Merge branch 'master' into bump-fe-1.30

.github/PULL_REQUEST_TEMPLATE/api-node.md

@@ -0,0 +1,21 @@
+<!-- API_NODE_PR_CHECKLIST: do not remove -->
+
+## API Node PR Checklist
+
+### Scope
+- [ ] **Is API Node Change**
+
+### Pricing & Billing
+- [ ] **Need pricing update**
+- [ ] **No pricing update**
+
+If **Need pricing update**:
+- [ ] Metronome rate cards updated
+- [ ] Auto‑billing tests updated and passing
+
+### QA
+- [ ] **QA done**
+- [ ] **QA not required**
+
+### Comms
+- [ ] Informed **Kosinkadink**

.github/workflows/api-node-template.yml

@@ -0,0 +1,58 @@
+name: Append API Node PR template
+
+on:
+  pull_request_target:
+    types: [opened, reopened, synchronize, ready_for_review]
+    paths:
+      - 'comfy_api_nodes/**'   # only run if these files changed
+
+permissions:
+  contents: read
+  pull-requests: write
+
+jobs:
+  inject:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Ensure template exists and append to PR body
+        uses: actions/github-script@v7
+        with:
+          script: |
+            const { owner, repo } = context.repo;
+            const number = context.payload.pull_request.number;
+            const templatePath = '.github/PULL_REQUEST_TEMPLATE/api-node.md';
+            const marker = '<!-- API_NODE_PR_CHECKLIST: do not remove -->';
+
+            const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number: number });
+
+            let templateText;
+            try {
+              const res = await github.rest.repos.getContent({
+                owner,
+                repo,
+                path: templatePath,
+                ref: pr.base.ref
+              });
+              const buf = Buffer.from(res.data.content, res.data.encoding || 'base64');
+              templateText = buf.toString('utf8');
+            } catch (e) {
+              core.setFailed(`Required PR template not found at "${templatePath}" on ${pr.base.ref}. Please add it to the repo.`);
+              return;
+            }
+
+            // Enforce the presence of the marker inside the template (for idempotence)
+            if (!templateText.includes(marker)) {
+              core.setFailed(`Template at "${templatePath}" does not contain the required marker:\n${marker}\nAdd it so we can detect duplicates safely.`);
+              return;
+            }
+
+            // If the PR already contains the marker, do not append again.
+            const body = pr.body || '';
+            if (body.includes(marker)) {
+              core.info('Template already present in PR body; nothing to inject.');
+              return;
+            }
+
+            const newBody = (body ? body + '\n\n' : '') + templateText + '\n';
+            await github.rest.pulls.update({ owner, repo, pull_number: number, body: newBody });
+            core.notice('API Node template appended to PR description.');

.github/workflows/release-stable-all.yml

@@ -14,7 +14,7 @@ jobs:
       contents: "write"
       packages: "write"
       pull-requests: "read"
-    name: "Release NVIDIA Default (cu129)"
+    name: "Release NVIDIA Default (cu130)"
     uses: ./.github/workflows/stable-release.yml
     with:
       git_tag: ${{ inputs.git_tag }}
@@ -43,6 +43,23 @@ jobs:
       test_release: true
     secrets: inherit
 
+  release_nvidia_cu126:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    name: "Release NVIDIA cu126"
+    uses: ./.github/workflows/stable-release.yml
+    with:
+      git_tag: ${{ inputs.git_tag }}
+      cache_tag: "cu126"
+      python_minor: "12"
+      python_patch: "10"
+      rel_name: "nvidia"
+      rel_extra_name: "_cu126"
+      test_release: true
+    secrets: inherit
+
   release_amd_rocm:
     permissions:
       contents: "write"

README.md

@@ -173,7 +173,7 @@ There is a portable standalone build for Windows that should work for running on
 
 ### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z)
 
-Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
+Simply download, extract with [7-Zip](https://7-zip.org) or with the windows explorer on recent windows versions and run. For smaller models you normally only need to put the checkpoints (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints but many of the larger models have multiple files. Make sure to follow the instructions to know which subfolder to put them in ComfyUI\models\
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
@@ -183,7 +183,9 @@ Update your Nvidia drivers if it doesn't start.
 
 [Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
 
-[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z) (Supports Nvidia 10 series and older GPUs).
+[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z).
+
+[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
 
 #### How do I share models between another UI and ComfyUI?
 
@@ -221,7 +223,7 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins
 
 This is the command to install the nightly with ROCm 7.0 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.0```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.1```
 
 
 ### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.

app/frontend_management.py

@@ -10,7 +10,8 @@ import importlib
 from dataclasses import dataclass
 from functools import cached_property
 from pathlib import Path
-from typing import TypedDict, Optional
+from typing import Dict, TypedDict, Optional
+from aiohttp import web
 from importlib.metadata import version
 
 import requests
@@ -257,7 +258,54 @@ comfyui-frontend-package is not installed.
             sys.exit(-1)
 
     @classmethod
-    def templates_path(cls) -> str:
+    def template_asset_map(cls) -> Optional[Dict[str, str]]:
+        """Return a mapping of template asset names to their absolute paths."""
+        try:
+            from comfyui_workflow_templates import (
+                get_asset_path,
+                iter_templates,
+            )
+        except ImportError:
+            logging.error(
+                f"""
+********** ERROR ***********
+
+comfyui-workflow-templates is not installed.
+
+{frontend_install_warning_message()}
+
+********** ERROR ***********
+""".strip()
+            )
+            return None
+
+        try:
+            template_entries = list(iter_templates())
+        except Exception as exc:
+            logging.error(f"Failed to enumerate workflow templates: {exc}")
+            return None
+
+        asset_map: Dict[str, str] = {}
+        try:
+            for entry in template_entries:
+                for asset in entry.assets:
+                    asset_map[asset.filename] = get_asset_path(
+                        entry.template_id, asset.filename
+                    )
+        except Exception as exc:
+            logging.error(f"Failed to resolve template asset paths: {exc}")
+            return None
+
+        if not asset_map:
+            logging.error("No workflow template assets found. Did the packages install correctly?")
+            return None
+
+        return asset_map
+
+
+    @classmethod
+    def legacy_templates_path(cls) -> Optional[str]:
+        """Return the legacy templates directory shipped inside the meta package."""
         try:
             import comfyui_workflow_templates
 
@@ -276,6 +324,7 @@ comfyui-workflow-templates is not installed.
 ********** ERROR ***********
 """.strip()
             )
+            return None
 
     @classmethod
     def embedded_docs_path(cls) -> str:
@@ -392,3 +441,17 @@ comfyui-workflow-templates is not installed.
             logging.info("Falling back to the default frontend.")
             check_frontend_version()
             return cls.default_frontend_path()
+    @classmethod
+    def template_asset_handler(cls):
+        assets = cls.template_asset_map()
+        if not assets:
+            return None
+
+        async def serve_template(request: web.Request) -> web.StreamResponse:
+            rel_path = request.match_info.get("path", "")
+            target = assets.get(rel_path)
+            if target is None:
+                raise web.HTTPNotFound()
+            return web.FileResponse(target)
+
+        return serve_template

comfy/ldm/chroma/layers.py

@@ -1,15 +1,15 @@
 import torch
 from torch import Tensor, nn
 
-from comfy.ldm.flux.math import attention
 from comfy.ldm.flux.layers import (
     MLPEmbedder,
     RMSNorm,
-    QKNorm,
-    SelfAttention,
     ModulationOut,
 )
 
+# TODO: remove this in a few months
+SingleStreamBlock = None
+DoubleStreamBlock = None
 
 
 class ChromaModulationOut(ModulationOut):
@@ -48,124 +48,6 @@ class Approximator(nn.Module):
         return x
 
 
-class DoubleStreamBlock(nn.Module):
-    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None):
-        super().__init__()
-
-        mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        self.num_heads = num_heads
-        self.hidden_size = hidden_size
-        self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
-
-        self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.img_mlp = nn.Sequential(
-            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
-            nn.GELU(approximate="tanh"),
-            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
-        )
-
-        self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
-
-        self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.txt_mlp = nn.Sequential(
-            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
-            nn.GELU(approximate="tanh"),
-            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
-        )
-        self.flipped_img_txt = flipped_img_txt
-
-    def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}):
-        (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
-
-        # prepare image for attention
-        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 = 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)
-
-        # run actual attention
-        attn = attention(torch.cat((txt_q, img_q), dim=2),
-                         torch.cat((txt_k, img_k), dim=2),
-                         torch.cat((txt_v, img_v), dim=2),
-                         pe=pe, mask=attn_mask, transformer_options=transformer_options)
-
-        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
-
-        # calculate the img bloks
-        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.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)
-
-        return img, txt
-
-
-class SingleStreamBlock(nn.Module):
-    """
-    A DiT block with parallel linear layers as described in
-    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        mlp_ratio: float = 4.0,
-        qk_scale: float = None,
-        dtype=None,
-        device=None,
-        operations=None
-    ):
-        super().__init__()
-        self.hidden_dim = hidden_size
-        self.num_heads = num_heads
-        head_dim = hidden_size // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        # qkv and mlp_in
-        self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
-        # proj and mlp_out
-        self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
-
-        self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
-
-        self.hidden_size = hidden_size
-        self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-
-        self.mlp_act = nn.GELU(approximate="tanh")
-
-    def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}) -> Tensor:
-        mod = vec
-        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)
-        q, k = self.norm(q, k, v)
-
-        # compute attention
-        attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
-        # compute activation in mlp stream, cat again and run second linear layer
-        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
-        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
-
-
 class LastLayer(nn.Module):
     def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
         super().__init__()

comfy/ldm/chroma/model.py

@@ -11,12 +11,12 @@ import comfy.ldm.common_dit
 from comfy.ldm.flux.layers import (
     EmbedND,
     timestep_embedding,
+    DoubleStreamBlock,
+    SingleStreamBlock,
 )
 
 from .layers import (
-    DoubleStreamBlock,
     LastLayer,
-    SingleStreamBlock,
     Approximator,
     ChromaModulationOut,
 )
@@ -90,6 +90,7 @@ class Chroma(nn.Module):
                     self.num_heads,
                     mlp_ratio=params.mlp_ratio,
                     qkv_bias=params.qkv_bias,
+                    modulation=False,
                     dtype=dtype, device=device, operations=operations
                 )
                 for _ in range(params.depth)
@@ -98,7 +99,7 @@ class Chroma(nn.Module):
 
         self.single_blocks = nn.ModuleList(
             [
-                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=False, dtype=dtype, device=device, operations=operations)
                 for _ in range(params.depth_single_blocks)
             ]
         )

comfy/ldm/chroma_radiance/model.py

@@ -10,12 +10,10 @@ from torch import Tensor, nn
 from einops import repeat
 import comfy.ldm.common_dit
 
-from comfy.ldm.flux.layers import EmbedND
+from comfy.ldm.flux.layers import EmbedND, DoubleStreamBlock, SingleStreamBlock
 
 from comfy.ldm.chroma.model import Chroma, ChromaParams
 from comfy.ldm.chroma.layers import (
-    DoubleStreamBlock,
-    SingleStreamBlock,
     Approximator,
 )
 from .layers import (
@@ -89,7 +87,6 @@ class ChromaRadiance(Chroma):
                     dtype=dtype, device=device, operations=operations
                 )
 
-
         self.double_blocks = nn.ModuleList(
             [
                 DoubleStreamBlock(
@@ -97,6 +94,7 @@ class ChromaRadiance(Chroma):
                     self.num_heads,
                     mlp_ratio=params.mlp_ratio,
                     qkv_bias=params.qkv_bias,
+                    modulation=False,
                     dtype=dtype, device=device, operations=operations
                 )
                 for _ in range(params.depth)
@@ -109,6 +107,7 @@ class ChromaRadiance(Chroma):
                     self.hidden_size,
                     self.num_heads,
                     mlp_ratio=params.mlp_ratio,
+                    modulation=False,
                     dtype=dtype, device=device, operations=operations,
                 )
                 for _ in range(params.depth_single_blocks)

comfy/ldm/flux/layers.py

@@ -130,13 +130,17 @@ def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
 
 
 class DoubleStreamBlock(nn.Module):
-    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, dtype=None, device=None, operations=None):
         super().__init__()
 
         mlp_hidden_dim = int(hidden_size * mlp_ratio)
         self.num_heads = num_heads
         self.hidden_size = hidden_size
-        self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+        self.modulation = modulation
+
+        if self.modulation:
+            self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+
         self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
         self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
 
@@ -147,7 +151,9 @@ class DoubleStreamBlock(nn.Module):
             operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
         )
 
-        self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+        if self.modulation:
+            self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+
         self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
         self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
 
@@ -160,46 +166,65 @@ class DoubleStreamBlock(nn.Module):
         self.flipped_img_txt = flipped_img_txt
 
     def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}):
-        img_mod1, img_mod2 = self.img_mod(vec)
-        txt_mod1, txt_mod2 = self.txt_mod(vec)
+        if self.modulation:
+            img_mod1, img_mod2 = self.img_mod(vec)
+            txt_mod1, txt_mod2 = self.txt_mod(vec)
+        else:
+            (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
 
         # prepare image for attention
         img_modulated = self.img_norm1(img)
         img_modulated = apply_mod(img_modulated, (1 + img_mod1.scale), img_mod1.shift, modulation_dims_img)
         img_qkv = self.img_attn.qkv(img_modulated)
+        del 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)
+        del img_qkv
         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 = apply_mod(txt_modulated, (1 + txt_mod1.scale), txt_mod1.shift, modulation_dims_txt)
         txt_qkv = self.txt_attn.qkv(txt_modulated)
+        del 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)
+        del txt_qkv
         txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
 
         if self.flipped_img_txt:
+            q = torch.cat((img_q, txt_q), dim=2)
+            del img_q, txt_q
+            k = torch.cat((img_k, txt_k), dim=2)
+            del img_k, txt_k
+            v = torch.cat((img_v, txt_v), dim=2)
+            del img_v, txt_v
             # run actual attention
-            attn = attention(torch.cat((img_q, txt_q), dim=2),
-                             torch.cat((img_k, txt_k), dim=2),
-                             torch.cat((img_v, txt_v), dim=2),
+            attn = attention(q, k, v,
                              pe=pe, mask=attn_mask, transformer_options=transformer_options)
+            del q, k, v
 
             img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:]
         else:
+            q = torch.cat((txt_q, img_q), dim=2)
+            del txt_q, img_q
+            k = torch.cat((txt_k, img_k), dim=2)
+            del txt_k, img_k
+            v = torch.cat((txt_v, img_v), dim=2)
+            del txt_v, img_v
             # run actual attention
-            attn = attention(torch.cat((txt_q, img_q), dim=2),
-                             torch.cat((txt_k, img_k), dim=2),
-                             torch.cat((txt_v, img_v), dim=2),
+            attn = attention(q, k, v,
                              pe=pe, mask=attn_mask, transformer_options=transformer_options)
+            del q, k, v
 
             txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
 
         # calculate the img bloks
         img += apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img)
+        del img_attn
         img += apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img)
 
         # calculate the txt bloks
         txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims_txt)
+        del txt_attn
         txt += apply_mod(self.txt_mlp(apply_mod(self.txt_norm2(txt), (1 + txt_mod2.scale), txt_mod2.shift, modulation_dims_txt)), txt_mod2.gate, None, modulation_dims_txt)
 
         if txt.dtype == torch.float16:
@@ -220,6 +245,7 @@ class SingleStreamBlock(nn.Module):
         num_heads: int,
         mlp_ratio: float = 4.0,
         qk_scale: float = None,
+        modulation=True,
         dtype=None,
         device=None,
         operations=None
@@ -242,19 +268,29 @@ class SingleStreamBlock(nn.Module):
         self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
 
         self.mlp_act = nn.GELU(approximate="tanh")
-        self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
+        if modulation:
+            self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
+        else:
+            self.modulation = None
 
     def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None, transformer_options={}) -> Tensor:
-        mod, _ = self.modulation(vec)
+        if self.modulation:
+            mod, _ = self.modulation(vec)
+        else:
+            mod = vec
+
         qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [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)
+        del qkv
         q, k = self.norm(q, k, v)
 
         # compute attention
         attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
+        del q, k, v
         # compute activation in mlp stream, cat again and run second linear layer
-        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+        mlp = self.mlp_act(mlp)
+        output = self.linear2(torch.cat((attn, mlp), 2))
         x += apply_mod(output, mod.gate, None, modulation_dims)
         if x.dtype == torch.float16:
             x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)

comfy/ldm/flux/math.py

@@ -7,7 +7,8 @@ import comfy.model_management
 
 
 def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
-    q, k = apply_rope(q, k, pe)
+    if pe is not None:
+        q, k = apply_rope(q, k, pe)
     heads = q.shape[1]
     x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)
     return x

comfy/ops.py

@@ -58,7 +58,8 @@ except (ModuleNotFoundError, TypeError):
 NVIDIA_MEMORY_CONV_BUG_WORKAROUND = False
 try:
     if comfy.model_management.is_nvidia():
-        if torch.backends.cudnn.version() >= 91002 and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):
+        cudnn_version = torch.backends.cudnn.version()
+        if (cudnn_version >= 91002 and cudnn_version < 91500) and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):
             #TODO: change upper bound version once it's fixed'
             NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True
             logging.info("working around nvidia conv3d memory bug.")

comfy/sd1_clip.py

@@ -460,7 +460,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     return embed_out
 
 class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data={}, tokenizer_args={}):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, pad_left=False, tokenizer_data={}, tokenizer_args={}):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
         self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
@@ -468,6 +468,7 @@ class SDTokenizer:
         self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length)
         self.end_token = None
         self.min_padding = min_padding
+        self.pad_left = pad_left
 
         empty = self.tokenizer('')["input_ids"]
         self.tokenizer_adds_end_token = has_end_token
@@ -522,6 +523,12 @@ class SDTokenizer:
                 return (embed, "{} {}".format(embedding_name[len(stripped):], leftover))
         return (embed, leftover)
 
+    def pad_tokens(self, tokens, amount):
+        if self.pad_left:
+            for i in range(amount):
+                tokens.insert(0, (self.pad_token, 1.0, 0))
+        else:
+            tokens.extend([(self.pad_token, 1.0, 0)] * amount)
 
     def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs):
         '''
@@ -600,7 +607,7 @@ class SDTokenizer:
                         if self.end_token is not None:
                             batch.append((self.end_token, 1.0, 0))
                         if self.pad_to_max_length:
-                            batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
+                            self.pad_tokens(batch, remaining_length)
                     #start new batch
                     batch = []
                     if self.start_token is not None:
@@ -614,11 +621,11 @@ class SDTokenizer:
         if self.end_token is not None:
             batch.append((self.end_token, 1.0, 0))
         if min_padding is not None:
-            batch.extend([(self.pad_token, 1.0, 0)] * min_padding)
+            self.pad_tokens(batch, min_padding)
         if self.pad_to_max_length and len(batch) < self.max_length:
-            batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch)))
+            self.pad_tokens(batch, self.max_length - len(batch))
         if min_length is not None and len(batch) < min_length:
-            batch.extend([(self.pad_token, 1.0, 0)] * (min_length - len(batch)))
+            self.pad_tokens(batch, min_length - len(batch))
 
         if not return_word_ids:
             batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]

comfy/text_encoders/llama.py

@@ -32,6 +32,7 @@ class Llama2Config:
     q_norm = None
     k_norm = None
     rope_scale = None
+    final_norm: bool = True
 
 @dataclass
 class Qwen25_3BConfig:
@@ -53,6 +54,7 @@ class Qwen25_3BConfig:
     q_norm = None
     k_norm = None
     rope_scale = None
+    final_norm: bool = True
 
 @dataclass
 class Qwen25_7BVLI_Config:
@@ -74,6 +76,7 @@ class Qwen25_7BVLI_Config:
     q_norm = None
     k_norm = None
     rope_scale = None
+    final_norm: bool = True
 
 @dataclass
 class Gemma2_2B_Config:
@@ -96,6 +99,7 @@ class Gemma2_2B_Config:
     k_norm = None
     sliding_attention = None
     rope_scale = None
+    final_norm: bool = True
 
 @dataclass
 class Gemma3_4B_Config:
@@ -118,6 +122,7 @@ class Gemma3_4B_Config:
     k_norm = "gemma3"
     sliding_attention = [False, False, False, False, False, 1024]
     rope_scale = [1.0, 8.0]
+    final_norm: bool = True
 
 class RMSNorm(nn.Module):
     def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
@@ -366,7 +371,12 @@ class Llama2_(nn.Module):
             transformer(config, index=i, device=device, dtype=dtype, ops=ops)
             for i in range(config.num_hidden_layers)
         ])
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
+        if config.final_norm:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        else:
+            self.norm = None
+
         # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
 
     def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[]):
@@ -421,14 +431,16 @@ class Llama2_(nn.Module):
             if i == intermediate_output:
                 intermediate = x.clone()
 
-        x = self.norm(x)
+        if self.norm is not None:
+            x = self.norm(x)
+
         if all_intermediate is not None:
             all_intermediate.append(x.unsqueeze(1).clone())
 
         if all_intermediate is not None:
             intermediate = torch.cat(all_intermediate, dim=1)
 
-        if intermediate is not None and final_layer_norm_intermediate:
+        if intermediate is not None and final_layer_norm_intermediate and self.norm is not None:
             intermediate = self.norm(intermediate)
 
         return x, intermediate

comfy_api/latest/__init__.py

@@ -7,7 +7,7 @@ from comfy_api.internal.singleton import ProxiedSingleton
 from comfy_api.internal.async_to_sync import create_sync_class
 from comfy_api.latest._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
 from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents
-from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents
+from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL
 from . import _io as io
 from . import _ui as ui
 # from comfy_api.latest._resources import _RESOURCES as resources  #noqa: F401
@@ -104,6 +104,8 @@ class Types:
     VideoCodec = VideoCodec
     VideoContainer = VideoContainer
     VideoComponents = VideoComponents
+    MESH = MESH
+    VOXEL = VOXEL
 
 ComfyAPI = ComfyAPI_latest
 

comfy_api/latest/_io.py

@@ -27,6 +27,7 @@ from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classpr
     prune_dict, shallow_clone_class)
 from comfy_api.latest._resources import Resources, ResourcesLocal
 from comfy_execution.graph_utils import ExecutionBlocker
+from ._util import MESH, VOXEL
 
 # from comfy_extras.nodes_images import SVG as SVG_ # NOTE: needs to be moved before can be imported due to circular reference
 
@@ -656,11 +657,11 @@ class LossMap(ComfyTypeIO):
 
 @comfytype(io_type="VOXEL")
 class Voxel(ComfyTypeIO):
-    Type = Any # TODO: VOXEL class is defined in comfy_extras/nodes_hunyuan3d.py; should be moved to somewhere else before referenced directly in v3
+    Type = VOXEL
 
 @comfytype(io_type="MESH")
 class Mesh(ComfyTypeIO):
-    Type = Any # TODO: MESH class is defined in comfy_extras/nodes_hunyuan3d.py; should be moved to somewhere else before referenced directly in v3
+    Type = MESH
 
 @comfytype(io_type="HOOKS")
 class Hooks(ComfyTypeIO):

comfy_api/latest/_util/__init__.py

@@ -1,8 +1,11 @@
 from .video_types import VideoContainer, VideoCodec, VideoComponents
+from .geometry_types import VOXEL, MESH
 
 __all__ = [
     # Utility Types
     "VideoContainer",
     "VideoCodec",
     "VideoComponents",
+    "VOXEL",
+    "MESH",
 ]

comfy_api/latest/_util/geometry_types.py

@@ -0,0 +1,12 @@
+import torch
+
+
+class VOXEL:
+    def __init__(self, data: torch.Tensor):
+        self.data = data
+
+
+class MESH:
+    def __init__(self, vertices: torch.Tensor, faces: torch.Tensor):
+        self.vertices = vertices
+        self.faces = faces

comfy_api_nodes/apis/gemini_api.py

@@ -1,22 +1,229 @@
-from typing import Optional
+from datetime import date
+from enum import Enum
+from typing import Any
 
-from comfy_api_nodes.apis import GeminiGenerationConfig, GeminiContent, GeminiSafetySetting, GeminiSystemInstructionContent, GeminiTool, GeminiVideoMetadata
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
+
+
+class GeminiSafetyCategory(str, Enum):
+    HARM_CATEGORY_SEXUALLY_EXPLICIT = "HARM_CATEGORY_SEXUALLY_EXPLICIT"
+    HARM_CATEGORY_HATE_SPEECH = "HARM_CATEGORY_HATE_SPEECH"
+    HARM_CATEGORY_HARASSMENT = "HARM_CATEGORY_HARASSMENT"
+    HARM_CATEGORY_DANGEROUS_CONTENT = "HARM_CATEGORY_DANGEROUS_CONTENT"
+
+
+class GeminiSafetyThreshold(str, Enum):
+    OFF = "OFF"
+    BLOCK_NONE = "BLOCK_NONE"
+    BLOCK_LOW_AND_ABOVE = "BLOCK_LOW_AND_ABOVE"
+    BLOCK_MEDIUM_AND_ABOVE = "BLOCK_MEDIUM_AND_ABOVE"
+    BLOCK_ONLY_HIGH = "BLOCK_ONLY_HIGH"
+
+
+class GeminiSafetySetting(BaseModel):
+    category: GeminiSafetyCategory
+    threshold: GeminiSafetyThreshold
+
+
+class GeminiRole(str, Enum):
+    user = "user"
+    model = "model"
+
+
+class GeminiMimeType(str, Enum):
+    application_pdf = "application/pdf"
+    audio_mpeg = "audio/mpeg"
+    audio_mp3 = "audio/mp3"
+    audio_wav = "audio/wav"
+    image_png = "image/png"
+    image_jpeg = "image/jpeg"
+    image_webp = "image/webp"
+    text_plain = "text/plain"
+    video_mov = "video/mov"
+    video_mpeg = "video/mpeg"
+    video_mp4 = "video/mp4"
+    video_mpg = "video/mpg"
+    video_avi = "video/avi"
+    video_wmv = "video/wmv"
+    video_mpegps = "video/mpegps"
+    video_flv = "video/flv"
+
+
+class GeminiInlineData(BaseModel):
+    data: str | None = Field(
+        None,
+        description="The base64 encoding of the image, PDF, or video to include inline in the prompt. "
+        "When including media inline, you must also specify the media type (mimeType) of the data. Size limit: 20MB",
+    )
+    mimeType: GeminiMimeType | None = Field(None)
+
+
+class GeminiPart(BaseModel):
+    inlineData: GeminiInlineData | None = Field(None)
+    text: str | None = Field(None)
+
+
+class GeminiTextPart(BaseModel):
+    text: str | None = Field(None)
+
+
+class GeminiContent(BaseModel):
+    parts: list[GeminiPart] = Field(...)
+    role: GeminiRole = Field(..., examples=["user"])
+
+
+class GeminiSystemInstructionContent(BaseModel):
+    parts: list[GeminiTextPart] = Field(
+        ...,
+        description="A list of ordered parts that make up a single message. "
+        "Different parts may have different IANA MIME types.",
+    )
+    role: GeminiRole = Field(
+        ...,
+        description="The identity of the entity that creates the message. "
+        "The following values are supported: "
+        "user: This indicates that the message is sent by a real person, typically a user-generated message. "
+        "model: This indicates that the message is generated by the model. "
+        "The model value is used to insert messages from model into the conversation during multi-turn conversations. "
+        "For non-multi-turn conversations, this field can be left blank or unset.",
+    )
+
+
+class GeminiFunctionDeclaration(BaseModel):
+    description: str | None = Field(None)
+    name: str = Field(...)
+    parameters: dict[str, Any] = Field(..., description="JSON schema for the function parameters")
+
+
+class GeminiTool(BaseModel):
+    functionDeclarations: list[GeminiFunctionDeclaration] | None = Field(None)
+
+
+class GeminiOffset(BaseModel):
+    nanos: int | None = Field(None, ge=0, le=999999999)
+    seconds: int | None = Field(None, ge=-315576000000, le=315576000000)
+
+
+class GeminiVideoMetadata(BaseModel):
+    endOffset: GeminiOffset | None = Field(None)
+    startOffset: GeminiOffset | None = Field(None)
+
+
+class GeminiGenerationConfig(BaseModel):
+    maxOutputTokens: int | None = Field(None, ge=16, le=8192)
+    seed: int | None = Field(None)
+    stopSequences: list[str] | None = Field(None)
+    temperature: float | None = Field(1, ge=0.0, le=2.0)
+    topK: int | None = Field(40, ge=1)
+    topP: float | None = Field(0.95, ge=0.0, le=1.0)
 
 
 class GeminiImageConfig(BaseModel):
-    aspectRatio: Optional[str] = None
+    aspectRatio: str | None = Field(None)
+    resolution: str | None = Field(None)
 
 
 class GeminiImageGenerationConfig(GeminiGenerationConfig):
-    responseModalities: Optional[list[str]] = None
-    imageConfig: Optional[GeminiImageConfig] = None
+    responseModalities: list[str] | None = Field(None)
+    imageConfig: GeminiImageConfig | None = Field(None)
 
 
 class GeminiImageGenerateContentRequest(BaseModel):
-    contents: list[GeminiContent]
-    generationConfig: Optional[GeminiImageGenerationConfig] = None
-    safetySettings: Optional[list[GeminiSafetySetting]] = None
-    systemInstruction: Optional[GeminiSystemInstructionContent] = None
-    tools: Optional[list[GeminiTool]] = None
-    videoMetadata: Optional[GeminiVideoMetadata] = None
+    contents: list[GeminiContent] = Field(...)
+    generationConfig: GeminiImageGenerationConfig | None = Field(None)
+    safetySettings: list[GeminiSafetySetting] | None = Field(None)
+    systemInstruction: GeminiSystemInstructionContent | None = Field(None)
+    tools: list[GeminiTool] | None = Field(None)
+    videoMetadata: GeminiVideoMetadata | None = Field(None)
+
+
+class GeminiGenerateContentRequest(BaseModel):
+    contents: list[GeminiContent] = Field(...)
+    generationConfig: GeminiGenerationConfig | None = Field(None)
+    safetySettings: list[GeminiSafetySetting] | None = Field(None)
+    systemInstruction: GeminiSystemInstructionContent | None = Field(None)
+    tools: list[GeminiTool] | None = Field(None)
+    videoMetadata: GeminiVideoMetadata | None = Field(None)
+
+
+class Modality(str, Enum):
+    MODALITY_UNSPECIFIED = "MODALITY_UNSPECIFIED"
+    TEXT = "TEXT"
+    IMAGE = "IMAGE"
+    VIDEO = "VIDEO"
+    AUDIO = "AUDIO"
+    DOCUMENT = "DOCUMENT"
+
+
+class ModalityTokenCount(BaseModel):
+    modality: Modality | None = None
+    tokenCount: int | None = Field(None, description="Number of tokens for the given modality.")
+
+
+class Probability(str, Enum):
+    NEGLIGIBLE = "NEGLIGIBLE"
+    LOW = "LOW"
+    MEDIUM = "MEDIUM"
+    HIGH = "HIGH"
+    UNKNOWN = "UNKNOWN"
+
+
+class GeminiSafetyRating(BaseModel):
+    category: GeminiSafetyCategory | None = None
+    probability: Probability | None = Field(
+        None,
+        description="The probability that the content violates the specified safety category",
+    )
+
+
+class GeminiCitation(BaseModel):
+    authors: list[str] | None = None
+    endIndex: int | None = None
+    license: str | None = None
+    publicationDate: date | None = None
+    startIndex: int | None = None
+    title: str | None = None
+    uri: str | None = None
+
+
+class GeminiCitationMetadata(BaseModel):
+    citations: list[GeminiCitation] | None = None
+
+
+class GeminiCandidate(BaseModel):
+    citationMetadata: GeminiCitationMetadata | None = None
+    content: GeminiContent | None = None
+    finishReason: str | None = None
+    safetyRatings: list[GeminiSafetyRating] | None = None
+
+
+class GeminiPromptFeedback(BaseModel):
+    blockReason: str | None = None
+    blockReasonMessage: str | None = None
+    safetyRatings: list[GeminiSafetyRating] | None = None
+
+
+class GeminiUsageMetadata(BaseModel):
+    cachedContentTokenCount: int | None = Field(
+        None,
+        description="Output only. Number of tokens in the cached part in the input (the cached content).",
+    )
+    candidatesTokenCount: int | None = Field(None, description="Number of tokens in the response(s).")
+    candidatesTokensDetails: list[ModalityTokenCount] | None = Field(
+        None, description="Breakdown of candidate tokens by modality."
+    )
+    promptTokenCount: int | None = Field(
+        None,
+        description="Number of tokens in the request. When cachedContent is set, this is still the total effective prompt size meaning this includes the number of tokens in the cached content.",
+    )
+    promptTokensDetails: list[ModalityTokenCount] | None = Field(
+        None, description="Breakdown of prompt tokens by modality."
+    )
+    thoughtsTokenCount: int | None = Field(None, description="Number of tokens present in thoughts output.")
+    toolUsePromptTokenCount: int | None = Field(None, description="Number of tokens present in tool-use prompt(s).")
+
+
+class GeminiGenerateContentResponse(BaseModel):
+    candidates: list[GeminiCandidate] | None = Field(None)
+    promptFeedback: GeminiPromptFeedback | None = Field(None)
+    usageMetadata: GeminiUsageMetadata | None = Field(None)

comfy_api_nodes/apis/topaz_api.py

@@ -0,0 +1,133 @@
+from typing import Optional, Union
+
+from pydantic import BaseModel, Field
+
+
+class ImageEnhanceRequest(BaseModel):
+    model: str = Field("Reimagine")
+    output_format: str = Field("jpeg")
+    subject_detection: str = Field("All")
+    face_enhancement: bool = Field(True)
+    face_enhancement_creativity: float = Field(0, description="Is ignored if face_enhancement is false")
+    face_enhancement_strength: float = Field(0.8, description="Is ignored if face_enhancement is false")
+    source_url: str = Field(...)
+    output_width: Optional[int] = Field(None)
+    output_height: Optional[int] = Field(None)
+    crop_to_fill: bool = Field(False)
+    prompt: Optional[str] = Field(None, description="Text prompt for creative upscaling guidance")
+    creativity: int = Field(3, description="Creativity settings range from 1 to 9")
+    face_preservation: str = Field("true", description="To preserve the identity of characters")
+    color_preservation: str = Field("true", description="To preserve the original color")
+
+
+class ImageAsyncTaskResponse(BaseModel):
+    process_id: str = Field(...)
+
+
+class ImageStatusResponse(BaseModel):
+    process_id: str = Field(...)
+    status: str = Field(...)
+    progress: Optional[int] = Field(None)
+    credits: int = Field(...)
+
+
+class ImageDownloadResponse(BaseModel):
+    download_url: str = Field(...)
+    expiry: int = Field(...)
+
+
+class Resolution(BaseModel):
+    width: int = Field(...)
+    height: int = Field(...)
+
+
+class CreateCreateVideoRequestSource(BaseModel):
+    container: str = Field(...)
+    size: int = Field(..., description="Size of the video file in bytes")
+    duration: int = Field(..., description="Duration of the video file in seconds")
+    frameCount: int = Field(..., description="Total number of frames in the video")
+    frameRate: int = Field(...)
+    resolution: Resolution = Field(...)
+
+
+class VideoFrameInterpolationFilter(BaseModel):
+    model: str = Field(...)
+    slowmo: Optional[int] = Field(None)
+    fps: int = Field(...)
+    duplicate: bool = Field(...)
+    duplicate_threshold: float = Field(...)
+
+
+class VideoEnhancementFilter(BaseModel):
+    model: str = Field(...)
+    auto: Optional[str] = Field(None, description="Auto, Manual, Relative")
+    focusFixLevel: Optional[str] = Field(None, description="Downscales video input for correction of blurred subjects")
+    compression: Optional[float] = Field(None, description="Strength of compression recovery")
+    details: Optional[float] = Field(None, description="Amount of detail reconstruction")
+    prenoise: Optional[float] = Field(None, description="Amount of noise to add to input to reduce over-smoothing")
+    noise: Optional[float] = Field(None, description="Amount of noise reduction")
+    halo: Optional[float] = Field(None, description="Amount of halo reduction")
+    preblur: Optional[float] = Field(None, description="Anti-aliasing and deblurring strength")
+    blur: Optional[float] = Field(None, description="Amount of sharpness applied")
+    grain: Optional[float] = Field(None, description="Grain after AI model processing")
+    grainSize: Optional[float] = Field(None, description="Size of generated grain")
+    recoverOriginalDetailValue: Optional[float] = Field(None, description="Source details into the output video")
+    creativity: Optional[str] = Field(None, description="Creativity level(high, low) for slc-1 only")
+    isOptimizedMode: Optional[bool] = Field(None, description="Set to true for Starlight Creative (slc-1) only")
+
+
+class OutputInformationVideo(BaseModel):
+    resolution: Resolution = Field(...)
+    frameRate: int = Field(...)
+    audioCodec: Optional[str] = Field(..., description="Required if audioTransfer is Copy or Convert")
+    audioTransfer: str = Field(..., description="Copy, Convert, None")
+    dynamicCompressionLevel: str = Field(..., description="Low, Mid, High")
+
+
+class Overrides(BaseModel):
+    isPaidDiffusion: bool = Field(True)
+
+
+class CreateVideoRequest(BaseModel):
+    source: CreateCreateVideoRequestSource = Field(...)
+    filters: list[Union[VideoFrameInterpolationFilter, VideoEnhancementFilter]] = Field(...)
+    output: OutputInformationVideo = Field(...)
+    overrides: Overrides = Field(Overrides(isPaidDiffusion=True))
+
+
+class CreateVideoResponse(BaseModel):
+    requestId: str = Field(...)
+
+
+class VideoAcceptResponse(BaseModel):
+    uploadId: str = Field(...)
+    urls: list[str] = Field(...)
+
+
+class VideoCompleteUploadRequestPart(BaseModel):
+    partNum: int = Field(...)
+    eTag: str = Field(...)
+
+
+class VideoCompleteUploadRequest(BaseModel):
+    uploadResults: list[VideoCompleteUploadRequestPart] = Field(...)
+
+
+class VideoCompleteUploadResponse(BaseModel):
+    message: str = Field(..., description="Confirmation message")
+
+
+class VideoStatusResponseEstimates(BaseModel):
+    cost: list[int] = Field(...)
+
+
+class VideoStatusResponseDownloadUrl(BaseModel):
+    url: str = Field(...)
+
+
+class VideoStatusResponse(BaseModel):
+    status: str = Field(...)
+    estimates: Optional[VideoStatusResponseEstimates] = Field(None)
+    progress: Optional[float] = Field(None)
+    message: Optional[str] = Field("")
+    download: Optional[VideoStatusResponseDownloadUrl] = Field(None)

comfy_api_nodes/nodes_gemini.py

@@ -3,8 +3,6 @@ API Nodes for Gemini Multimodal LLM Usage via Remote API
 See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
 """
 
-from __future__ import annotations
-
 import base64
 import json
 import os
@@ -12,7 +10,7 @@ import time
 import uuid
 from enum import Enum
 from io import BytesIO
-from typing import Literal, Optional
+from typing import Literal
 
 import torch
 from typing_extensions import override
@@ -20,18 +18,17 @@ from typing_extensions import override
 import folder_paths
 from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api.util import VideoCodec, VideoContainer
-from comfy_api_nodes.apis import (
+from comfy_api_nodes.apis.gemini_api import (
     GeminiContent,
     GeminiGenerateContentRequest,
     GeminiGenerateContentResponse,
-    GeminiInlineData,
-    GeminiMimeType,
-    GeminiPart,
-)
-from comfy_api_nodes.apis.gemini_api import (
     GeminiImageConfig,
     GeminiImageGenerateContentRequest,
     GeminiImageGenerationConfig,
+    GeminiInlineData,
+    GeminiMimeType,
+    GeminiPart,
+    GeminiRole,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
@@ -57,6 +54,7 @@ class GeminiModel(str, Enum):
     gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17"
     gemini_2_5_pro = "gemini-2.5-pro"
     gemini_2_5_flash = "gemini-2.5-flash"
+    gemini_3_0_pro = "gemini-3-pro-preview"
 
 
 class GeminiImageModel(str, Enum):
@@ -103,6 +101,16 @@ def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Litera
     Returns:
         List of response parts matching the requested type.
     """
+    if response.candidates is None:
+        if response.promptFeedback.blockReason:
+            feedback = response.promptFeedback
+            raise ValueError(
+                f"Gemini API blocked the request. Reason: {feedback.blockReason} ({feedback.blockReasonMessage})"
+            )
+        raise NotImplementedError(
+            "Gemini returned no response candidates. "
+            "Please report to ComfyUI repository with the example of workflow to reproduce this."
+        )
     parts = []
     for part in response.candidates[0].content.parts:
         if part_type == "text" and hasattr(part, "text") and part.text:
@@ -272,10 +280,10 @@ class GeminiNode(IO.ComfyNode):
         prompt: str,
         model: str,
         seed: int,
-        images: Optional[torch.Tensor] = None,
-        audio: Optional[Input.Audio] = None,
-        video: Optional[Input.Video] = None,
-        files: Optional[list[GeminiPart]] = None,
+        images: torch.Tensor | None = None,
+        audio: Input.Audio | None = None,
+        video: Input.Video | None = None,
+        files: list[GeminiPart] | None = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
 
@@ -300,7 +308,7 @@ class GeminiNode(IO.ComfyNode):
             data=GeminiGenerateContentRequest(
                 contents=[
                     GeminiContent(
-                        role="user",
+                        role=GeminiRole.user,
                         parts=parts,
                     )
                 ]
@@ -308,7 +316,6 @@ class GeminiNode(IO.ComfyNode):
             response_model=GeminiGenerateContentResponse,
         )
 
-        # Get result output
         output_text = get_text_from_response(response)
         if output_text:
             # Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button.
@@ -406,7 +413,7 @@ class GeminiInputFiles(IO.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, file: str, GEMINI_INPUT_FILES: Optional[list[GeminiPart]] = None) -> IO.NodeOutput:
+    def execute(cls, file: str, GEMINI_INPUT_FILES: list[GeminiPart] | None = None) -> IO.NodeOutput:
         """Loads and formats input files for Gemini API."""
         if GEMINI_INPUT_FILES is None:
             GEMINI_INPUT_FILES = []
@@ -421,7 +428,7 @@ class GeminiImage(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="GeminiImageNode",
-            display_name="Google Gemini Image",
+            display_name="Nano Banana (Google Gemini Image)",
             category="api node/image/Gemini",
             description="Edit images synchronously via Google API.",
             inputs=[
@@ -488,8 +495,8 @@ class GeminiImage(IO.ComfyNode):
         prompt: str,
         model: str,
         seed: int,
-        images: Optional[torch.Tensor] = None,
-        files: Optional[list[GeminiPart]] = None,
+        images: torch.Tensor | None = None,
+        files: list[GeminiPart] | None = None,
         aspect_ratio: str = "auto",
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
@@ -510,7 +517,7 @@ class GeminiImage(IO.ComfyNode):
             endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"),
             data=GeminiImageGenerateContentRequest(
                 contents=[
-                    GeminiContent(role="user", parts=parts),
+                    GeminiContent(role=GeminiRole.user, parts=parts),
                 ],
                 generationConfig=GeminiImageGenerationConfig(
                     responseModalities=["TEXT", "IMAGE"],

comfy_api_nodes/nodes_topaz.py

@@ -0,0 +1,421 @@
+import builtins
+from io import BytesIO
+
+import aiohttp
+import torch
+from typing_extensions import override
+
+from comfy_api.input.video_types import VideoInput
+from comfy_api.latest import IO, ComfyExtension
+from comfy_api_nodes.apis import topaz_api
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    get_fs_object_size,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    upload_images_to_comfyapi,
+    validate_container_format_is_mp4,
+)
+
+UPSCALER_MODELS_MAP = {
+    "Starlight (Astra) Fast": "slf-1",
+    "Starlight (Astra) Creative": "slc-1",
+}
+UPSCALER_VALUES_MAP = {
+    "FullHD (1080p)": 1920,
+    "4K (2160p)": 3840,
+}
+
+
+class TopazImageEnhance(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TopazImageEnhance",
+            display_name="Topaz Image Enhance",
+            category="api node/image/Topaz",
+            description="Industry-standard upscaling and image enhancement.",
+            inputs=[
+                IO.Combo.Input("model", options=["Reimagine"]),
+                IO.Image.Input("image"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Optional text prompt for creative upscaling guidance.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "subject_detection",
+                    options=["All", "Foreground", "Background"],
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "face_enhancement",
+                    default=True,
+                    optional=True,
+                    tooltip="Enhance faces (if present) during processing.",
+                ),
+                IO.Float.Input(
+                    "face_enhancement_creativity",
+                    default=0.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                    tooltip="Set the creativity level for face enhancement.",
+                ),
+                IO.Float.Input(
+                    "face_enhancement_strength",
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                    tooltip="Controls how sharp enhanced faces are relative to the background.",
+                ),
+                IO.Boolean.Input(
+                    "crop_to_fill",
+                    default=False,
+                    optional=True,
+                    tooltip="By default, the image is letterboxed when the output aspect ratio differs. "
+                    "Enable to crop the image to fill the output dimensions.",
+                ),
+                IO.Int.Input(
+                    "output_width",
+                    default=0,
+                    min=0,
+                    max=32000,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                    tooltip="Zero value means to calculate automatically (usually it will be original size or output_height if specified).",
+                ),
+                IO.Int.Input(
+                    "output_height",
+                    default=0,
+                    min=0,
+                    max=32000,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    optional=True,
+                    tooltip="Zero value means to output in the same height as original or output width.",
+                ),
+                IO.Int.Input(
+                    "creativity",
+                    default=3,
+                    min=1,
+                    max=9,
+                    step=1,
+                    display_mode=IO.NumberDisplay.slider,
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "face_preservation",
+                    default=True,
+                    optional=True,
+                    tooltip="Preserve subjects' facial identity.",
+                ),
+                IO.Boolean.Input(
+                    "color_preservation",
+                    default=True,
+                    optional=True,
+                    tooltip="Preserve the original colors.",
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: torch.Tensor,
+        prompt: str = "",
+        subject_detection: str = "All",
+        face_enhancement: bool = True,
+        face_enhancement_creativity: float = 1.0,
+        face_enhancement_strength: float = 0.8,
+        crop_to_fill: bool = False,
+        output_width: int = 0,
+        output_height: int = 0,
+        creativity: int = 3,
+        face_preservation: bool = True,
+        color_preservation: bool = True,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Only one input image is supported.")
+        download_url = await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png")
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/topaz/image/v1/enhance-gen/async", method="POST"),
+            response_model=topaz_api.ImageAsyncTaskResponse,
+            data=topaz_api.ImageEnhanceRequest(
+                model=model,
+                prompt=prompt,
+                subject_detection=subject_detection,
+                face_enhancement=face_enhancement,
+                face_enhancement_creativity=face_enhancement_creativity,
+                face_enhancement_strength=face_enhancement_strength,
+                crop_to_fill=crop_to_fill,
+                output_width=output_width if output_width else None,
+                output_height=output_height if output_height else None,
+                creativity=creativity,
+                face_preservation=str(face_preservation).lower(),
+                color_preservation=str(color_preservation).lower(),
+                source_url=download_url[0],
+                output_format="png",
+            ),
+            content_type="multipart/form-data",
+        )
+
+        await poll_op(
+            cls,
+            poll_endpoint=ApiEndpoint(path=f"/proxy/topaz/image/v1/status/{initial_response.process_id}"),
+            response_model=topaz_api.ImageStatusResponse,
+            status_extractor=lambda x: x.status,
+            progress_extractor=lambda x: getattr(x, "progress", 0),
+            price_extractor=lambda x: x.credits * 0.08,
+            poll_interval=8.0,
+            max_poll_attempts=160,
+            estimated_duration=60,
+        )
+
+        results = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/topaz/image/v1/download/{initial_response.process_id}"),
+            response_model=topaz_api.ImageDownloadResponse,
+            monitor_progress=False,
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(results.download_url))
+
+
+class TopazVideoEnhance(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TopazVideoEnhance",
+            display_name="Topaz Video Enhance",
+            category="api node/video/Topaz",
+            description="Breathe new life into video with powerful upscaling and recovery technology.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Boolean.Input("upscaler_enabled", default=True),
+                IO.Combo.Input("upscaler_model", options=list(UPSCALER_MODELS_MAP.keys())),
+                IO.Combo.Input("upscaler_resolution", options=list(UPSCALER_VALUES_MAP.keys())),
+                IO.Combo.Input(
+                    "upscaler_creativity",
+                    options=["low", "middle", "high"],
+                    default="low",
+                    tooltip="Creativity level (applies only to Starlight (Astra) Creative).",
+                    optional=True,
+                ),
+                IO.Boolean.Input("interpolation_enabled", default=False, optional=True),
+                IO.Combo.Input("interpolation_model", options=["apo-8"], default="apo-8", optional=True),
+                IO.Int.Input(
+                    "interpolation_slowmo",
+                    default=1,
+                    min=1,
+                    max=16,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Slow-motion factor applied to the input video. "
+                    "For example, 2 makes the output twice as slow and doubles the duration.",
+                    optional=True,
+                ),
+                IO.Int.Input(
+                    "interpolation_frame_rate",
+                    default=60,
+                    min=15,
+                    max=240,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Output frame rate.",
+                    optional=True,
+                ),
+                IO.Boolean.Input(
+                    "interpolation_duplicate",
+                    default=False,
+                    tooltip="Analyze the input for duplicate frames and remove them.",
+                    optional=True,
+                ),
+                IO.Float.Input(
+                    "interpolation_duplicate_threshold",
+                    default=0.01,
+                    min=0.001,
+                    max=0.1,
+                    step=0.001,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Detection sensitivity for duplicate frames.",
+                    optional=True,
+                ),
+                IO.Combo.Input(
+                    "dynamic_compression_level",
+                    options=["Low", "Mid", "High"],
+                    default="Low",
+                    tooltip="CQP level.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: VideoInput,
+        upscaler_enabled: bool,
+        upscaler_model: str,
+        upscaler_resolution: str,
+        upscaler_creativity: str = "low",
+        interpolation_enabled: bool = False,
+        interpolation_model: str = "apo-8",
+        interpolation_slowmo: int = 1,
+        interpolation_frame_rate: int = 60,
+        interpolation_duplicate: bool = False,
+        interpolation_duplicate_threshold: float = 0.01,
+        dynamic_compression_level: str = "Low",
+    ) -> IO.NodeOutput:
+        if upscaler_enabled is False and interpolation_enabled is False:
+            raise ValueError("There is nothing to do: both upscaling and interpolation are disabled.")
+        src_width, src_height = video.get_dimensions()
+        video_components = video.get_components()
+        src_frame_rate = int(video_components.frame_rate)
+        duration_sec = video.get_duration()
+        estimated_frames = int(duration_sec * src_frame_rate)
+        validate_container_format_is_mp4(video)
+        src_video_stream = video.get_stream_source()
+        target_width = src_width
+        target_height = src_height
+        target_frame_rate = src_frame_rate
+        filters = []
+        if upscaler_enabled:
+            target_width = UPSCALER_VALUES_MAP[upscaler_resolution]
+            target_height = UPSCALER_VALUES_MAP[upscaler_resolution]
+            filters.append(
+                topaz_api.VideoEnhancementFilter(
+                    model=UPSCALER_MODELS_MAP[upscaler_model],
+                    creativity=(upscaler_creativity if UPSCALER_MODELS_MAP[upscaler_model] == "slc-1" else None),
+                    isOptimizedMode=(True if UPSCALER_MODELS_MAP[upscaler_model] == "slc-1" else None),
+                ),
+            )
+        if interpolation_enabled:
+            target_frame_rate = interpolation_frame_rate
+            filters.append(
+                topaz_api.VideoFrameInterpolationFilter(
+                    model=interpolation_model,
+                    slowmo=interpolation_slowmo,
+                    fps=interpolation_frame_rate,
+                    duplicate=interpolation_duplicate,
+                    duplicate_threshold=interpolation_duplicate_threshold,
+                ),
+            )
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/topaz/video/", method="POST"),
+            response_model=topaz_api.CreateVideoResponse,
+            data=topaz_api.CreateVideoRequest(
+                source=topaz_api.CreateCreateVideoRequestSource(
+                    container="mp4",
+                    size=get_fs_object_size(src_video_stream),
+                    duration=int(duration_sec),
+                    frameCount=estimated_frames,
+                    frameRate=src_frame_rate,
+                    resolution=topaz_api.Resolution(width=src_width, height=src_height),
+                ),
+                filters=filters,
+                output=topaz_api.OutputInformationVideo(
+                    resolution=topaz_api.Resolution(width=target_width, height=target_height),
+                    frameRate=target_frame_rate,
+                    audioCodec="AAC",
+                    audioTransfer="Copy",
+                    dynamicCompressionLevel=dynamic_compression_level,
+                ),
+            ),
+            wait_label="Creating task",
+            final_label_on_success="Task created",
+        )
+        upload_res = await sync_op(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/topaz/video/{initial_res.requestId}/accept",
+                method="PATCH",
+            ),
+            response_model=topaz_api.VideoAcceptResponse,
+            wait_label="Preparing upload",
+            final_label_on_success="Upload started",
+        )
+        if len(upload_res.urls) > 1:
+            raise NotImplementedError(
+                "Large files are not currently supported. Please open an issue in the ComfyUI repository."
+            )
+        async with aiohttp.ClientSession(headers={"Content-Type": "video/mp4"}) as session:
+            if isinstance(src_video_stream, BytesIO):
+                src_video_stream.seek(0)
+                async with session.put(upload_res.urls[0], data=src_video_stream, raise_for_status=True) as res:
+                    upload_etag = res.headers["Etag"]
+            else:
+                with builtins.open(src_video_stream, "rb") as video_file:
+                    async with session.put(upload_res.urls[0], data=video_file, raise_for_status=True) as res:
+                        upload_etag = res.headers["Etag"]
+        await sync_op(
+            cls,
+            ApiEndpoint(
+                path=f"/proxy/topaz/video/{initial_res.requestId}/complete-upload",
+                method="PATCH",
+            ),
+            response_model=topaz_api.VideoCompleteUploadResponse,
+            data=topaz_api.VideoCompleteUploadRequest(
+                uploadResults=[
+                    topaz_api.VideoCompleteUploadRequestPart(
+                        partNum=1,
+                        eTag=upload_etag,
+                    ),
+                ],
+            ),
+            wait_label="Finalizing upload",
+            final_label_on_success="Upload completed",
+        )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/topaz/video/{initial_res.requestId}/status"),
+            response_model=topaz_api.VideoStatusResponse,
+            status_extractor=lambda x: x.status,
+            progress_extractor=lambda x: getattr(x, "progress", 0),
+            price_extractor=lambda x: (x.estimates.cost[0] * 0.08 if x.estimates and x.estimates.cost[0] else None),
+            poll_interval=10.0,
+            max_poll_attempts=320,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.download.url))
+
+
+class TopazExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TopazImageEnhance,
+            TopazVideoEnhance,
+        ]
+
+
+async def comfy_entrypoint() -> TopazExtension:
+    return TopazExtension()

comfy_api_nodes/util/client.py

@@ -77,9 +77,9 @@ class _PollUIState:
 
 
 _RETRY_STATUS = {408, 429, 500, 502, 503, 504}
-COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done"]
-FAILED_STATUSES = ["cancelled", "canceled", "fail", "failed", "error"]
-QUEUED_STATUSES = ["created", "queued", "queueing", "submitted"]
+COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done", "complete"]
+FAILED_STATUSES = ["cancelled", "canceled", "canceling", "fail", "failed", "error"]
+QUEUED_STATUSES = ["created", "queued", "queueing", "submitted", "initializing"]
 
 
 async def sync_op(
@@ -424,7 +424,8 @@ def _display_text(
     if status:
         display_lines.append(f"Status: {status.capitalize() if isinstance(status, str) else status}")
     if price is not None:
-        display_lines.append(f"Price: ${float(price):,.4f}")
+        p = f"{float(price):,.4f}".rstrip("0").rstrip(".")
+        display_lines.append(f"Price: ${p}")
     if text is not None:
         display_lines.append(text)
     if display_lines:

comfy_extras/nodes_easycache.py

@@ -11,13 +11,13 @@ if TYPE_CHECKING:
 
 def easycache_forward_wrapper(executor, *args, **kwargs):
     # get values from args
-    x: torch.Tensor = args[0]
     transformer_options: dict[str] = args[-1]
     if not isinstance(transformer_options, dict):
         transformer_options = kwargs.get("transformer_options")
         if not transformer_options:
             transformer_options = args[-2]
     easycache: EasyCacheHolder = transformer_options["easycache"]
+    x: torch.Tensor = args[0][:, :easycache.output_channels]
     sigmas = transformer_options["sigmas"]
     uuids = transformer_options["uuids"]
     if sigmas is not None and easycache.is_past_end_timestep(sigmas):
@@ -82,13 +82,13 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
 
 def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
     # get values from args
-    x: torch.Tensor = args[0]
     timestep: float = args[1]
     model_options: dict[str] = args[2]
     easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"]
     if easycache.is_past_end_timestep(timestep):
         return executor(*args, **kwargs)
     # prepare next x_prev
+    x: torch.Tensor = args[0][:, :easycache.output_channels]
     next_x_prev = x
     input_change = None
     do_easycache = easycache.should_do_easycache(timestep)
@@ -173,7 +173,7 @@ def easycache_sample_wrapper(executor, *args, **kwargs):
 
 
 class EasyCacheHolder:
-    def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False):
+    def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False, output_channels: int=None):
         self.name = "EasyCache"
         self.reuse_threshold = reuse_threshold
         self.start_percent = start_percent
@@ -202,6 +202,7 @@ class EasyCacheHolder:
         self.allow_mismatch = True
         self.cut_from_start = True
         self.state_metadata = None
+        self.output_channels = output_channels
 
     def is_past_end_timestep(self, timestep: float) -> bool:
         return not (timestep[0] > self.end_t).item()
@@ -264,7 +265,7 @@ class EasyCacheHolder:
                     else:
                         slicing.append(slice(None))
                 batch_slice = batch_slice + slicing
-            x[batch_slice] += self.uuid_cache_diffs[uuid].to(x.device)
+            x[tuple(batch_slice)] += self.uuid_cache_diffs[uuid].to(x.device)
         return x
 
     def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]):
@@ -283,7 +284,7 @@ class EasyCacheHolder:
                 else:
                     slicing.append(slice(None))
                 skip_dim = False
-            x = x[slicing]
+            x = x[tuple(slicing)]
         diff = output - x
         batch_offset = diff.shape[0] // len(uuids)
         for i, uuid in enumerate(uuids):
@@ -323,7 +324,7 @@ class EasyCacheHolder:
         return self
 
     def clone(self):
-        return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose)
+        return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose, output_channels=self.output_channels)
 
 
 class EasyCacheNode(io.ComfyNode):
@@ -350,7 +351,7 @@ class EasyCacheNode(io.ComfyNode):
     @classmethod
     def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput:
         model = model.clone()
-        model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose)
+        model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose, output_channels=model.model.latent_format.latent_channels)
         model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "easycache", easycache_sample_wrapper)
         model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, "easycache", easycache_calc_cond_batch_wrapper)
         model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "easycache", easycache_forward_wrapper)
@@ -358,7 +359,7 @@ class EasyCacheNode(io.ComfyNode):
 
 
 class LazyCacheHolder:
-    def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False):
+    def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False, output_channels: int=None):
         self.name = "LazyCache"
         self.reuse_threshold = reuse_threshold
         self.start_percent = start_percent
@@ -382,6 +383,7 @@ class LazyCacheHolder:
         self.approx_output_change_rates = []
         self.total_steps_skipped = 0
         self.state_metadata = None
+        self.output_channels = output_channels
 
     def has_cache_diff(self) -> bool:
         return self.cache_diff is not None
@@ -456,7 +458,7 @@ class LazyCacheHolder:
         return self
 
     def clone(self):
-        return LazyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose)
+        return LazyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose, output_channels=self.output_channels)
 
 class LazyCacheNode(io.ComfyNode):
     @classmethod
@@ -482,7 +484,7 @@ class LazyCacheNode(io.ComfyNode):
     @classmethod
     def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput:
         model = model.clone()
-        model.model_options["transformer_options"]["easycache"] = LazyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose)
+        model.model_options["transformer_options"]["easycache"] = LazyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose, output_channels=model.model.latent_format.latent_channels)
         model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "lazycache", easycache_sample_wrapper)
         model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "lazycache", lazycache_predict_noise_wrapper)
         return io.NodeOutput(model)

comfy_extras/nodes_hunyuan3d.py

@@ -7,63 +7,79 @@ from comfy.ldm.modules.diffusionmodules.mmdit import get_1d_sincos_pos_embed_fro
 import folder_paths
 import comfy.model_management
 from comfy.cli_args import args
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO, Types
+from comfy_api.latest._util import MESH, VOXEL  # only for backward compatibility if someone import it from this file (will be removed later) # noqa
 
-class EmptyLatentHunyuan3Dv2:
+
+class EmptyLatentHunyuan3Dv2(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "resolution": ("INT", {"default": 3072, "min": 1, "max": 8192}),
-                "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}),
-            }
-        }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="EmptyLatentHunyuan3Dv2",
+            category="latent/3d",
+            inputs=[
+                IO.Int.Input("resolution", default=3072, min=1, max=8192),
+                IO.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."),
+            ],
+            outputs=[
+                IO.Latent.Output(),
+            ]
+        )
 
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "generate"
-
-    CATEGORY = "latent/3d"
-
-    def generate(self, resolution, batch_size):
+    @classmethod
+    def execute(cls, resolution, batch_size) -> IO.NodeOutput:
         latent = torch.zeros([batch_size, 64, resolution], device=comfy.model_management.intermediate_device())
-        return ({"samples": latent, "type": "hunyuan3dv2"}, )
+        return IO.NodeOutput({"samples": latent, "type": "hunyuan3dv2"})
 
-class Hunyuan3Dv2Conditioning:
+    generate = execute  # TODO: remove
+
+
+class Hunyuan3Dv2Conditioning(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"clip_vision_output": ("CLIP_VISION_OUTPUT",),
-                             }}
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Hunyuan3Dv2Conditioning",
+            category="conditioning/video_models",
+            inputs=[
+                IO.ClipVisionOutput.Input("clip_vision_output"),
+            ],
+            outputs=[
+                IO.Conditioning.Output(display_name="positive"),
+                IO.Conditioning.Output(display_name="negative"),
+            ]
+        )
 
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
-    RETURN_NAMES = ("positive", "negative")
-
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/video_models"
-
-    def encode(self, clip_vision_output):
+    @classmethod
+    def execute(cls, clip_vision_output) -> IO.NodeOutput:
         embeds = clip_vision_output.last_hidden_state
         positive = [[embeds, {}]]
         negative = [[torch.zeros_like(embeds), {}]]
-        return (positive, negative)
+        return IO.NodeOutput(positive, negative)
+
+    encode = execute  # TODO: remove
 
 
-class Hunyuan3Dv2ConditioningMultiView:
+class Hunyuan3Dv2ConditioningMultiView(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {},
-                "optional": {"front": ("CLIP_VISION_OUTPUT",),
-                             "left": ("CLIP_VISION_OUTPUT",),
-                             "back": ("CLIP_VISION_OUTPUT",),
-                             "right": ("CLIP_VISION_OUTPUT",), }}
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Hunyuan3Dv2ConditioningMultiView",
+            category="conditioning/video_models",
+            inputs=[
+                IO.ClipVisionOutput.Input("front", optional=True),
+                IO.ClipVisionOutput.Input("left", optional=True),
+                IO.ClipVisionOutput.Input("back", optional=True),
+                IO.ClipVisionOutput.Input("right", optional=True),
+            ],
+            outputs=[
+                IO.Conditioning.Output(display_name="positive"),
+                IO.Conditioning.Output(display_name="negative"),
+            ]
+        )
 
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
-    RETURN_NAMES = ("positive", "negative")
-
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/video_models"
-
-    def encode(self, front=None, left=None, back=None, right=None):
+    @classmethod
+    def execute(cls, front=None, left=None, back=None, right=None) -> IO.NodeOutput:
         all_embeds = [front, left, back, right]
         out = []
         pos_embeds = None
@@ -76,29 +92,35 @@ class Hunyuan3Dv2ConditioningMultiView:
         embeds = torch.cat(out, dim=1)
         positive = [[embeds, {}]]
         negative = [[torch.zeros_like(embeds), {}]]
-        return (positive, negative)
+        return IO.NodeOutput(positive, negative)
+
+    encode = execute  # TODO: remove
 
 
-class VOXEL:
-    def __init__(self, data):
-        self.data = data
-
-class VAEDecodeHunyuan3D:
+class VAEDecodeHunyuan3D(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"samples": ("LATENT", ),
-                             "vae": ("VAE", ),
-                             "num_chunks": ("INT", {"default": 8000, "min": 1000, "max": 500000}),
-                             "octree_resolution": ("INT", {"default": 256, "min": 16, "max": 512}),
-                             }}
-    RETURN_TYPES = ("VOXEL",)
-    FUNCTION = "decode"
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VAEDecodeHunyuan3D",
+            category="latent/3d",
+            inputs=[
+                IO.Latent.Input("samples"),
+                IO.Vae.Input("vae"),
+                IO.Int.Input("num_chunks", default=8000, min=1000, max=500000),
+                IO.Int.Input("octree_resolution", default=256, min=16, max=512),
+            ],
+            outputs=[
+                IO.Voxel.Output(),
+            ]
+        )
 
-    CATEGORY = "latent/3d"
+    @classmethod
+    def execute(cls, vae, samples, num_chunks, octree_resolution) -> IO.NodeOutput:
+        voxels = Types.VOXEL(vae.decode(samples["samples"], vae_options={"num_chunks": num_chunks, "octree_resolution": octree_resolution}))
+        return IO.NodeOutput(voxels)
+
+    decode = execute  # TODO: remove
 
-    def decode(self, vae, samples, num_chunks, octree_resolution):
-        voxels = VOXEL(vae.decode(samples["samples"], vae_options={"num_chunks": num_chunks, "octree_resolution": octree_resolution}))
-        return (voxels, )
 
 def voxel_to_mesh(voxels, threshold=0.5, device=None):
     if device is None:
@@ -396,24 +418,24 @@ def voxel_to_mesh_surfnet(voxels, threshold=0.5, device=None):
 
     return final_vertices, faces
 
-class MESH:
-    def __init__(self, vertices, faces):
-        self.vertices = vertices
-        self.faces = faces
 
-
-class VoxelToMeshBasic:
+class VoxelToMeshBasic(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"voxel": ("VOXEL", ),
-                             "threshold": ("FLOAT", {"default": 0.6, "min": -1.0, "max": 1.0, "step": 0.01}),
-                             }}
-    RETURN_TYPES = ("MESH",)
-    FUNCTION = "decode"
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VoxelToMeshBasic",
+            category="3d",
+            inputs=[
+                IO.Voxel.Input("voxel"),
+                IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                IO.Mesh.Output(),
+            ]
+        )
 
-    CATEGORY = "3d"
-
-    def decode(self, voxel, threshold):
+    @classmethod
+    def execute(cls, voxel, threshold) -> IO.NodeOutput:
         vertices = []
         faces = []
         for x in voxel.data:
@@ -421,21 +443,29 @@ class VoxelToMeshBasic:
             vertices.append(v)
             faces.append(f)
 
-        return (MESH(torch.stack(vertices), torch.stack(faces)), )
+        return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces)))
 
-class VoxelToMesh:
+    decode = execute  # TODO: remove
+
+
+class VoxelToMesh(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"voxel": ("VOXEL", ),
-                             "algorithm": (["surface net", "basic"], ),
-                             "threshold": ("FLOAT", {"default": 0.6, "min": -1.0, "max": 1.0, "step": 0.01}),
-                             }}
-    RETURN_TYPES = ("MESH",)
-    FUNCTION = "decode"
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="VoxelToMesh",
+            category="3d",
+            inputs=[
+                IO.Voxel.Input("voxel"),
+                IO.Combo.Input("algorithm", options=["surface net", "basic"]),
+                IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                IO.Mesh.Output(),
+            ]
+        )
 
-    CATEGORY = "3d"
-
-    def decode(self, voxel, algorithm, threshold):
+    @classmethod
+    def execute(cls, voxel, algorithm, threshold) -> IO.NodeOutput:
         vertices = []
         faces = []
 
@@ -449,7 +479,9 @@ class VoxelToMesh:
             vertices.append(v)
             faces.append(f)
 
-        return (MESH(torch.stack(vertices), torch.stack(faces)), )
+        return IO.NodeOutput(Types.MESH(torch.stack(vertices), torch.stack(faces)))
+
+    decode = execute  # TODO: remove
 
 
 def save_glb(vertices, faces, filepath, metadata=None):
@@ -581,31 +613,32 @@ def save_glb(vertices, faces, filepath, metadata=None):
     return filepath
 
 
-class SaveGLB:
+class SaveGLB(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"mesh": ("MESH", ),
-                             "filename_prefix": ("STRING", {"default": "mesh/ComfyUI"}), },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, }
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveGLB",
+            category="3d",
+            is_output_node=True,
+            inputs=[
+                IO.Mesh.Input("mesh"),
+                IO.String.Input("filename_prefix", default="mesh/ComfyUI"),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo]
+        )
 
-    RETURN_TYPES = ()
-    FUNCTION = "save"
-
-    OUTPUT_NODE = True
-
-    CATEGORY = "3d"
-
-    def save(self, mesh, filename_prefix, prompt=None, extra_pnginfo=None):
+    @classmethod
+    def execute(cls, mesh, filename_prefix) -> IO.NodeOutput:
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
         results = []
 
         metadata = {}
         if not args.disable_metadata:
-            if prompt is not None:
-                metadata["prompt"] = json.dumps(prompt)
-            if extra_pnginfo is not None:
-                for x in extra_pnginfo:
-                    metadata[x] = json.dumps(extra_pnginfo[x])
+            if cls.hidden.prompt is not None:
+                metadata["prompt"] = json.dumps(cls.hidden.prompt)
+            if cls.hidden.extra_pnginfo is not None:
+                for x in cls.hidden.extra_pnginfo:
+                    metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
 
         for i in range(mesh.vertices.shape[0]):
             f = f"{filename}_{counter:05}_.glb"
@@ -616,15 +649,22 @@ class SaveGLB:
                 "type": "output"
             })
             counter += 1
-        return {"ui": {"3d": results}}
+        return IO.NodeOutput(ui={"3d": results})
 
 
-NODE_CLASS_MAPPINGS = {
-    "EmptyLatentHunyuan3Dv2": EmptyLatentHunyuan3Dv2,
-    "Hunyuan3Dv2Conditioning": Hunyuan3Dv2Conditioning,
-    "Hunyuan3Dv2ConditioningMultiView": Hunyuan3Dv2ConditioningMultiView,
-    "VAEDecodeHunyuan3D": VAEDecodeHunyuan3D,
-    "VoxelToMeshBasic": VoxelToMeshBasic,
-    "VoxelToMesh": VoxelToMesh,
-    "SaveGLB": SaveGLB,
-}
+class Hunyuan3dExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            EmptyLatentHunyuan3Dv2,
+            Hunyuan3Dv2Conditioning,
+            Hunyuan3Dv2ConditioningMultiView,
+            VAEDecodeHunyuan3D,
+            VoxelToMeshBasic,
+            VoxelToMesh,
+            SaveGLB,
+        ]
+
+
+async def comfy_entrypoint() -> Hunyuan3dExtension:
+    return Hunyuan3dExtension()

comfy_extras/nodes_nop.py

@@ -0,0 +1,39 @@
+from comfy_api.latest import ComfyExtension, io
+from typing_extensions import override
+# If you write a node that is so useless that it breaks ComfyUI it will be featured in this exclusive list
+
+# "native" block swap nodes are placebo at best and break the ComfyUI memory management system.
+# They are also considered harmful because instead of users reporting issues with the built in
+# memory management they install these stupid nodes and complain even harder. Now it completely
+# breaks with some of the new ComfyUI memory optimizations so I have made the decision to NOP it
+# out of all workflows.
+class wanBlockSwap(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="wanBlockSwap",
+            category="",
+            description="NOP",
+            inputs=[
+                io.Model.Input("model"),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+            is_deprecated=True,
+        )
+
+    @classmethod
+    def execute(cls, model) -> io.NodeOutput:
+        return io.NodeOutput(model)
+
+
+class NopExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            wanBlockSwap
+        ]
+
+async def comfy_entrypoint() -> NopExtension:
+    return NopExtension()

comfy_extras/nodes_preview_any.py

@@ -39,5 +39,5 @@ NODE_CLASS_MAPPINGS = {
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "PreviewAny": "Preview Any",
+    "PreviewAny": "Preview as Text",
 }

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.68"
+__version__ = "0.3.70"

nodes.py

@@ -2330,6 +2330,7 @@ async def init_builtin_extra_nodes():
         "nodes_easycache.py",
         "nodes_audio_encoder.py",
         "nodes_rope.py",
+        "nodes_nop.py",
     ]
 
     import_failed = []
@@ -2358,6 +2359,7 @@ async def init_builtin_api_nodes():
         "nodes_pika.py",
         "nodes_runway.py",
         "nodes_sora.py",
+        "nodes_topaz.py",
         "nodes_tripo.py",
         "nodes_moonvalley.py",
         "nodes_rodin.py",

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.68"
+version = "0.3.70"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"
@@ -24,7 +24,7 @@ lint.select = [
 exclude = ["*.ipynb", "**/generated/*.pyi"]
 
 [tool.pylint]
-master.py-version = "3.9"
+master.py-version = "3.10"
 master.extension-pkg-allow-list = [
   "pydantic",
 ]

requirements.txt

@@ -1,5 +1,5 @@
-comfyui-frontend-package==1.30.4
-comfyui-workflow-templates==0.2.11
+comfyui-frontend-package==1.30.6
+comfyui-workflow-templates==0.3.1
 comfyui-embedded-docs==0.3.1
 torch
 torchsde

server.py

@@ -2,6 +2,7 @@ import os
 import sys
 import asyncio
 import traceback
+import time
 
 import nodes
 import folder_paths
@@ -29,7 +30,7 @@ import comfy.model_management
 from comfy_api import feature_flags
 import node_helpers
 from comfyui_version import __version__
-from app.frontend_management import FrontendManager
+from app.frontend_management import FrontendManager, parse_version
 from comfy_api.internal import _ComfyNodeInternal
 
 from app.user_manager import UserManager
@@ -733,6 +734,7 @@ class PromptServer():
                     for sensitive_val in execution.SENSITIVE_EXTRA_DATA_KEYS:
                         if sensitive_val in extra_data:
                             sensitive[sensitive_val] = extra_data.pop(sensitive_val)
+                    extra_data["create_time"] = int(time.time() * 1000)  # timestamp in milliseconds
                     self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive))
                     response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]}
                     return web.json_response(response)
@@ -847,11 +849,31 @@ class PromptServer():
         for name, dir in nodes.EXTENSION_WEB_DIRS.items():
             self.app.add_routes([web.static('/extensions/' + name, dir)])
 
-        workflow_templates_path = FrontendManager.templates_path()
-        if workflow_templates_path:
-            self.app.add_routes([
-                web.static('/templates', workflow_templates_path)
-            ])
+        installed_templates_version = FrontendManager.get_installed_templates_version()
+        use_legacy_templates = True
+        if installed_templates_version:
+            try:
+                use_legacy_templates = (
+                    parse_version(installed_templates_version)
+                    < parse_version("0.3.0")
+                )
+            except Exception as exc:
+                logging.warning(
+                    "Unable to parse templates version '%s': %s",
+                    installed_templates_version,
+                    exc,
+                )
+
+        if use_legacy_templates:
+            workflow_templates_path = FrontendManager.legacy_templates_path()
+            if workflow_templates_path:
+                self.app.add_routes([
+                    web.static('/templates', workflow_templates_path)
+                ])
+        else:
+            handler = FrontendManager.template_asset_handler()
+            if handler:
+                self.app.router.add_get("/templates/{path:.*}", handler)
 
         # Serve embedded documentation from the package
         embedded_docs_path = FrontendManager.embedded_docs_path()