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Merge branch 'master' into dr-support-pip-cm

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Dr.Lt.Data 2025-10-05 07:36:33 +09:00
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30
README.md
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@ -206,14 +206,32 @@ Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
Put your VAE in: models/vae
### AMD GPUs (Linux only)
### AMD GPUs (Linux)
AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4```
This is the command to install the nightly with ROCm 6.4 which might have some performance improvements:
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/rocm6.4```
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.0```
### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.
These have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware.
RDNA 3 (RX 7000 series):
```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-dgpu/```
RDNA 3.5 (Strix halo/Ryzen AI Max+ 365):
```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/```
RDNA 4 (RX 9000 series):
```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/```
### Intel GPUs (Windows and Linux)
@ -270,12 +288,6 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve
> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
#### DirectML (AMD Cards on Windows)
This is very badly supported and is not recommended. There are some unofficial builds of pytorch ROCm on windows that exist that will give you a much better experience than this. This readme will be updated once official pytorch ROCm builds for windows come out.
```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
#### Ascend NPUs
For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the [installation](https://ascend.github.io/docs/sources/ascend/quick_install.html) page. Here's a step-by-step guide tailored to your platform and installation method:

3
comfy_api_nodes/nodes_gemini.py
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@ -39,6 +39,7 @@ from comfy_api_nodes.apinode_utils import (
tensor_to_base64_string,
bytesio_to_image_tensor,
)
from comfy_api.util import VideoContainer, VideoCodec
GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini"
@ -310,7 +311,7 @@ class GeminiNode(ComfyNodeABC):
Returns:
List of GeminiPart objects containing the encoded video.
"""
from comfy_api.util import VideoContainer, VideoCodec
base_64_string = video_to_base64_string(
video_input,
container_format=VideoContainer.MP4,

49
comfy_api_nodes/nodes_moonvalley.py
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@ -2,11 +2,7 @@ import logging
from typing import Any, Callable, Optional, TypeVar
import torch
from typing_extensions import override
from comfy_api_nodes.util.validation_utils import (
get_image_dimensions,
validate_image_dimensions,
)
from comfy_api_nodes.util.validation_utils import validate_image_dimensions
from comfy_api_nodes.apis import (
MoonvalleyTextToVideoRequest,
@ -132,47 +128,6 @@ def validate_prompts(
return True
def validate_input_media(width, height, with_frame_conditioning, num_frames_in=None):
# inference validation
# T = num_frames
# in all cases, the following must be true: T divisible by 16 and H,W by 8. in addition...
# with image conditioning: H*W must be divisible by 8192
# without image conditioning: T divisible by 32
if num_frames_in and not num_frames_in % 16 == 0:
return False, ("The input video total frame count must be divisible by 16!")
if height % 8 != 0 or width % 8 != 0:
return False, (
f"Height ({height}) and width ({width}) must be " "divisible by 8"
)
if with_frame_conditioning:
if (height * width) % 8192 != 0:
return False, (
f"Height * width ({height * width}) must be "
"divisible by 8192 for frame conditioning"
)
else:
if num_frames_in and not num_frames_in % 32 == 0:
return False, ("The input video total frame count must be divisible by 32!")
def validate_input_image(
image: torch.Tensor, with_frame_conditioning: bool = False
) -> None:
"""
Validates the input image adheres to the expectations of the API:
- The image resolution should not be less than 300*300px
- The aspect ratio of the image should be between 1:2.5 ~ 2.5:1
"""
height, width = get_image_dimensions(image)
validate_input_media(width, height, with_frame_conditioning)
validate_image_dimensions(
image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH
)
def validate_video_to_video_input(video: VideoInput) -> VideoInput:
"""
Validates and processes video input for Moonvalley Video-to-Video generation.
@ -499,7 +454,7 @@ class MoonvalleyImg2VideoNode(comfy_io.ComfyNode):
seed: int,
steps: int,
) -> comfy_io.NodeOutput:
validate_input_image(image, True)
validate_image_dimensions(image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH)
validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
width_height = parse_width_height_from_res(resolution)

941
comfy_api_nodes/nodes_rodin.py
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149
comfy_extras/nodes_stable3d.py
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@ -1,6 +1,8 @@
import torch
import nodes
import comfy.utils
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
def camera_embeddings(elevation, azimuth):
elevation = torch.as_tensor([elevation])
@ -20,26 +22,31 @@ def camera_embeddings(elevation, azimuth):
return embeddings
class StableZero123_Conditioning:
class StableZero123_Conditioning(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
def define_schema(cls):
return io.Schema(
node_id="StableZero123_Conditioning",
category="conditioning/3d_models",
inputs=[
io.ClipVision.Input("clip_vision"),
io.Image.Input("init_image"),
io.Vae.Input("vae"),
io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent")
]
)
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth):
@classmethod
def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth) -> io.NodeOutput:
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
@ -51,30 +58,35 @@ class StableZero123_Conditioning:
positive = [[cond, {"concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
return io.NodeOutput(positive, negative, {"samples":latent})
class StableZero123_Conditioning_Batched:
class StableZero123_Conditioning_Batched(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
"azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
def define_schema(cls):
return io.Schema(
node_id="StableZero123_Conditioning_Batched",
category="conditioning/3d_models",
inputs=[
io.ClipVision.Input("clip_vision"),
io.Image.Input("init_image"),
io.Vae.Input("vae"),
io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent")
]
)
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment):
@classmethod
def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment) -> io.NodeOutput:
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
@ -93,27 +105,32 @@ class StableZero123_Conditioning_Batched:
positive = [[cond, {"concat_latent_image": t}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]]
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
return io.NodeOutput(positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
class SV3D_Conditioning:
class SV3D_Conditioning(io.ComfyNode):
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",),
"vae": ("VAE",),
"width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}),
"elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
def define_schema(cls):
return io.Schema(
node_id="SV3D_Conditioning",
category="conditioning/3d_models",
inputs=[
io.ClipVision.Input("clip_vision"),
io.Image.Input("init_image"),
io.Vae.Input("vae"),
io.Int.Input("width", default=576, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("height", default=576, min=16, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("video_frames", default=21, min=1, max=4096),
io.Float.Input("elevation", default=0.0, min=-90.0, max=90.0, step=0.1, round=False)
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent")
]
)
FUNCTION = "encode"
CATEGORY = "conditioning/3d_models"
def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation):
@classmethod
def execute(cls, clip_vision, init_image, vae, width, height, video_frames, elevation) -> io.NodeOutput:
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
@ -133,11 +150,17 @@ class SV3D_Conditioning:
positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]]
latent = torch.zeros([video_frames, 4, height // 8, width // 8])
return (positive, negative, {"samples":latent})
return io.NodeOutput(positive, negative, {"samples":latent})
NODE_CLASS_MAPPINGS = {
"StableZero123_Conditioning": StableZero123_Conditioning,
"StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched,
"SV3D_Conditioning": SV3D_Conditioning,
}
class Stable3DExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
StableZero123_Conditioning,
StableZero123_Conditioning_Batched,
SV3D_Conditioning,
]
async def comfy_entrypoint() -> Stable3DExtension:
return Stable3DExtension()

2
pyproject.toml
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@ -70,7 +70,5 @@ messages_control.disable = [
"invalid-overridden-method",
"unused-variable",
"pointless-string-statement",
"inconsistent-return-statements",
"import-outside-toplevel",
"redefined-outer-name",
]