
+        values = [x.as_dict() if isinstance(x, SavedResult) else x for x in self.values]
+        return {
+            "images": values,
+            "animated": (self.animated,)
+        }
+
+class UILatents(UIOutput):
+    def __init__(self, values: list[SavedResult | dict], **kwargs):
+        self.values = values
+    
+    def as_dict(self):
+        values = [x.as_dict() if isinstance(x, SavedResult) else x for x in self.values]
+        return {
+            "latents": values,
+        }
+
+class UIAudio(UIOutput):
+    def __init__(self, values: list[SavedResult | dict], **kwargs):
+        self.values = values
+    
+    def as_dict(self):
+        values = [x.as_dict() if isinstance(x, SavedResult) else x for x in self.values]
+        return {
+            "audio": values,
+        }
+
+class UI3D(UIOutput):
+    def __init__(self, values: list[SavedResult | dict], **kwargs):
+        self.values = values
+    
+    def as_dict(self):
+        values = [x.as_dict() if isinstance(x, SavedResult) else x for x in self.values]
+        return {
+            "3d": values,
+        }
+
+class UIText(UIOutput):
+    def __init__(self, value: str, **kwargs):
+        self.value = value
+    
+    def as_dict(self):
+        return {"text": (self.value,)}
+
+
+class TestNode(ComfyNodeV3):
+    SCHEMA = SchemaV3(
+        node_id="TestNode_v3",
+        display_name="Test Node (V3)",
+        category="v3_test",
+        inputs=[IntegerInput("my_int"),
+                #AutoGrowDynamicInput("growing", ImageInput),
+                MaskInput("thing"),
+                ],
+        outputs=[ImageOutput("image_output")],
+        hidden=[Hidden.api_key_comfy_org, Hidden.auth_token_comfy_org, Hidden.unique_id]
+    )
+
+    # @classmethod
+    # def GET_SCHEMA(cls):
+    #     return cls.SCHEMA
+
+    @classmethod
+    def DEFINE_SCHEMA(cls):
+        return cls.SCHEMA
+
+    def execute(**kwargs):
+        pass
+
+
+if __name__ == "__main__":
+    print("hello there")
+    inputs: list[InputV3] = [
+        IntegerInput("my_int"),
+        CustomInput("xyz", "XYZ"),
+        CustomInput("model1", "MODEL_M"),
+        ImageInput("my_image"),
+        FloatInput("my_float"),
+        MultitypedInput("my_inputs", [CustomType("MODEL_M"), CustomType("XYZ")]),
+    ]
+
+    outputs: list[OutputV3] = [
+        ImageOutput("image"),
+        CustomOutput("xyz", "XYZ")
+    ]
+
+    for c in inputs:
+        if isinstance(c, MultitypedInput):
+            print(f"{c}, {type(c)}, {type(c).io_type}, {c.id}, {[x.io_type for x in c.io_types]}")
+            print(c.get_io_type_V1())
+        else:
+            print(f"{c}, {type(c)}, {type(c).io_type}, {c.id}")
+
+    for c in outputs:
+        print(f"{c}, {type(c)}, {type(c).io_type}, {c.id}")
+
+    zz = TestNode()
+    print(zz.GET_NODE_INFO_V1())
+
+    # aa = NodeInfoV1()
+    # print(asdict(aa))
+    # print(as_pruned_dict(aa))

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

@@ -327,7 +327,9 @@ class ApiClient:
             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()

comfy_api_nodes/nodes_bfl.py

@@ -1,6 +1,6 @@
 import io
 from inspect import cleandoc
-from typing import Union
+from typing import Union, Optional
 from comfy.comfy_types.node_typing import IO, ComfyNodeABC
 from comfy_api_nodes.apis.bfl_api import (
     BFLStatus,
@@ -9,6 +9,7 @@ from comfy_api_nodes.apis.bfl_api import (
     BFLFluxCannyImageRequest,
     BFLFluxDepthImageRequest,
     BFLFluxProGenerateRequest,
+    BFLFluxKontextProGenerateRequest,
     BFLFluxProUltraGenerateRequest,
     BFLFluxProGenerateResponse,
 )
@@ -269,6 +270,158 @@ class FluxProUltraImageNode(ComfyNodeABC):
         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):
     """
@@ -914,6 +1067,8 @@ class FluxProDepthNode(ComfyNodeABC):
 NODE_CLASS_MAPPINGS = {
     "FluxProUltraImageNode": FluxProUltraImageNode,
     # "FluxProImageNode": FluxProImageNode,
+    "FluxKontextProImageNode": FluxKontextProImageNode,
+    "FluxKontextMaxImageNode": FluxKontextMaxImageNode,
     "FluxProExpandNode": FluxProExpandNode,
     "FluxProFillNode": FluxProFillNode,
     "FluxProCannyNode": FluxProCannyNode,
@@ -924,6 +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_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")
 
@@ -204,6 +206,7 @@ class PikaImageToVideoV2_2(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -457,7 +460,7 @@ class PikAdditionsNode(PikaNodeBase):
             },
         }
 
-    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,

comfy_extras/nodes_images.py

@@ -14,6 +14,7 @@ import re
 from io import BytesIO
 from inspect import cleandoc
 import torch
+import comfy.utils
 
 from comfy.comfy_types import FileLocator
 
@@ -229,6 +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.
@@ -318,4 +499,5 @@ NODE_CLASS_MAPPINGS = {
     "SaveAnimatedWEBP": SaveAnimatedWEBP,
     "SaveAnimatedPNG": SaveAnimatedPNG,
     "SaveSVGNode": SaveSVGNode,
+    "ImageStitch": ImageStitch,
 }

comfy_extras/nodes_string.py

@@ -296,6 +296,41 @@ class RegexExtract():
 
         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,
@@ -306,7 +341,8 @@ NODE_CLASS_MAPPINGS = {
     "StringContains": StringContains,
     "StringCompare": StringCompare,
     "RegexMatch": RegexMatch,
-    "RegexExtract": RegexExtract
+    "RegexExtract": RegexExtract,
+    "RegexReplace": RegexReplace,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -319,5 +355,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "StringContains": "Contains",
     "StringCompare": "Compare",
     "RegexMatch": "Regex Match",
-    "RegexExtract": "Regex Extract"
+    "RegexExtract": "Regex Extract",
+    "RegexReplace": "Regex Replace",
 }

comfy_extras/nodes_v3_test.py

@@ -0,0 +1,67 @@
+import torch
+
+from comfy_api.v3.io import (
+    ComfyNodeV3, SchemaV3, CustomType, CustomInput, CustomOutput, InputBehavior, NumberDisplay,
+    IntegerInput, MaskInput, ImageInput, ComboDynamicInput, NodeOutput,
+)
+
+
+
+
+
+class V3TestNode(ComfyNodeV3):
+
+
+    @classmethod
+    def DEFINE_SCHEMA(cls):
+        return SchemaV3(
+            node_id="V3TestNode1",
+            display_name="V3 Test Node (1djekjd)",
+            description="This is a funky V3 node test.",
+            category="v3 nodes",
+            inputs=[
+                IntegerInput("some_int", display_name="new_name", min=0, tooltip="My tooltip 😎", display_mode=NumberDisplay.slider),
+                MaskInput("mask", behavior=InputBehavior.optional),
+                ImageInput("image", display_name="new_image"),
+                # IntegerInput("some_int", display_name="new_name", min=0, tooltip="My tooltip 😎", display=NumberDisplay.slider, ),
+                # ComboDynamicInput("mask", behavior=InputBehavior.optional),
+                # IntegerInput("some_int", display_name="new_name", min=0, tooltip="My tooltip 😎", display=NumberDisplay.slider,
+                #              dependent_inputs=[ComboDynamicInput("mask", behavior=InputBehavior.optional)],
+                #              dependent_values=[lambda my_value: IO.STRING if my_value < 5 else IO.NUMBER],
+                #              ),
+                # ["option1", "option2". "option3"]
+                # ComboDynamicInput["sdfgjhl", [ComboDynamicOptions("option1", [IntegerInput("some_int", display_name="new_name", min=0, tooltip="My tooltip 😎", display=NumberDisplay.slider, ImageInput(), MaskInput(), String()]),
+                #                              CombyDynamicOptons("option2", [])
+                #                                                   ]]
+            ],
+            is_output_node=True,
+        )
+
+    def execute(self, some_int: int, image: torch.Tensor, mask: torch.Tensor=None, **kwargs):
+        a  = NodeOutput(1)
+        aa = NodeOutput(1, "hellothere")
+        ab = NodeOutput(1, "hellothere", ui={"lol": "jk"})
+        b  = NodeOutput()
+        c  = NodeOutput(ui={"lol": "jk"})
+        return NodeOutput()
+        return NodeOutput(1)
+        return NodeOutput(1, block_execution="Kill yourself")
+        return ()
+
+
+
+
+
+
+NODES_LIST: list[ComfyNodeV3] = [
+    V3TestNode,
+]
+
+
+# NODE_CLASS_MAPPINGS = {}
+# NODE_DISPLAY_NAME_MAPPINGS = {}
+# for node in NODES_LIST:
+#     schema = node.GET_SCHEMA()
+#     NODE_CLASS_MAPPINGS[schema.node_id] = node
+#     if schema.display_name:
+#         NODE_DISPLAY_NAME_MAPPINGS[schema.node_id] = schema.display_name

comfy_extras/nodes_wan.py

@@ -345,6 +345,44 @@ class WanCameraImageToVideo:
         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,
     "WanFunControlToVideo": WanFunControlToVideo,
@@ -353,4 +391,5 @@ NODE_CLASS_MAPPINGS = {
     "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__ = "0.3.36"
+__version__ = "0.3.39"

execution.py

@@ -17,6 +17,7 @@ from comfy_execution.graph import get_input_info, ExecutionList, DynamicPrompt,
 from comfy_execution.graph_utils import is_link, GraphBuilder
 from comfy_execution.caching import HierarchicalCache, LRUCache, DependencyAwareCache, CacheKeySetInputSignature, CacheKeySetID
 from comfy_execution.validation import validate_node_input
+from comfy_api.v3.io import NodeOutput
 
 class ExecutionResult(Enum):
     SUCCESS = 0
@@ -242,6 +243,22 @@ def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb
                     result = tuple([result] * len(obj.RETURN_TYPES))
                 results.append(result)
                 subgraph_results.append((None, result))
+        elif isinstance(r, NodeOutput):
+            if r.ui is not None:
+                uis.append(r.ui.as_dict())
+            if r.expand is not None:
+                has_subgraph = True
+                new_graph = r.expand
+                result = r.result
+                if r.block_execution is not None:
+                    result = tuple([ExecutionBlocker(r.block_execution)] * len(obj.RETURN_TYPES))
+                subgraph_results.append((new_graph, result))
+            elif r.result is not None:
+                result = r.result
+                if r.block_execution is not None:
+                    result = tuple([ExecutionBlocker(r.block_execution)] * len(obj.RETURN_TYPES))
+                results.append(result)
+                subgraph_results.append((None, result))
         else:
             if isinstance(r, ExecutionBlocker):
                 r = tuple([r] * len(obj.RETURN_TYPES))

nodes.py

@@ -26,6 +26,7 @@ import comfy.sd
 import comfy.utils
 import comfy.controlnet
 from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict, FileLocator
+from comfy_api.v3.io import ComfyNodeV3
 
 import comfy.clip_vision
 
@@ -2061,6 +2062,7 @@ 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",
@@ -2128,6 +2130,7 @@ def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes
             if os.path.isdir(web_dir):
                 EXTENSION_WEB_DIRS[module_name] = web_dir
 
+        # V1 node definition
         if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None:
             for name, node_cls in module.NODE_CLASS_MAPPINGS.items():
                 if name not in ignore:
@@ -2136,8 +2139,19 @@ def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes
             if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None:
                 NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
             return True
+        # V3 node definition
+        elif getattr(module, "NODES_LIST", None) is not None:
+            for node_cls in module.NODES_LIST:
+                node_cls: ComfyNodeV3
+                schema = node_cls.GET_SCHEMA()
+                if schema.node_id not in ignore:
+                    NODE_CLASS_MAPPINGS[schema.node_id] = node_cls
+                    node_cls.RELATIVE_PYTHON_MODULE = "{}.{}".format(module_parent, get_module_name(module_path))
+                if schema.display_name is not None:
+                    NODE_DISPLAY_NAME_MAPPINGS[schema.node_id] = schema.display_name
+            return True
         else:
-            logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.")
+            logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS or NODES_LIST (need one).")
             return False
     except Exception as e:
         logging.warning(traceback.format_exc())
@@ -2257,6 +2271,7 @@ def init_builtin_extra_nodes():
         "nodes_ace.py",
         "nodes_string.py",
         "nodes_camera_trajectory.py",
+        "nodes_v3_test.py",
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.36"
+version = "0.3.39"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,5 +1,6 @@
-comfyui-frontend-package==1.20.6
-comfyui-workflow-templates==0.1.20
+comfyui-frontend-package==1.21.3
+comfyui-workflow-templates==0.1.23
+comfyui-embedded-docs==0.2.0
 torch
 torchsde
 torchvision

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 comfy_api.v3.io import ComfyNodeV3
 
 from app.user_manager import UserManager
 from app.model_manager import ModelFileManager
@@ -555,6 +556,8 @@ class PromptServer():
 
         def node_info(node_class):
             obj_class = nodes.NODE_CLASS_MAPPINGS[node_class]
+            if isinstance(obj_class, ComfyNodeV3):
+                return obj_class.GET_NODE_INFO_V1()
             info = {}
             info['input'] = obj_class.INPUT_TYPES()
             info['input_order'] = {key: list(value.keys()) for (key, value) in obj_class.INPUT_TYPES().items()}
@@ -746,6 +749,13 @@ 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_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
+