Add mochi support to readme.

You can still use models/clip but the folder might get removed eventually
on new installs of ComfyUI.

README.md

@@ -40,6 +40,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
 - Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
 - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
+- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
 - Smart memory management: can automatically run models on GPUs with as low as 1GB vram.

comfy/extra_samplers/uni_pc.py

@@ -16,7 +16,7 @@ class NoiseScheduleVP:
             continuous_beta_0=0.1,
             continuous_beta_1=20.,
         ):
-        """Create a wrapper class for the forward SDE (VP type).
+        r"""Create a wrapper class for the forward SDE (VP type).
 
         ***
         Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.

comfy/ldm/genmo/vae/model.py

@@ -2,12 +2,16 @@
 #adapted to ComfyUI
 
 from typing import Callable, List, Optional, Tuple, Union
+from functools import partial
+import math
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
 
+from comfy.ldm.modules.attention import optimized_attention
+
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
@@ -158,8 +162,10 @@ class ResBlock(nn.Module):
         *,
         affine: bool = True,
         attn_block: Optional[nn.Module] = None,
-        padding_mode: str = "replicate",
         causal: bool = True,
+        prune_bottleneck: bool = False,
+        padding_mode: str,
+        bias: bool = True,
     ):
         super().__init__()
         self.channels = channels
@@ -170,23 +176,23 @@ class ResBlock(nn.Module):
             nn.SiLU(inplace=True),
             PConv3d(
                 in_channels=channels,
-                out_channels=channels,
+                out_channels=channels // 2 if prune_bottleneck else channels,
                 kernel_size=(3, 3, 3),
                 stride=(1, 1, 1),
                 padding_mode=padding_mode,
-                bias=True,
+                bias=bias,
-                # causal=causal,
+                causal=causal,
             ),
             norm_fn(channels, affine=affine),
             nn.SiLU(inplace=True),
             PConv3d(
-                in_channels=channels,
+                in_channels=channels // 2 if prune_bottleneck else channels,
                 out_channels=channels,
                 kernel_size=(3, 3, 3),
                 stride=(1, 1, 1),
                 padding_mode=padding_mode,
-                bias=True,
+                bias=bias,
-                # causal=causal,
+                causal=causal,
             ),
         )
 
@@ -206,6 +212,81 @@ class ResBlock(nn.Module):
         return self.attn_block(x)
 
 
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        head_dim: int = 32,
+        qkv_bias: bool = False,
+        out_bias: bool = True,
+        qk_norm: bool = True,
+    ) -> None:
+        super().__init__()
+        self.head_dim = head_dim
+        self.num_heads = dim // head_dim
+        self.qk_norm = qk_norm
+
+        self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias)
+        self.out = nn.Linear(dim, dim, bias=out_bias)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        """Compute temporal self-attention.
+
+        Args:
+            x: Input tensor. Shape: [B, C, T, H, W].
+            chunk_size: Chunk size for large tensors.
+
+        Returns:
+            x: Output tensor. Shape: [B, C, T, H, W].
+        """
+        B, _, T, H, W = x.shape
+
+        if T == 1:
+            # No attention for single frame.
+            x = x.movedim(1, -1)  # [B, C, T, H, W] -> [B, T, H, W, C]
+            qkv = self.qkv(x)
+            _, _, x = qkv.chunk(3, dim=-1)  # Throw away queries and keys.
+            x = self.out(x)
+            return x.movedim(-1, 1)  # [B, T, H, W, C] -> [B, C, T, H, W]
+
+        # 1D temporal attention.
+        x = rearrange(x, "B C t h w -> (B h w) t C")
+        qkv = self.qkv(x)
+
+        # Input: qkv with shape [B, t, 3 * num_heads * head_dim]
+        # Output: x with shape [B, num_heads, t, head_dim]
+        q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, self.head_dim).transpose(1, 3).unbind(2)
+
+        if self.qk_norm:
+            q = F.normalize(q, p=2, dim=-1)
+            k = F.normalize(k, p=2, dim=-1)
+
+        x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True)
+
+        assert x.size(0) == q.size(0)
+
+        x = self.out(x)
+        x = rearrange(x, "(B h w) t C -> B C t h w", B=B, h=H, w=W)
+        return x
+
+
+class AttentionBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        **attn_kwargs,
+    ) -> None:
+        super().__init__()
+        self.norm = norm_fn(dim)
+        self.attn = Attention(dim, **attn_kwargs)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x + self.attn(self.norm(x))
+
+
 class CausalUpsampleBlock(nn.Module):
     def __init__(
         self,
@@ -244,14 +325,9 @@ class CausalUpsampleBlock(nn.Module):
         return x
 
 
-def block_fn(channels, *, has_attention: bool = False, **block_kwargs):
+def block_fn(channels, *, affine: bool = True, has_attention: bool = False, **block_kwargs):
-    assert has_attention is False #NOTE: if this is ever true add back the attention code.
+    attn_block = AttentionBlock(channels) if has_attention else None
-
+    return ResBlock(channels, affine=affine, attn_block=attn_block, **block_kwargs)
-    attn_block = None #AttentionBlock(channels) if has_attention else None
-
-    return ResBlock(
-        channels, affine=True, attn_block=attn_block, **block_kwargs
-    )
 
 
 class DownsampleBlock(nn.Module):
@@ -288,8 +364,9 @@ class DownsampleBlock(nn.Module):
                 out_channels=out_channels,
                 kernel_size=(temporal_reduction, spatial_reduction, spatial_reduction),
                 stride=(temporal_reduction, spatial_reduction, spatial_reduction),
+                # First layer in each block always uses replicate padding
                 padding_mode="replicate",
-                bias=True,
+                bias=block_kwargs["bias"],
             )
         )
 
@@ -382,7 +459,7 @@ class Decoder(nn.Module):
         blocks = []
 
         first_block = [
-            nn.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1))
+            ops.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1))
         ]  # Input layer.
         # First set of blocks preserve channel count.
         for _ in range(num_res_blocks[-1]):
@@ -452,11 +529,165 @@ class Decoder(nn.Module):
 
         return self.output_proj(x).contiguous()
 
+class LatentDistribution:
+    def __init__(self, mean: torch.Tensor, logvar: torch.Tensor):
+        """Initialize latent distribution.
+
+        Args:
+            mean: Mean of the distribution. Shape: [B, C, T, H, W].
+            logvar: Logarithm of variance of the distribution. Shape: [B, C, T, H, W].
+        """
+        assert mean.shape == logvar.shape
+        self.mean = mean
+        self.logvar = logvar
+
+    def sample(self, temperature=1.0, generator: torch.Generator = None, noise=None):
+        if temperature == 0.0:
+            return self.mean
+
+        if noise is None:
+            noise = torch.randn(self.mean.shape, device=self.mean.device, dtype=self.mean.dtype, generator=generator)
+        else:
+            assert noise.device == self.mean.device
+            noise = noise.to(self.mean.dtype)
+
+        if temperature != 1.0:
+            raise NotImplementedError(f"Temperature {temperature} is not supported.")
+
+        # Just Gaussian sample with no scaling of variance.
+        return noise * torch.exp(self.logvar * 0.5) + self.mean
+
+    def mode(self):
+        return self.mean
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels: int,
+        base_channels: int,
+        channel_multipliers: List[int],
+        num_res_blocks: List[int],
+        latent_dim: int,
+        temporal_reductions: List[int],
+        spatial_reductions: List[int],
+        prune_bottlenecks: List[bool],
+        has_attentions: List[bool],
+        affine: bool = True,
+        bias: bool = True,
+        input_is_conv_1x1: bool = False,
+        padding_mode: str,
+    ):
+        super().__init__()
+        self.temporal_reductions = temporal_reductions
+        self.spatial_reductions = spatial_reductions
+        self.base_channels = base_channels
+        self.channel_multipliers = channel_multipliers
+        self.num_res_blocks = num_res_blocks
+        self.latent_dim = latent_dim
+
+        self.fourier_features = FourierFeatures()
+        ch = [mult * base_channels for mult in channel_multipliers]
+        num_down_blocks = len(ch) - 1
+        assert len(num_res_blocks) == num_down_blocks + 2
+
+        layers = (
+            [ops.Conv3d(in_channels, ch[0], kernel_size=(1, 1, 1), bias=True)]
+            if not input_is_conv_1x1
+            else [Conv1x1(in_channels, ch[0])]
+        )
+
+        assert len(prune_bottlenecks) == num_down_blocks + 2
+        assert len(has_attentions) == num_down_blocks + 2
+        block = partial(block_fn, padding_mode=padding_mode, affine=affine, bias=bias)
+
+        for _ in range(num_res_blocks[0]):
+            layers.append(block(ch[0], has_attention=has_attentions[0], prune_bottleneck=prune_bottlenecks[0]))
+        prune_bottlenecks = prune_bottlenecks[1:]
+        has_attentions = has_attentions[1:]
+
+        assert len(temporal_reductions) == len(spatial_reductions) == len(ch) - 1
+        for i in range(num_down_blocks):
+            layer = DownsampleBlock(
+                ch[i],
+                ch[i + 1],
+                num_res_blocks=num_res_blocks[i + 1],
+                temporal_reduction=temporal_reductions[i],
+                spatial_reduction=spatial_reductions[i],
+                prune_bottleneck=prune_bottlenecks[i],
+                has_attention=has_attentions[i],
+                affine=affine,
+                bias=bias,
+                padding_mode=padding_mode,
+            )
+
+            layers.append(layer)
+
+        # Additional blocks.
+        for _ in range(num_res_blocks[-1]):
+            layers.append(block(ch[-1], has_attention=has_attentions[-1], prune_bottleneck=prune_bottlenecks[-1]))
+
+        self.layers = nn.Sequential(*layers)
+
+        # Output layers.
+        self.output_norm = norm_fn(ch[-1])
+        self.output_proj = Conv1x1(ch[-1], 2 * latent_dim, bias=False)
+
+    @property
+    def temporal_downsample(self):
+        return math.prod(self.temporal_reductions)
+
+    @property
+    def spatial_downsample(self):
+        return math.prod(self.spatial_reductions)
+
+    def forward(self, x) -> LatentDistribution:
+        """Forward pass.
+
+        Args:
+            x: Input video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1]
+
+        Returns:
+            means: Latent tensor. Shape: [B, latent_dim, t, h, w]. Scaled [-1, 1].
+                   h = H // 8, w = W // 8, t - 1 = (T - 1) // 6
+            logvar: Shape: [B, latent_dim, t, h, w].
+        """
+        assert x.ndim == 5, f"Expected 5D input, got {x.shape}"
+        x = self.fourier_features(x)
+
+        x = self.layers(x)
+
+        x = self.output_norm(x)
+        x = F.silu(x, inplace=True)
+        x = self.output_proj(x)
+
+        means, logvar = torch.chunk(x, 2, dim=1)
+
+        assert means.ndim == 5
+        assert logvar.shape == means.shape
+        assert means.size(1) == self.latent_dim
+
+        return LatentDistribution(means, logvar)
+
 
 class VideoVAE(nn.Module):
     def __init__(self):
         super().__init__()
-        self.encoder = None #TODO once the model releases
+        self.encoder = Encoder(
+            in_channels=15,
+            base_channels=64,
+            channel_multipliers=[1, 2, 4, 6],
+            num_res_blocks=[3, 3, 4, 6, 3],
+            latent_dim=12,
+            temporal_reductions=[1, 2, 3],
+            spatial_reductions=[2, 2, 2],
+            prune_bottlenecks=[False, False, False, False, False],
+            has_attentions=[False, True, True, True, True],
+            affine=True,
+            bias=True,
+            input_is_conv_1x1=True,
+            padding_mode="replicate"
+        )
         self.decoder = Decoder(
             out_channels=3,
             base_channels=128,
@@ -474,7 +705,7 @@ class VideoVAE(nn.Module):
         )
 
     def encode(self, x):
-        return self.encoder(x)
+        return self.encoder(x).mode()
 
     def decode(self, x):
         return self.decoder(x)

comfy/ldm/modules/attention.py

@@ -393,6 +393,13 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
 
     return out
 
+if model_management.is_nvidia(): #pytorch 2.3 and up seem to have this issue.
+    SDP_BATCH_LIMIT = 2**15
+else:
+    #TODO: other GPUs ?
+    SDP_BATCH_LIMIT = 2**31
+
+
 def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
@@ -404,10 +411,15 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
             (q, k, v),
         )
 
-    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+    if SDP_BATCH_LIMIT >= q.shape[0]:
-    out = (
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
-        out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+        out = (
-    )
+            out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+        )
+    else:
+        out = torch.empty((q.shape[0], q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device)
+        for i in range(0, q.shape[0], SDP_BATCH_LIMIT):
+            out[i : i + SDP_BATCH_LIMIT] = torch.nn.functional.scaled_dot_product_attention(q[i : i + SDP_BATCH_LIMIT], k[i : i + SDP_BATCH_LIMIT], v[i : i + SDP_BATCH_LIMIT], attn_mask=mask, dropout_p=0.0, is_causal=False).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
     return out
 
 

comfy/model_detection.py

@@ -540,7 +540,11 @@ def model_config_from_diffusers_unet(state_dict):
 def convert_diffusers_mmdit(state_dict, output_prefix=""):
     out_sd = {}
 
-    if 'transformer_blocks.0.attn.norm_added_k.weight' in state_dict: #Flux
+    if 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow
+        num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
+        num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
+        sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
+    elif 'x_embedder.weight' in state_dict: #Flux
         depth = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')
         hidden_size = state_dict["x_embedder.bias"].shape[0]
@@ -549,10 +553,6 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
         num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth = state_dict["pos_embed.proj.weight"].shape[0] // 64
         sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix)
-    elif 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow
-        num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
-        num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
-        sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
     else:
         return None
 

comfy/model_management.py

@@ -896,7 +896,7 @@ def force_upcast_attention_dtype():
     upcast = args.force_upcast_attention
     try:
         macos_version = tuple(int(n) for n in platform.mac_ver()[0].split("."))
-        if (14, 5) <= macos_version <= (15, 0, 1):  # black image bug on recent versions of macOS
+        if (14, 5) <= macos_version <= (15, 2):  # black image bug on recent versions of macOS
             upcast = True
     except:
         pass

comfy/sd.py

@@ -171,6 +171,7 @@ class VAE:
         self.downscale_ratio = 8
         self.upscale_ratio = 8
         self.latent_channels = 4
+        self.latent_dim = 2
         self.output_channels = 3
         self.process_input = lambda image: image * 2.0 - 1.0
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
@@ -240,16 +241,22 @@ class VAE:
                 self.output_channels = 2
                 self.upscale_ratio = 2048
                 self.downscale_ratio =  2048
+                self.latent_dim = 1
                 self.process_output = lambda audio: audio
                 self.process_input = lambda audio: audio
                 self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
-            elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd: #genmo mochi vae
+            elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight": #genmo mochi vae
                 if "blocks.2.blocks.3.stack.5.weight" in sd:
                     sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."})
+                if "layers.4.layers.1.attn_block.attn.qkv.weight" in sd:
+                    sd = comfy.utils.state_dict_prefix_replace(sd, {"": "encoder."})
                 self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE()
                 self.latent_channels = 12
+                self.latent_dim = 3
                 self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8)
+                self.working_dtypes = [torch.float16, torch.float32]
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -361,17 +368,22 @@ class VAE:
 
     def encode(self, pixel_samples):
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
-        pixel_samples = pixel_samples.movedim(-1,1)
+        pixel_samples = pixel_samples.movedim(-1, 1)
+        if self.latent_dim == 3:
+            pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
         try:
             memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
             model_management.load_models_gpu([self.patcher], memory_required=memory_used)
             free_memory = model_management.get_free_memory(self.device)
             batch_number = int(free_memory / max(1, memory_used))
             batch_number = max(1, batch_number)
-            samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a // self.downscale_ratio, pixel_samples.shape[2:])), device=self.output_device)
+            samples = None
             for x in range(0, pixel_samples.shape[0], batch_number):
-                pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device)
+                pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device)
-                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
+                out = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
+                if samples is None:
+                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+                samples[x:x + batch_number] = out
 
         except model_management.OOM_EXCEPTION as e:
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")

comfy/text_encoders/genmo.py

@@ -12,7 +12,7 @@ class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
 
 class MochiT5XXL(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}):
-        super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
+        super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
 
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):

comfy/utils.py

@@ -316,10 +316,18 @@ MMDIT_MAP_BLOCK = {
     ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"),
     ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"),
     ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"),
+    ("context_block.attn.ln_q.weight", "attn.norm_added_q.weight"),
+    ("context_block.attn.ln_k.weight", "attn.norm_added_k.weight"),
     ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"),
     ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"),
     ("x_block.attn.proj.bias", "attn.to_out.0.bias"),
     ("x_block.attn.proj.weight", "attn.to_out.0.weight"),
+    ("x_block.attn.ln_q.weight", "attn.norm_q.weight"),
+    ("x_block.attn.ln_k.weight", "attn.norm_k.weight"),
+    ("x_block.attn2.proj.bias", "attn2.to_out.0.bias"),
+    ("x_block.attn2.proj.weight", "attn2.to_out.0.weight"),
+    ("x_block.attn2.ln_q.weight", "attn2.norm_q.weight"),
+    ("x_block.attn2.ln_k.weight", "attn2.norm_k.weight"),
     ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"),
     ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"),
     ("x_block.mlp.fc2.bias", "ff.net.2.bias"),
@@ -349,6 +357,12 @@ def mmdit_to_diffusers(mmdit_config, output_prefix=""):
             key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset))
             key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
 
+            k = "{}.attn2.".format(block_from)
+            qkv = "{}.x_block.attn2.qkv.{}".format(block_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
         for k in MMDIT_MAP_BLOCK:
             key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
 

comfy_extras/nodes_sd3.py

@@ -3,11 +3,11 @@ import comfy.sd
 import comfy.model_management
 import nodes
 import torch
-
+import re
 class TripleCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "clip_name3": (folder_paths.get_filename_list("clip"), )
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), "clip_name3": (folder_paths.get_filename_list("text_encoders"), )
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -15,9 +15,9 @@ class TripleCLIPLoader:
     CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name1, clip_name2, clip_name3):
-        clip_path1 = folder_paths.get_full_path_or_raise("clip", clip_name1)
+        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
-        clip_path2 = folder_paths.get_full_path_or_raise("clip", clip_name2)
+        clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
-        clip_path3 = folder_paths.get_full_path_or_raise("clip", clip_name3)
+        clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return (clip,)
 
@@ -95,11 +95,70 @@ class ControlNetApplySD3(nodes.ControlNetApplyAdvanced):
     CATEGORY = "conditioning/controlnet"
     DEPRECATED = True
 
+class SkipLayerGuidanceSD3:
+    '''
+    Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers.
+    Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377)
+    Experimental implementation by Dango233@StabilityAI.
+    '''
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL", ),
+                             "layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
+                             "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}),
+                             "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001})
+                                }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "skip_guidance"
+
+    CATEGORY = "advanced/guidance"
+
+
+    def skip_guidance(self, model, layers, scale, start_percent, end_percent):
+        if layers == "" or layers == None:
+            return (model, )
+        # check if layer is comma separated integers
+        def skip(args, extra_args):
+            return args
+
+        model_sampling = model.get_model_object("model_sampling")
+        sigma_start = model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model_sampling.percent_to_sigma(end_percent)
+
+        def post_cfg_function(args):
+            model = args["model"]
+            cond_pred = args["cond_denoised"]
+            cond = args["cond"]
+            cfg_result = args["denoised"]
+            sigma = args["sigma"]
+            x = args["input"]
+            model_options = args["model_options"].copy()
+        
+            for layer in layers:
+                model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer)
+            model_sampling.percent_to_sigma(start_percent)
+
+            sigma_ = sigma[0].item()
+            if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start:
+                (slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
+                cfg_result = cfg_result + (cond_pred - slg) * scale
+            return cfg_result
+
+        layers = re.findall(r'\d+', layers)
+        layers = [int(i) for i in layers]
+        m = model.clone()
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
+
+        return (m, )
+
+
 NODE_CLASS_MAPPINGS = {
     "TripleCLIPLoader": TripleCLIPLoader,
     "EmptySD3LatentImage": EmptySD3LatentImage,
     "CLIPTextEncodeSD3": CLIPTextEncodeSD3,
     "ControlNetApplySD3": ControlNetApplySD3,
+    "SkipLayerGuidanceSD3": SkipLayerGuidanceSD3,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {

folder_paths.py

@@ -18,7 +18,7 @@ folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".y
 
 folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions)
 folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions)
-folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions)
+folder_names_and_paths["text_encoders"] = ([os.path.join(models_dir, "text_encoders"), os.path.join(models_dir, "clip")], supported_pt_extensions)
 folder_names_and_paths["diffusion_models"] = ([os.path.join(models_dir, "unet"), os.path.join(models_dir, "diffusion_models")], supported_pt_extensions)
 folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions)
 folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions)
@@ -81,7 +81,8 @@ extension_mimetypes_cache = {
 }
 
 def map_legacy(folder_name: str) -> str:
-    legacy = {"unet": "diffusion_models"}
+    legacy = {"unet": "diffusion_models",
+              "clip": "text_encoders"}
     return legacy.get(folder_name, folder_name)
 
 if not os.path.exists(input_directory):

nodes.py

@@ -888,7 +888,7 @@ class UNETLoader:
 class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
                               "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
@@ -908,15 +908,15 @@ class CLIPLoader:
         else:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
-        clip_path = folder_paths.get_full_path_or_raise("clip", clip_name)
+        clip_path = folder_paths.get_full_path_or_raise("text_encoders", clip_name)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
         return (clip,)
 
 class DualCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
-                              "clip_name2": (folder_paths.get_filename_list("clip"), ),
+                              "clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
                               "type": (["sdxl", "sd3", "flux"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
@@ -925,8 +925,8 @@ class DualCLIPLoader:
     CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name1, clip_name2, type):
-        clip_path1 = folder_paths.get_full_path_or_raise("clip", clip_name1)
+        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
-        clip_path2 = folder_paths.get_full_path_or_raise("clip", clip_name2)
+        clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
         if type == "sdxl":
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
         elif type == "sd3":
@@ -1957,6 +1957,12 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageInvert": "Invert Image",
     "ImagePadForOutpaint": "Pad Image for Outpainting",
     "ImageBatch": "Batch Images",
+    "ImageCrop": "Image Crop",
+    "ImageBlend": "Image Blend",
+    "ImageBlur": "Image Blur",
+    "ImageQuantize": "Image Quantize",
+    "ImageSharpen": "Image Sharpen",
+    "ImageScaleToTotalPixels": "Scale Image to Total Pixels",
     # _for_testing
     "VAEDecodeTiled": "VAE Decode (Tiled)",
     "VAEEncodeTiled": "VAE Encode (Tiled)",

web/assets/ExtensionPanel-BmKi_NKS.js

@@ -1,8 +1,8 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, bK as useExtensionStore, u as useSettingStore, r as ref, o as onMounted, q as computed, g as openBlock, h as createElementBlock, i as createVNode, y as withCtx, z as unref, bL as script$1, A as createBaseVNode, x as createBlock, M as Fragment, N as renderList, am as toDisplayString, ap as createTextVNode, j as createCommentVNode, D as script$4 } from "./index-CgU1oKZt.js";
+import { d as defineComponent, bQ as useExtensionStore, u as useSettingStore, r as ref, o as onMounted, q as computed, g as openBlock, h as createElementBlock, i as createVNode, y as withCtx, z as unref, bR as script$1, A as createBaseVNode, x as createBlock, N as Fragment, O as renderList, a6 as toDisplayString, aw as createTextVNode, j as createCommentVNode, D as script$4 } from "./index-BHayQCxv.js";
-import { s as script, a as script$2, b as script$3 } from "./index-DBWDcZsl.js";
+import { s as script, a as script$2, b as script$3 } from "./index-CwRXxFdA.js";
-import "./index-DYEEBf64.js";
+import "./index-C_wOqB0f.js";
 const _hoisted_1 = { class: "extension-panel" };
 const _hoisted_2 = { class: "mt-4" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
@@ -100,4 +100,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=ExtensionPanel-DZLYjWBj.js.map
+//# sourceMappingURL=ExtensionPanel-BmKi_NKS.js.map

web/assets/ExtensionPanel-BmKi_NKS.js.map

@@ -1 +1 @@
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+{"version":3,"file":"ExtensionPanel-BmKi_NKS.js","sources":["../../src/components/dialog/content/setting/ExtensionPanel.vue"],"sourcesContent":["<template>\n  <div class=\"extension-panel\">\n    <DataTable :value=\"extensionStore.extensions\" stripedRows size=\"small\">\n      <Column field=\"name\" :header=\"$t('extensionName')\" sortable></Column>\n      <Column\n        :pt=\"{\n          bodyCell: 'flex items-center justify-end'\n        }\"\n      >\n        <template #body=\"slotProps\">\n          <ToggleSwitch\n            v-model=\"editingEnabledExtensions[slotProps.data.name]\"\n            @change=\"updateExtensionStatus\"\n          />\n        </template>\n      </Column>\n    </DataTable>\n    <div class=\"mt-4\">\n      <Message v-if=\"hasChanges\" severity=\"info\">\n        <ul>\n          <li v-for=\"ext in changedExtensions\" :key=\"ext.name\">\n            <span>\n              {{ extensionStore.isExtensionEnabled(ext.name) ? '[-]' : '[+]' }}\n            </span>\n            {{ ext.name }}\n          </li>\n        </ul>\n      </Message>\n      <Button\n        :label=\"$t('reloadToApplyChanges')\"\n        icon=\"pi pi-refresh\"\n        @click=\"applyChanges\"\n        :disabled=\"!hasChanges\"\n        text\n        fluid\n        severity=\"danger\"\n      />\n    </div>\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport { ref, computed, onMounted } from 'vue'\nimport { useExtensionStore } from '@/stores/extensionStore'\nimport { useSettingStore } from '@/stores/settingStore'\nimport DataTable from 'primevue/datatable'\nimport Column from 'primevue/column'\nimport ToggleSwitch from 'primevue/toggleswitch'\nimport Button from 'primevue/button'\nimport Message from 'primevue/message'\n\nconst extensionStore = useExtensionStore()\nconst settingStore = useSettingStore()\n\nconst editingEnabledExtensions = ref<Record<string, boolean>>({})\n\nonMounted(() => {\n  extensionStore.extensions.forEach((ext) => {\n    editingEnabledExtensions.value[ext.name] =\n      extensionStore.isExtensionEnabled(ext.name)\n  })\n})\n\nconst changedExtensions = computed(() => {\n  return extensionStore.extensions.filter(\n    (ext) =>\n      editingEnabledExtensions.value[ext.name] !==\n      extensionStore.isExtensionEnabled(ext.name)\n  )\n})\n\nconst hasChanges = computed(() => {\n  return changedExtensions.value.length > 0\n})\n\nconst updateExtensionStatus = () => {\n  const editingDisabledExtensionNames = Object.entries(\n    editingEnabledExtensions.value\n  )\n    .filter(([_, enabled]) => !enabled)\n    .map(([name]) => name)\n\n  settingStore.set('Comfy.Extension.Disabled', [\n    ...extensionStore.inactiveDisabledExtensionNames,\n    ...editingDisabledExtensionNames\n  ])\n}\n\nconst applyChanges = () => {\n  // Refresh the page to apply changes\n  window.location.reload()\n}\n</script>\n"],"names":[],"mappings":";;;;;;;;;;AAmDA,UAAM,iBAAiB;AACvB,UAAM,eAAe;AAEf,UAAA,2BAA2B,IAA6B,CAAA,CAAE;AAEhE,cAAU,MAAM;AACC,qBAAA,WAAW,QAAQ,CAAC,QAAQ;AACzC,iCAAyB,MAAM,IAAI,IAAI,IACrC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA,CAC7C;AAAA,IAAA,CACF;AAEK,UAAA,oBAAoB,SAAS,MAAM;AACvC,aAAO,eAAe,WAAW;AAAA,QAC/B,CAAC,QACC,yBAAyB,MAAM,IAAI,IAAI,MACvC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA;AAAA,IAC9C,CACD;AAEK,UAAA,aAAa,SAAS,MAAM;AACzB,aAAA,kBAAkB,MAAM,SAAS;AAAA,IAAA,CACzC;AAED,UAAM,wBAAwB,6BAAM;AAClC,YAAM,gCAAgC,OAAO;AAAA,QAC3C,yBAAyB;AAAA,MAExB,EAAA,OAAO,CAAC,CAAC,GAAG,OAAO,MAAM,CAAC,OAAO,EACjC,IAAI,CAAC,CAAC,IAAI,MAAM,IAAI;AAEvB,mBAAa,IAAI,4BAA4B;AAAA,QAC3C,GAAG,eAAe;AAAA,QAClB,GAAG;AAAA,MAAA,CACJ;AAAA,IAAA,GAV2B;AAa9B,UAAM,eAAe,6BAAM;AAEzB,aAAO,SAAS;IAAO,GAFJ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/GraphView-Cf7ubG48.css

@@ -1,13 +1,13 @@
 
-.group-title-editor.node-title-editor[data-v-fc3f26e3] {
+.group-title-editor.node-title-editor[data-v-8a100d5a] {
   z-index: 9999;
   padding: 0.25rem;
 }
-[data-v-fc3f26e3] .editable-text {
+[data-v-8a100d5a] .editable-text {
   width: 100%;
   height: 100%;
 }
-[data-v-fc3f26e3] .editable-text input {
+[data-v-8a100d5a] .editable-text input {
   width: 100%;
   height: 100%;
   /* Override the default font size */
@@ -45,7 +45,7 @@
   --sidebar-icon-size: 1rem;
 }
 
-.side-tool-bar-container[data-v-b6bfc188] {
+.side-tool-bar-container[data-v-37fd2fa4] {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -58,36 +58,39 @@
   background-color: var(--comfy-menu-bg);
   color: var(--fg-color);
 }
-.side-tool-bar-end[data-v-b6bfc188] {
+.side-tool-bar-end[data-v-37fd2fa4] {
   align-self: flex-end;
   margin-top: auto;
 }
 
-.p-splitter-gutter {
+[data-v-b49f20b1] .p-splitter-gutter {
   pointer-events: auto;
 }
-.gutter-hidden {
+.side-bar-panel[data-v-b49f20b1] {
-  display: none !important;
-}
-
-.side-bar-panel[data-v-b9df3042] {
   background-color: var(--bg-color);
   pointer-events: auto;
 }
-.splitter-overlay[data-v-b9df3042] {
+.bottom-panel[data-v-b49f20b1] {
-  width: 100%;
+  background-color: var(--bg-color);
-  height: 100%;
+  pointer-events: auto;
-  position: absolute;
+}
-  top: 0;
+.splitter-overlay[data-v-b49f20b1] {
-  left: 0;
-  background-color: transparent;
   pointer-events: none;
+  border-style: none;
+  background-color: transparent;
+}
+.splitter-overlay-root[data-v-b49f20b1] {
+  position: absolute;
+  top: 0px;
+  left: 0px;
+  height: 100%;
+  width: 100%;
+
   /* Set it the same as the ComfyUI menu */
   /* Note: Lite-graph DOM widgets have the same z-index as the node id, so
   999 should be sufficient to make sure splitter overlays on node's DOM
   widgets */
   z-index: 999;
-  border: none;
 }
 
 [data-v-37f672ab] .highlight {
@@ -251,7 +254,7 @@
   display: none;
 }
 
-.comfyui-menu[data-v-b13fdc92] {
+.comfyui-menu[data-v-ad2c662b] {
   width: 100vw;
   background: var(--comfy-menu-bg);
   color: var(--fg-color);
@@ -263,13 +266,13 @@
   grid-column: 1/-1;
   max-height: 90vh;
 }
-.comfyui-menu.dropzone[data-v-b13fdc92] {
+.comfyui-menu.dropzone[data-v-ad2c662b] {
   background: var(--p-highlight-background);
 }
-.comfyui-menu.dropzone-active[data-v-b13fdc92] {
+.comfyui-menu.dropzone-active[data-v-ad2c662b] {
   background: var(--p-highlight-background-focus);
 }
-.comfyui-logo[data-v-b13fdc92] {
+.comfyui-logo[data-v-ad2c662b] {
   font-size: 1.2em;
   -webkit-user-select: none;
      -moz-user-select: none;

web/assets/KeybindingPanel-Dm_3sBT5.js

@@ -1,8 +1,8 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, q as computed, g as openBlock, h as createElementBlock, M as Fragment, N as renderList, i as createVNode, y as withCtx, ap as createTextVNode, am as toDisplayString, z as unref, at as script, j as createCommentVNode, r as ref, bH as FilterMatchMode, K as useKeybindingStore, F as useCommandStore, aC as watchEffect, aZ as useToast, t as resolveDirective, bI as SearchBox, A as createBaseVNode, D as script$2, x as createBlock, af as script$4, b2 as withModifiers, aA as script$6, v as withDirectives, P as pushScopeId, Q as popScopeId, by as KeyComboImpl, bJ as KeybindingImpl, _ as _export_sfc } from "./index-CgU1oKZt.js";
+import { d as defineComponent, q as computed, g as openBlock, h as createElementBlock, N as Fragment, O as renderList, i as createVNode, y as withCtx, aw as createTextVNode, a6 as toDisplayString, z as unref, aA as script, j as createCommentVNode, r as ref, bN as FilterMatchMode, M as useKeybindingStore, F as useCommandStore, aJ as watchEffect, b9 as useToast, t as resolveDirective, bO as SearchBox, A as createBaseVNode, D as script$2, x as createBlock, ao as script$4, be as withModifiers, aH as script$6, v as withDirectives, P as pushScopeId, Q as popScopeId, bJ as KeyComboImpl, bP as KeybindingImpl, _ as _export_sfc } from "./index-BHayQCxv.js";
-import { s as script$1, a as script$3, b as script$5 } from "./index-DBWDcZsl.js";
+import { s as script$1, a as script$3, b as script$5 } from "./index-CwRXxFdA.js";
-import "./index-DYEEBf64.js";
+import "./index-C_wOqB0f.js";
 const _hoisted_1$1 = {
   key: 0,
   class: "px-2"
@@ -261,4 +261,4 @@ const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "d
 export {
   KeybindingPanel as default
 };
-//# sourceMappingURL=KeybindingPanel-YkUFoiMw.js.map
+//# sourceMappingURL=KeybindingPanel-Dm_3sBT5.js.map

web/assets/index-BReiUkk9.js

@@ -1,7 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { bu as ComfyDialog, bv as $el, bw as ComfyApp, c as app, k as LiteGraph, aP as LGraphCanvas, bx as DraggableList, a_ as useToastStore, ax as useNodeDefStore, bq as api, L as LGraphGroup, by as KeyComboImpl, K as useKeybindingStore, F as useCommandStore, e as LGraphNode, bz as ComfyWidgets, bA as applyTextReplacements, av as NodeSourceType, bB as NodeBadgeMode, u as useSettingStore, q as computed, bC as getColorPalette, w as watch, bD as BadgePosition, aR as LGraphBadge, bE as _, bF as defaultColorPalette } from "./index-CgU1oKZt.js";
+import { bF as ComfyDialog, bG as $el, bH as ComfyApp, c as app, k as LiteGraph, b0 as LGraphCanvas, bI as DraggableList, ba as useToastStore, aE as useNodeDefStore, bC as api, L as LGraphGroup, bJ as KeyComboImpl, M as useKeybindingStore, F as useCommandStore, e as LGraphNode, bK as ComfyWidgets, bL as applyTextReplacements } from "./index-BHayQCxv.js";
-import { mergeIfValid, getWidgetConfig, setWidgetConfig } from "./widgetInputs-DNVvusS1.js";
+import { mergeIfValid, getWidgetConfig, setWidgetConfig } from "./widgetInputs-DdecKYqd.js";
 class ClipspaceDialog extends ComfyDialog {
   static {
     __name(this, "ClipspaceDialog");
@@ -37,7 +37,9 @@ class ClipspaceDialog extends ComfyDialog {
         ...self.createButtons()
       ]);
       if (self.element) {
-        self.element.removeChild(self.element.firstChild);
+        if (self.element.firstChild) {
+          self.element.removeChild(self.element.firstChild);
+        }
         self.element.appendChild(children);
       } else {
         self.element = $el("div.comfy-modal", { parent: document.body }, [
@@ -76,7 +78,7 @@ class ClipspaceDialog extends ComfyDialog {
     return buttons;
   }
   createImgSettings() {
-    if (ComfyApp.clipspace.imgs) {
+    if (ComfyApp.clipspace?.imgs) {
       const combo_items = [];
       const imgs = ComfyApp.clipspace.imgs;
       for (let i = 0; i < imgs.length; i++) {
@@ -87,8 +89,10 @@ class ClipspaceDialog extends ComfyDialog {
         {
           id: "clipspace_img_selector",
           onchange: /* @__PURE__ */ __name((event) => {
-            ComfyApp.clipspace["selectedIndex"] = event.target.selectedIndex;
+            if (event.target && ComfyApp.clipspace) {
-            ClipspaceDialog.invalidatePreview();
+              ComfyApp.clipspace["selectedIndex"] = event.target.selectedIndex;
+              ClipspaceDialog.invalidatePreview();
+            }
           }, "onchange")
         },
         combo_items
@@ -102,7 +106,9 @@ class ClipspaceDialog extends ComfyDialog {
         {
           id: "clipspace_img_paste_mode",
           onchange: /* @__PURE__ */ __name((event) => {
-            ComfyApp.clipspace["img_paste_mode"] = event.target.value;
+            if (event.target && ComfyApp.clipspace) {
+              ComfyApp.clipspace["img_paste_mode"] = event.target.value;
+            }
           }, "onchange")
         },
         [
@@ -127,7 +133,7 @@ class ClipspaceDialog extends ComfyDialog {
     }
   }
   createImgPreview() {
-    if (ComfyApp.clipspace.imgs) {
+    if (ComfyApp.clipspace?.imgs) {
       return $el("img", { id: "clipspace_preview", ondragstart: /* @__PURE__ */ __name(() => false, "ondragstart") });
     } else return [];
   }
@@ -173,9 +179,9 @@ const ext$2 = {
         requestAnimationFrame(() => {
           const currentNode = LGraphCanvas.active_canvas.current_node;
           const clickedComboValue = currentNode.widgets?.filter(
-            (w) => w.type === "combo" && w.options.values.length === values.length
+            (w) => w.type === "combo" && w.options.values?.length === values.length
           ).find(
-            (w) => w.options.values.every((v, i) => v === values[i])
+            (w) => w.options.values?.every((v, i) => v === values[i])
           )?.value;
           let selectedIndex = clickedComboValue ? values.findIndex((v) => v === clickedComboValue) : 0;
           if (selectedIndex < 0) {
@@ -244,7 +250,7 @@ const ext$2 = {
           filter.addEventListener("input", () => {
             const term = filter.value.toLocaleLowerCase();
             displayedItems = items.filter((item) => {
-              const isVisible = !term || item.textContent.toLocaleLowerCase().includes(term);
+              const isVisible = !term || item.textContent?.toLocaleLowerCase().includes(term);
               item.style.display = isVisible ? "block" : "none";
               return isVisible;
             });
@@ -1224,7 +1230,7 @@ class GroupNodeConfig {
   checkPrimitiveConnection(link, inputName, inputs) {
     const sourceNode = this.nodeData.nodes[link[0]];
     if (sourceNode.type === "PrimitiveNode") {
-      const [sourceNodeId, _2, targetNodeId, __] = link;
+      const [sourceNodeId, _, targetNodeId, __] = link;
       const primitiveDef = this.primitiveDefs[sourceNodeId];
       const targetWidget = inputs[inputName];
       const primitiveConfig = primitiveDef.input.required.value;
@@ -1619,7 +1625,7 @@ class GroupNodeHandler {
       return newNodes;
     };
     const getExtraMenuOptions = this.node.getExtraMenuOptions;
-    this.node.getExtraMenuOptions = function(_2, options) {
+    this.node.getExtraMenuOptions = function(_, options) {
       getExtraMenuOptions?.apply(this, arguments);
       let optionIndex = options.findIndex((o) => o.content === "Outputs");
       if (optionIndex === -1) optionIndex = options.length;
@@ -1793,7 +1799,7 @@ class GroupNodeHandler {
       } else if (innerNode.type === "Reroute") {
         const rerouteLinks = this.groupData.linksFrom[old.node.index];
         if (rerouteLinks) {
-          for (const [_2, , targetNodeId, targetSlot] of rerouteLinks["0"]) {
+          for (const [_, , targetNodeId, targetSlot] of rerouteLinks["0"]) {
             const node = this.innerNodes[targetNodeId];
             const input = node.inputs[targetSlot];
             if (input.widget) {
@@ -2408,10 +2414,10 @@ const ext = {
       defaultValue: 2,
       type: "combo",
       options: [
-        { value: LiteGraph.STRAIGHT_LINK, text: "Straight" },
+        { value: LiteGraph.STRAIGHT_LINK.toString(), text: "Straight" },
-        { value: LiteGraph.LINEAR_LINK, text: "Linear" },
+        { value: LiteGraph.LINEAR_LINK.toString(), text: "Linear" },
-        { value: LiteGraph.SPLINE_LINK, text: "Spline" },
+        { value: LiteGraph.SPLINE_LINK.toString(), text: "Spline" },
-        { value: LiteGraph.HIDDEN_LINK, text: "Hidden" }
+        { value: LiteGraph.HIDDEN_LINK.toString(), text: "Hidden" }
       ],
       onChange(value) {
         app2.canvas.links_render_mode = +value;
@@ -3934,7 +3940,7 @@ app.registerExtension({
         };
         this.isVirtualNode = true;
       }
-      getExtraMenuOptions(_2, options) {
+      getExtraMenuOptions(_, options) {
         options.unshift(
           {
             content: (this.properties.showOutputText ? "Hide" : "Show") + " Type",
@@ -4157,7 +4163,7 @@ app.registerExtension({
   slot_types_default_in: {},
   async beforeRegisterNodeDef(nodeType, nodeData, app2) {
     var nodeId = nodeData.name;
-    const inputs = nodeData["input"]["required"];
+    const inputs = nodeData["input"]?.["required"];
     for (const inputKey in inputs) {
       var input = inputs[inputKey];
       if (typeof input[0] !== "string") continue;
@@ -4179,19 +4185,19 @@ app.registerExtension({
         nodeType.comfyClass
       );
     }
-    var outputs = nodeData["output"];
+    var outputs = nodeData["output"] ?? [];
-    for (const key in outputs) {
+    for (const el of outputs) {
-      var type = outputs[key];
+      const type2 = el;
-      if (!(type in this.slot_types_default_in)) {
+      if (!(type2 in this.slot_types_default_in)) {
-        this.slot_types_default_in[type] = ["Reroute"];
+        this.slot_types_default_in[type2] = ["Reroute"];
       }
-      this.slot_types_default_in[type].push(nodeId);
+      this.slot_types_default_in[type2].push(nodeId);
-      if (!(type in LiteGraph.registered_slot_out_types)) {
+      if (!(type2 in LiteGraph.registered_slot_out_types)) {
-        LiteGraph.registered_slot_out_types[type] = { nodes: [] };
+        LiteGraph.registered_slot_out_types[type2] = { nodes: [] };
       }
-      LiteGraph.registered_slot_out_types[type].nodes.push(nodeType.comfyClass);
+      LiteGraph.registered_slot_out_types[type2].nodes.push(nodeType.comfyClass);
-      if (!LiteGraph.slot_types_out.includes(type)) {
+      if (!LiteGraph.slot_types_out.includes(type2)) {
-        LiteGraph.slot_types_out.push(type);
+        LiteGraph.slot_types_out.push(type2);
       }
     }
     var maxNum = this.suggestionsNumber.value;
@@ -4276,7 +4282,7 @@ app.registerExtension({
         } else {
           w = node.size[0];
           h = node.size[1];
-          let titleMode = node.constructor.title_mode;
+          const titleMode = node.constructor.title_mode;
           if (titleMode !== LiteGraph.TRANSPARENT_TITLE && titleMode !== LiteGraph.NO_TITLE) {
             h += LiteGraph.NODE_TITLE_HEIGHT;
             shiftY -= LiteGraph.NODE_TITLE_HEIGHT;
@@ -4627,108 +4633,4 @@ app.registerExtension({
     };
   }
 });
-function getNodeSource(node) {
+//# sourceMappingURL=index-BReiUkk9.js.map
-  const nodeDef = node.constructor.nodeData;
-  if (!nodeDef) {
-    return null;
-  }
-  const nodeDefStore = useNodeDefStore();
-  return nodeDefStore.nodeDefsByName[nodeDef.name]?.nodeSource ?? null;
-}
-__name(getNodeSource, "getNodeSource");
-function isCoreNode(node) {
-  return getNodeSource(node)?.type === NodeSourceType.Core;
-}
-__name(isCoreNode, "isCoreNode");
-function badgeTextVisible(node, badgeMode) {
-  return badgeMode === NodeBadgeMode.None || isCoreNode(node) && badgeMode === NodeBadgeMode.HideBuiltIn;
-}
-__name(badgeTextVisible, "badgeTextVisible");
-function getNodeIdBadgeText(node, nodeIdBadgeMode) {
-  return badgeTextVisible(node, nodeIdBadgeMode) ? "" : `#${node.id}`;
-}
-__name(getNodeIdBadgeText, "getNodeIdBadgeText");
-function getNodeSourceBadgeText(node, nodeSourceBadgeMode) {
-  const nodeSource = getNodeSource(node);
-  return badgeTextVisible(node, nodeSourceBadgeMode) ? "" : nodeSource?.badgeText ?? "";
-}
-__name(getNodeSourceBadgeText, "getNodeSourceBadgeText");
-function getNodeLifeCycleBadgeText(node, nodeLifeCycleBadgeMode) {
-  let text = "";
-  const nodeDef = node.constructor.nodeData;
-  if (!nodeDef) {
-    return "";
-  }
-  if (nodeDef.deprecated) {
-    text = "[DEPR]";
-  }
-  if (nodeDef.experimental) {
-    text = "[BETA]";
-  }
-  return badgeTextVisible(node, nodeLifeCycleBadgeMode) ? "" : text;
-}
-__name(getNodeLifeCycleBadgeText, "getNodeLifeCycleBadgeText");
-class NodeBadgeExtension {
-  static {
-    __name(this, "NodeBadgeExtension");
-  }
-  constructor(nodeIdBadgeMode = null, nodeSourceBadgeMode = null, nodeLifeCycleBadgeMode = null, colorPalette = null) {
-    this.nodeIdBadgeMode = nodeIdBadgeMode;
-    this.nodeSourceBadgeMode = nodeSourceBadgeMode;
-    this.nodeLifeCycleBadgeMode = nodeLifeCycleBadgeMode;
-    this.colorPalette = colorPalette;
-  }
-  name = "Comfy.NodeBadge";
-  init(app2) {
-    const settingStore = useSettingStore();
-    this.nodeSourceBadgeMode = computed(
-      () => settingStore.get("Comfy.NodeBadge.NodeSourceBadgeMode")
-    );
-    this.nodeIdBadgeMode = computed(
-      () => settingStore.get("Comfy.NodeBadge.NodeIdBadgeMode")
-    );
-    this.nodeLifeCycleBadgeMode = computed(
-      () => settingStore.get(
-        "Comfy.NodeBadge.NodeLifeCycleBadgeMode"
-      )
-    );
-    this.colorPalette = computed(
-      () => getColorPalette(settingStore.get("Comfy.ColorPalette"))
-    );
-    watch(this.nodeSourceBadgeMode, () => {
-      app2.graph.setDirtyCanvas(true, true);
-    });
-    watch(this.nodeIdBadgeMode, () => {
-      app2.graph.setDirtyCanvas(true, true);
-    });
-    watch(this.nodeLifeCycleBadgeMode, () => {
-      app2.graph.setDirtyCanvas(true, true);
-    });
-  }
-  nodeCreated(node, app2) {
-    node.badgePosition = BadgePosition.TopRight;
-    node.badge_enabled = true;
-    const badge = computed(
-      () => new LGraphBadge({
-        text: _.truncate(
-          [
-            getNodeIdBadgeText(node, this.nodeIdBadgeMode.value),
-            getNodeLifeCycleBadgeText(
-              node,
-              this.nodeLifeCycleBadgeMode.value
-            ),
-            getNodeSourceBadgeText(node, this.nodeSourceBadgeMode.value)
-          ].filter((s) => s.length > 0).join(" "),
-          {
-            length: 31
-          }
-        ),
-        fgColor: this.colorPalette.value.colors.litegraph_base?.BADGE_FG_COLOR || defaultColorPalette.colors.litegraph_base.BADGE_FG_COLOR,
-        bgColor: this.colorPalette.value.colors.litegraph_base?.BADGE_BG_COLOR || defaultColorPalette.colors.litegraph_base.BADGE_BG_COLOR
-      })
-    );
-    node.badges.push(() => badge.value);
-  }
-}
-app.registerExtension(new NodeBadgeExtension());
-//# sourceMappingURL=index-D36_Nnai.js.map

web/assets/index-BitceZ14.css

@@ -1,51 +1,30 @@
 
-:root {
+.no-results-placeholder[data-v-a1e982e0] .p-card {
-  --red-600: #dc3545;
-}
-
-.comfy-missing-nodes[data-v-0a88b934] {
-  font-family: monospace;
-  color: var(--red-600);
-  padding: 1.5rem;
   background-color: var(--surface-ground);
-  border-radius: var(--border-radius);
+  text-align: center;
-  box-shadow: var(--card-shadow);
+  box-shadow: unset;
 }
-.warning-title[data-v-0a88b934] {
+.no-results-placeholder h3[data-v-a1e982e0] {
-  margin-top: 0;
+  color: var(--text-color);
+  margin-bottom: 0.5rem;
+}
+.no-results-placeholder p[data-v-a1e982e0] {
+  color: var(--text-color-secondary);
   margin-bottom: 1rem;
 }
-.warning-description[data-v-0a88b934] {
+
-  margin-bottom: 1rem;
+.comfy-missing-nodes[data-v-05a7c5eb] {
-}
-.missing-nodes-list[data-v-0a88b934] {
   max-height: 300px;
   overflow-y: auto;
 }
-.missing-nodes-list.maximized[data-v-0a88b934] {
+.node-hint[data-v-05a7c5eb] {
-  max-height: unset;
-}
-.missing-node-item[data-v-0a88b934] {
-  display: flex;
-  align-items: center;
-  padding: 0.5rem;
-}
-.node-type[data-v-0a88b934] {
-  font-weight: 600;
-  color: var(--text-color);
-}
-.node-hint[data-v-0a88b934] {
   margin-left: 0.5rem;
   font-style: italic;
   color: var(--text-color-secondary);
 }
-[data-v-0a88b934] .p-button {
+[data-v-05a7c5eb] .p-button {
   margin-left: auto;
 }
-.added-nodes-warning[data-v-0a88b934] {
-  margin-top: 1rem;
-  font-style: italic;
-}
 
 :root {
   --red-600: #dc3545;
@@ -217,24 +196,6 @@
   border: none !important;
 }
 
-.no-results-placeholder[data-v-c19e9e10] {
-  height: 100%;
-  padding: 2rem;
-}
-.no-results-placeholder[data-v-c19e9e10] .p-card {
-  background-color: var(--surface-ground);
-  text-align: center;
-  box-shadow: unset;
-}
-.no-results-placeholder h3[data-v-c19e9e10] {
-  color: var(--text-color);
-  margin-bottom: 0.5rem;
-}
-.no-results-placeholder p[data-v-c19e9e10] {
-  color: var(--text-color-secondary);
-  margin-bottom: 1rem;
-}
-
 .settings-tab-panels {
   padding-top: 0px !important;
 }
@@ -1808,6 +1769,9 @@ cursor: pointer;
       max-width: 3200px;
     }
   }
+  .pointer-events-none{
+    pointer-events: none;
+  }
   .pointer-events-auto{
     pointer-events: auto;
   }
@@ -2049,6 +2013,9 @@ cursor: pointer;
     margin-top: calc(0.5rem * calc(1 - var(--tw-space-y-reverse)));
     margin-bottom: calc(0.5rem * var(--tw-space-y-reverse));
   }
+  .justify-self-end{
+    justify-self: end;
+  }
   .overflow-hidden{
     overflow: hidden;
   }
@@ -2072,6 +2039,9 @@ cursor: pointer;
   .whitespace-pre-line{
     white-space: pre-line;
   }
+  .whitespace-pre-wrap{
+    white-space: pre-wrap;
+  }
   .text-wrap{
     text-wrap: wrap;
   }
@@ -2135,9 +2105,15 @@ cursor: pointer;
   .p-2{
     padding: 0.5rem;
   }
+  .p-3{
+    padding: 0.75rem;
+  }
   .p-4{
     padding: 1rem;
   }
+  .p-8{
+    padding: 2rem;
+  }
   .px-0{
     padding-left: 0px;
     padding-right: 0px;
@@ -2146,6 +2122,10 @@ cursor: pointer;
     padding-left: 0.5rem;
     padding-right: 0.5rem;
   }
+  .px-4{
+    padding-left: 1rem;
+    padding-right: 1rem;
+  }
   .py-0{
     padding-top: 0px;
     padding-bottom: 0px;

web/assets/index-C_wOqB0f.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { bM as script$4, A as createBaseVNode, g as openBlock, h as createElementBlock, m as mergeProps } from "./index-CgU1oKZt.js";
+import { bS as script$4, A as createBaseVNode, g as openBlock, h as createElementBlock, m as mergeProps } from "./index-BHayQCxv.js";
 var script$3 = {
   name: "BarsIcon",
   "extends": script$4
@@ -99,4 +99,4 @@ export {
   script$2 as c,
   script as s
 };
-//# sourceMappingURL=index-DYEEBf64.js.map
+//# sourceMappingURL=index-C_wOqB0f.js.map

web/assets/index-CwRXxFdA.js

@@ -1,7 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { bM as script$s, A as createBaseVNode, g as openBlock, h as createElementBlock, m as mergeProps, B as BaseStyle, R as script$t, am as toDisplayString, al as Ripple, t as resolveDirective, v as withDirectives, x as createBlock, O as resolveDynamicComponent, bN as script$u, l as resolveComponent, C as normalizeClass, ao as createSlots, y as withCtx, bf as script$v, b5 as script$w, M as Fragment, N as renderList, ap as createTextVNode, aX as setAttribute, a1 as UniqueComponentId, aV as normalizeProps, p as renderSlot, j as createCommentVNode, ac as equals, aS as script$x, bO as script$y, bP as getFirstFocusableElement, a5 as OverlayEventBus, Y as getVNodeProp, a3 as resolveFieldData, bQ as invokeElementMethod, bR as getAttribute, bS as getNextElementSibling, W as getOuterWidth, bT as getPreviousElementSibling, D as script$z, ai as script$A, bU as script$B, aU as script$D, a0 as isNotEmpty, b2 as withModifiers, X as getOuterHeight, bV as _default, a2 as ZIndex, a4 as focus, a7 as addStyle, a9 as absolutePosition, aa as ConnectedOverlayScrollHandler, ab as isTouchDevice, bW as FilterOperator, ah as script$E, bX as FocusTrap, i as createVNode, an as Transition, bY as withKeys, bZ as getIndex, s as script$G, b_ as isClickable, b$ as clearSelection, c0 as localeComparator, c1 as sort, c2 as FilterService, bH as FilterMatchMode, ae as findSingle, c3 as findIndexInList, c4 as find, c5 as exportCSV, c6 as getOffset, c7 as getHiddenElementOuterWidth, c8 as getHiddenElementOuterHeight, c9 as reorderArray, ca as getWindowScrollTop, cb as removeClass, cc as addClass, a6 as isEmpty, ag as script$H, aj as script$I, aT as script$L, $ as vShow } from "./index-CgU1oKZt.js";
+import { bS as script$s, A as createBaseVNode, g as openBlock, h as createElementBlock, m as mergeProps, B as BaseStyle, R as script$t, a6 as toDisplayString, a1 as Ripple, t as resolveDirective, v as withDirectives, x as createBlock, J as resolveDynamicComponent, bT as script$u, l as resolveComponent, C as normalizeClass, av as createSlots, y as withCtx, br as script$v, bh as script$w, N as Fragment, O as renderList, aw as createTextVNode, b7 as setAttribute, ad as UniqueComponentId, b5 as normalizeProps, p as renderSlot, j as createCommentVNode, a4 as equals, b2 as script$x, bU as script$y, bV as getFirstFocusableElement, ag as OverlayEventBus, a8 as getVNodeProp, af as resolveFieldData, bW as invokeElementMethod, a2 as getAttribute, bX as getNextElementSibling, Y as getOuterWidth, bY as getPreviousElementSibling, D as script$z, ar as script$A, a0 as script$B, b4 as script$D, ac as isNotEmpty, be as withModifiers, W as getOuterHeight, bZ as _default, ae as ZIndex, a3 as focus, ai as addStyle, ak as absolutePosition, al as ConnectedOverlayScrollHandler, am as isTouchDevice, b_ as FilterOperator, aq as script$E, b$ as FocusTrap, i as createVNode, au as Transition, c0 as withKeys, c1 as getIndex, s as script$G, c2 as isClickable, c3 as clearSelection, c4 as localeComparator, c5 as sort, c6 as FilterService, bN as FilterMatchMode, V as findSingle, c7 as findIndexInList, c8 as find, c9 as exportCSV, X as getOffset, ca as getHiddenElementOuterWidth, cb as getHiddenElementOuterHeight, cc as reorderArray, cd as getWindowScrollTop, ce as removeClass, cf as addClass, ah as isEmpty, ap as script$H, as as script$I, b3 as script$L, ab as vShow } from "./index-BHayQCxv.js";
-import { b as script$C, c as script$F, s as script$J, a as script$K } from "./index-DYEEBf64.js";
+import { b as script$C, c as script$F, s as script$J, a as script$K } from "./index-C_wOqB0f.js";
 var script$r = {
   name: "ArrowDownIcon",
   "extends": script$s
@@ -8994,4 +8994,4 @@ export {
   script as b,
   script$2 as s
 };
-//# sourceMappingURL=index-DBWDcZsl.js.map
+//# sourceMappingURL=index-CwRXxFdA.js.map

web/assets/userSelection-DITGVoWz.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { bq as api, bv as $el } from "./index-CgU1oKZt.js";
+import { bC as api, bG as $el } from "./index-BHayQCxv.js";
 function createSpinner() {
   const div = document.createElement("div");
   div.innerHTML = `<div class="lds-ring"><div></div><div></div><div></div><div></div></div>`;
@@ -126,4 +126,4 @@ window.comfyAPI.userSelection.UserSelectionScreen = UserSelectionScreen;
 export {
   UserSelectionScreen
 };
-//# sourceMappingURL=userSelection-DVDwxLD5.js.map
+//# sourceMappingURL=userSelection-DITGVoWz.js.map

web/assets/widgetInputs-DdecKYqd.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { e as LGraphNode, c as app, bA as applyTextReplacements, bz as ComfyWidgets, bG as addValueControlWidgets, k as LiteGraph } from "./index-CgU1oKZt.js";
+import { e as LGraphNode, c as app, bL as applyTextReplacements, bK as ComfyWidgets, bM as addValueControlWidgets, k as LiteGraph } from "./index-BHayQCxv.js";
 const CONVERTED_TYPE = "converted-widget";
 const VALID_TYPES = [
   "STRING",
@@ -753,4 +753,4 @@ export {
   mergeIfValid,
   setWidgetConfig
 };
-//# sourceMappingURL=widgetInputs-DNVvusS1.js.map
+//# sourceMappingURL=widgetInputs-DdecKYqd.js.map

web/index.html

@@ -6,8 +6,8 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
     <link rel="stylesheet" type="text/css" href="user.css" />
     <link rel="stylesheet" type="text/css" href="materialdesignicons.min.css" />
-    <script type="module" crossorigin src="./assets/index-CgU1oKZt.js"></script>
+    <script type="module" crossorigin src="./assets/index-BHayQCxv.js"></script>
-    <link rel="stylesheet" crossorigin href="./assets/index-BDQCPKeJ.css">
+    <link rel="stylesheet" crossorigin href="./assets/index-BitceZ14.css">
   </head>
   <body class="litegraph grid">
     <div id="vue-app"></div>