[pull] master from comfyanonymous:master

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

@@ -18,9 +18,9 @@ jobs:
     uses: ./.github/workflows/stable-release.yml
     with:
       git_tag: ${{ inputs.git_tag }}
-      cache_tag: "cu129"
+      cache_tag: "cu130"
       python_minor: "13"
-      python_patch: "6"
+      python_patch: "9"
       rel_name: "nvidia"
       rel_extra_name: ""
       test_release: true

README.md

@@ -176,6 +176,8 @@ Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
+Update your Nvidia drivers if it doesn't start.
+
 #### Alternative Downloads:
 
 [Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)

comfy_api_nodes/nodes_ltxv.py

@@ -0,0 +1,191 @@
+from io import BytesIO
+from typing import Optional
+
+import torch
+from pydantic import BaseModel, Field
+from typing_extensions import override
+
+from comfy_api.input_impl import VideoFromFile
+from comfy_api.latest import IO, ComfyExtension
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    get_number_of_images,
+    sync_op_raw,
+    upload_images_to_comfyapi,
+    validate_string,
+)
+
+MODELS_MAP = {
+    "LTX-2 (Pro)": "ltx-2-pro",
+    "LTX-2 (Fast)": "ltx-2-fast",
+}
+
+
+class ExecuteTaskRequest(BaseModel):
+    prompt: str = Field(...)
+    model: str = Field(...)
+    duration: int = Field(...)
+    resolution: str = Field(...)
+    fps: Optional[int] = Field(25)
+    generate_audio: Optional[bool] = Field(True)
+    image_uri: Optional[str] = Field(None)
+
+
+class TextToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LtxvApiTextToVideo",
+            display_name="LTXV Text To Video",
+            category="api node/video/LTXV",
+            description="Professional-quality videos with customizable duration and resolution.",
+            inputs=[
+                IO.Combo.Input("model", options=list(MODELS_MAP.keys())),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                ),
+                IO.Combo.Input("duration", options=[6, 8, 10], default=8),
+                IO.Combo.Input(
+                    "resolution",
+                    options=[
+                        "1920x1080",
+                        "2560x1440",
+                        "3840x2160",
+                    ],
+                ),
+                IO.Combo.Input("fps", options=[25, 50], default=25),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                ),
+            ],
+            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,
+        model: str,
+        prompt: str,
+        duration: int,
+        resolution: str,
+        fps: int = 25,
+        generate_audio: bool = False,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=10000)
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint("/proxy/ltx/v1/text-to-video", "POST"),
+            data=ExecuteTaskRequest(
+                prompt=prompt,
+                model=MODELS_MAP[model],
+                duration=duration,
+                resolution=resolution,
+                fps=fps,
+                generate_audio=generate_audio,
+            ),
+            as_binary=True,
+            max_retries=1,
+        )
+        return IO.NodeOutput(VideoFromFile(BytesIO(response)))
+
+
+class ImageToVideoNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="LtxvApiImageToVideo",
+            display_name="LTXV Image To Video",
+            category="api node/video/LTXV",
+            description="Professional-quality videos with customizable duration and resolution based on start image.",
+            inputs=[
+                IO.Image.Input("image", tooltip="First frame to be used for the video."),
+                IO.Combo.Input("model", options=list(MODELS_MAP.keys())),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                ),
+                IO.Combo.Input("duration", options=[6, 8, 10], default=8),
+                IO.Combo.Input(
+                    "resolution",
+                    options=[
+                        "1920x1080",
+                        "2560x1440",
+                        "3840x2160",
+                    ],
+                ),
+                IO.Combo.Input("fps", options=[25, 50], default=25),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="When true, the generated video will include AI-generated audio matching the scene.",
+                ),
+            ],
+            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,
+        image: torch.Tensor,
+        model: str,
+        prompt: str,
+        duration: int,
+        resolution: str,
+        fps: int = 25,
+        generate_audio: bool = False,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, min_length=1, max_length=10000)
+        if get_number_of_images(image) != 1:
+            raise ValueError("Currently only one input image is supported.")
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint("/proxy/ltx/v1/image-to-video", "POST"),
+            data=ExecuteTaskRequest(
+                image_uri=(await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png"))[0],
+                prompt=prompt,
+                model=MODELS_MAP[model],
+                duration=duration,
+                resolution=resolution,
+                fps=fps,
+                generate_audio=generate_audio,
+            ),
+            as_binary=True,
+            max_retries=1,
+        )
+        return IO.NodeOutput(VideoFromFile(BytesIO(response)))
+
+
+class LtxvApiExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            TextToVideoNode,
+            ImageToVideoNode,
+        ]
+
+
+async def comfy_entrypoint() -> LtxvApiExtension:
+    return LtxvApiExtension()

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.66"
+__version__ = "0.3.67"

execution.py

@@ -1116,7 +1116,7 @@ class ExecutionStatus(NamedTuple):
         messages: List[str]
 
     def task_done(self, item_id, history_result,
-                  status: Optional['PromptQueue.ExecutionStatus']):
+                  status: Optional['PromptQueue.ExecutionStatus'], process_item=None):
         with self.mutex:
             prompt = self.currently_running.pop(item_id)
             if len(self.history) > MAXIMUM_HISTORY_SIZE:
@@ -1126,10 +1126,8 @@ def task_done(self, item_id, history_result,
             if status is not None:
                 status_dict = copy.deepcopy(status._asdict())
 
-            # Remove sensitive data from extra_data before storing in history
-            for sensitive_val in SENSITIVE_EXTRA_DATA_KEYS:
-                if sensitive_val in prompt[3]:
-                    prompt[3].pop(sensitive_val)
+            if process_item is not None:
+                prompt = process_item(prompt)
 
             self.history[prompt[1]] = {
                 "prompt": prompt,

main.py

@@ -192,14 +192,21 @@ def prompt_worker(q, server_instance):
             prompt_id = item[1]
             server_instance.last_prompt_id = prompt_id
 
-            e.execute(item[2], prompt_id, item[3], item[4])
+            sensitive = item[5]
+            extra_data = item[3].copy()
+            for k in sensitive:
+                extra_data[k] = sensitive[k]
+
+            e.execute(item[2], prompt_id, extra_data, item[4])
             need_gc = True
+
+            remove_sensitive = lambda prompt: prompt[:5] + prompt[6:]
             q.task_done(item_id,
                         e.history_result,
                         status=execution.PromptQueue.ExecutionStatus(
                             status_str='success' if e.success else 'error',
                             completed=e.success,
-                            messages=e.status_messages))
+                            messages=e.status_messages), process_item=remove_sensitive)
             if server_instance.client_id is not None:
                 server_instance.send_sync("executing", {"node": None, "prompt_id": prompt_id}, server_instance.client_id)
 

nodes.py

@@ -2349,6 +2349,7 @@ async def init_builtin_api_nodes():
         "nodes_kling.py",
         "nodes_bfl.py",
         "nodes_bytedance.py",
+        "nodes_ltxv.py",
         "nodes_luma.py",
         "nodes_recraft.py",
         "nodes_pixverse.py",

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.66"
+version = "0.3.67"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,5 +1,5 @@
-comfyui-frontend-package==1.28.7
-comfyui-workflow-templates==0.2.2
+comfyui-frontend-package==1.28.8
+comfyui-workflow-templates==0.2.4
 comfyui-embedded-docs==0.3.0
 torch
 torchsde

server.py

@@ -691,8 +691,9 @@ async def get_history_prompt_id(request):
         async def get_queue(request):
             queue_info = {}
             current_queue = self.prompt_queue.get_current_queue_volatile()
-            queue_info['queue_running'] = current_queue[0]
-            queue_info['queue_pending'] = current_queue[1]
+            remove_sensitive = lambda queue: [x[:5] for x in queue]
+            queue_info['queue_running'] = remove_sensitive(current_queue[0])
+            queue_info['queue_pending'] = remove_sensitive(current_queue[1])
             return web.json_response(queue_info)
 
         @routes.post("/prompt")
@@ -728,7 +729,11 @@ async def post_prompt(request):
                     extra_data["client_id"] = json_data["client_id"]
                 if valid[0]:
                     outputs_to_execute = valid[2]
-                    self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
+                    sensitive = {}
+                    for sensitive_val in execution.SENSITIVE_EXTRA_DATA_KEYS:
+                        if sensitive_val in extra_data:
+                            sensitive[sensitive_val] = extra_data.pop(sensitive_val)
+                    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)
                 else: