add new example & fix ImageSizeAndBatchSize node

fix typos
add video upload node and improve video preview
2023-09-18 18:18:00 +07:00 · 2023-09-18 17:53:25 +07:00 · 2023-09-18 17:49:35 +07:00 · 2023-09-18 17:48:23 +07:00 · 2023-09-18 14:31:52 +07:00 · 2023-09-17 19:45:35 -05:00
16 changed files with 4212 additions and 79 deletions
@@ -5,33 +5,120 @@
 ## How to Use

 1. Clone this repo into `custom_nodes` folder.
-2. Download motion modules from [Google Drive](https://drive.google.com/drive/folders/1EqLC65eR1-W-sGD0Im7fkED6c8GkiNFI) | [HuggingFace](https://huggingface.co/guoyww/animatediff) | [CivitAI](https://civitai.com/models/108836) | [Baidu NetDisk](https://pan.baidu.com/s/18ZpcSM6poBqxWNHtnyMcxg?pwd=et8y). You only need to download one of `mm_sd_v14.ckpt` | `mm_sd_v15.ckpt`. Put the model weights under `comfyui-animatediff/models/`. DO NOT change model filename.
+2. Download motion modules and put them under `comfyui-animatediff/models/`.

-#### Update 2023/09/15
+- Original modules: [Google Drive](https://drive.google.com/drive/folders/1EqLC65eR1-W-sGD0Im7fkED6c8GkiNFI) | [HuggingFace](https://huggingface.co/guoyww/animatediff) | [CivitAI](https://civitai.com/models/108836) | [Baidu NetDisk](https://pan.baidu.com/s/18ZpcSM6poBqxWNHtnyMcxg?pwd=et8y)
+- Community modules: [manshoety/AD_Stabilized_Motion](https://huggingface.co/manshoety/AD_Stabilized_Motion) | [CiaraRowles/TemporalDiff](https://huggingface.co/CiaraRowles/TemporalDiff)
+- AnimateDiff v2 [mm_sd_v15_v2.ckpt](https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15_v2.ckpt)

- You can now use community models from [manshoety/AD_Stabilized_Motion](https://huggingface.co/manshoety/AD_Stabilized_Motion) or [CiaraRowles/TemporalDiff](https://huggingface.co/CiaraRowles/TemporalDiff)
- Supports AnimateDiff v2 [mm_sd_v15_v2.ckpt](https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15_v2.ckpt) model
- Fix image is grayed out.
- New node: **AnimateDiffSampler** and **AnimateDiffLoader**
-  - Mostly the same with `KSampler`
-  - Use `AnimateDiffLoader` to load the motion module
-  - `inject_method`: should left default. See [this issue](https://github.com/ArtVentureX/comfyui-animatediff#gif-has-wartermark-after-update-to-the-latest-version) for more details.
-  - `frame_number`: animation length
+## Nodes

-<img width="506" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/f22d6b36-ce36-44cc-80e8-dffe6f77b296">
+#### AnimateDiffLoader

-#### Example Workflow
+<img width="370" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/9d756d01-ea45-4d1c-8e48-56f2725c7ca1">

-<img width="1311" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/b7164539-bc58-4ef9-b178-d914e833805e">
+#### AnimateDiffSampler

+- Mostly the same with `KSampler`
+- Use `AnimateDiffLoader` to load the motion module
+- `inject_method`: should left default
+- `frame_number`: animation length
+- `latent_image`: You can pass an `EmptyLatentImage`

-Workflow file: https://github.com/ArtVentureX/comfyui-animatediff/blob/main/workflow.json
+<img width="370" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/f22d6b36-ce36-44cc-80e8-dffe6f77b296">

-## Samples
+#### AnimateDiffCombine

-![23b44c29-29e8-4f48-ab3c-4df87c90c13f](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/97efb96f-3d3d-4976-8789-78b88f89b2eb)
+- Combine GIF frames and produce the GIF image
+- `frame_rate`: number of frame per second
+- `loop_count`: use 0 for infinite loop
+- `save_image`: should GIF be saved to disk
+- `format`: supports `image/gif`, `image/webp` (better compression) or `video/webm` (need `ffmpeg` installed and available in PATH)

-![25f6c60c-f8ac-4abe-984f-1559c355d7f6](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/c39b26f7-a2af-4dc4-902f-c363e2e6f39a)
+<img width="370" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/381c5acc-06ef-43da-ada0-3dc76f37a3e4">
+
+## Workflows
+
+### Simple txt2gif
+
+<img width="1280" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/b7164539-bc58-4ef9-b178-d914e833805e">
+
+Workflow: [simple.json](https://github.com/ArtVentureX/comfyui-animatediff/blob/main/workflows/simple.json)
+
+Samples:
+
+![animate_diff_01](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/97efb96f-3d3d-4976-8789-78b88f89b2eb)
+
+![animate_diff_02](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/c39b26f7-a2af-4dc4-902f-c363e2e6f39a)
+
+### Latent upscale
+
+Upscale latent output using `LatentUpscale` then do a 2nd pass with `AnimateDiffSampler`.
+
+<img width="1280" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/987a1c5a-c1f8-4b24-8c62-f14496261d6c">
+
+Workflow: [latent-upscale.json](https://github.com/ArtVentureX/comfyui-animatediff/blob/main/workflows/latent-upscale.json)
+
+Samples:
+![animate_diff_upscale](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/f363f6f8-3117-4fa8-bca9-62f6a6e38ce7)
+
+### Using with ControlNet
+
+You will need following additional nodes:
+
+- [Kosinkadink/ComfyUI-Advanced-ControlNet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet): Apply different weight for each latent in batch
+- [Fannovel16/comfyui_controlnet_aux](https://github.com/Fannovel16/comfyui_controlnet_aux): ControlNet preprocessors
+
+#### Animate with starting and ending images
+
+- Use `LatentKeyframe` and `TimestampKeyframe` from [ComfyUI-Advanced-ControlNet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet) to apply diffrent weights for each latent index.
+- Use 2 controlnet modules for two images with weights reverted.
+
+![image](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/bcca1070-e4a1-4698-a2af-aadf9723d015)
+
+Workflow: [cn-2images.json](https://github.com/ArtVentureX/comfyui-animatediff/blob/main/workflows/cn-2images.json)
+
+Samples:
+
+<table>
+<tr>
+<td>
+<img src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/e73fc3cd-a590-40a9-8b33-11358b54f0cd">
+</td>
+<td>
+<img src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/96c2ee92-d457-4862-94d3-d675b7fa2d1f">
+</td>
+</tr>
+<tr>
+<td>
+<img src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/46338853-1ae0-433e-925c-2a41e0382e68">
+</td>
+<td>
+<img src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/707e4ce3-3594-4ff5-9a5f-f9596eb2bcf4">
+</td>
+</tr>
+</table>
+
+#### Using GIF as ControlNet input
+
+Using a GIF (or video, or a list of images) as ControlNet input.
+
+![image](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/cfeed634-e683-4797-b2fd-dbe0926a449e)
+
+Workflow: [cn-vid2vid.json](https://github.com/ArtVentureX/comfyui-animatediff/blob/main/workflows/cn-vid2vid.json)
+
+Samples:
+
+<table>
+<tr>
+<td>
+<img src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/bf926f52-da97-4fb4-b86a-8b26ef5fab04">
+</td>
+<td>
+<img src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/f6472c8c-9b92-47c2-8f28-638726f21be7">
+</td>
+</tr>
+</table>

 ## Known Issues

@@ -39,39 +126,14 @@ Workflow file: https://github.com/ArtVentureX/comfyui-animatediff/blob/main/work

 ![AnimateDiff_00007_](https://github.com/ArtVentureX/comfyui-animatediff/assets/8894763/e6cd53cb-9878-45da-a58a-a15851882386)

-See: https://github.com/continue-revolution/sd-webui-animatediff/issues/38
-
-Main reasons:
-
- Promt are too long (more than 75 tokens)
- Resolution are too high
- Number of frame too high
-
 Work around:

 - Shorter your prompt and negative prompt
 - Reduce resolution. AnimateDiff is trained on 512x512 images so it works best with 512x512 output.
- Shouldn't generate longer than 16 frames. AnimateDiff is trained to output the best results with 16 frames.
+- Disable xformers with `--disable-xformers`

-### GIF has Wartermark after update to the latest version
+### GIF has Wartermark (especially when using mm_sd_v15)

 See: https://github.com/continue-revolution/sd-webui-animatediff/issues/31

-As mentioned in the issue thread, it seems to be due to the training dataset. The new version is the correct implementation and produces smoother GIFs compared to the older version.
-
-<table  class="center">
-    <tr>
-    <td>Old revision</td>
-    <td>New revision</td>
-    </tr>
-    <tr>
-    <td><img src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/8f1a6233-875f-4f0c-aa60-ba93e73b7d64" /></td>
-    <td><img src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/a2029eba-f519-437c-a0b5-1f881e099a20" /></td>
-    </tr>
-    <tr>
-    <td><img src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/41ec449f-1955-466c-bd38-6f2a55d654f8" /></td>
-    <td><img src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/766c2891-5d27-4052-99f9-be9862620919" /></td>
-    </tr>
-</table>
-
-I played around with both version and found that the watermark only present in some models, not always. To use the **old (legacy)** method, change `injection_method` to `legacy` in the `AnimateDiffSampler` node.
+Training data used by the authors of the AnimateDiff paper contained Shutterstock watermarks. Since mm_sd_v15 was finetuned on finer, less drastic movement, the motion module attempts to replicate the transparency of that watermark and does not get blurred away like mm_sd_v14. Try other community finetuned modules.
@@ -5,4 +5,6 @@ from .animatediff.model_utils import get_available_models
 if len(get_available_models()) == 0:
    logger.error("No models available. Please download one and put it in models folder")

-__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
+WEB_DIRECTORY = "./web"
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
@@ -11,6 +11,12 @@ folder_paths.folder_names_and_paths["AnimateDiff"] = (
    ],
    folder_paths.supported_pt_extensions,
 )
+folder_paths.folder_names_and_paths["video_formats"] = (
+    [
+        os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "video_formats"),
+    ],
+    [".json"]
+)


 def get_available_models():
@@ -5,7 +5,6 @@ from torch import Tensor, nn
 import math
 from einops import rearrange, repeat

-from comfy.utils import load_torch_file
 from comfy.ldm.modules.attention import FeedForward, CrossAttention


@@ -57,14 +56,12 @@ class MotionWrapper(nn.Module):
            )

    @classmethod
-    def from_pretrained(cls, checkpoint_path: str):
-        mm_state_dict = load_torch_file(checkpoint_path)
-        mm_type = os.path.basename(checkpoint_path)
+    def from_pretrained(cls, mm_state_dict: dict[str, Tensor], mm_type: str):
        encoding_max_len = get_encoding_max_len(mm_state_dict)
        is_v2 = has_mid_block(mm_state_dict)

        mm = cls(mm_type, encoding_max_len=encoding_max_len, is_v2=is_v2)
-        mm.load_state_dict(mm_state_dict)
+        mm.load_state_dict(mm_state_dict, strict=False)
        return mm

    def set_video_length(self, video_length: int):
@@ -2,10 +2,11 @@ import os
 import json
 import torch
 import numpy as np
+import hashlib
 from typing import Dict, List
 from torch import Tensor
 from torch.nn.functional import group_norm
-from PIL import Image
+from PIL import Image, ImageSequence
 from PIL.PngImagePlugin import PngInfo
 from einops import rearrange

@@ -14,12 +15,13 @@ import comfy.ldm.modules.diffusionmodules.openaimodel as openaimodel
 import comfy.model_management as model_management
 from comfy.model_base import BaseModel
 from comfy.ldm.modules.attention import SpatialTransformer
-from comfy.cli_args import args as cli_args
+from comfy.utils import load_torch_file, calculate_parameters
 from nodes import KSampler

 from .logger import logger
 from .motion_module import MotionWrapper, VanillaTemporalModule
 from .model_utils import get_available_models, get_model_path, get_model_hash
+from .utils import pil2tensor


 def forward_timestep_embed(
@@ -47,7 +49,8 @@ def groupnorm_mm_factory(video_length: int):
        axes_factor = input.size(0) // video_length

        input = rearrange(input, "(b f) c h w -> b c f h w", b=axes_factor)
-        input = group_norm(input, self.num_groups, self.weight, self.bias, self.eps)
+        input = group_norm(input, self.num_groups,
+                           self.weight, self.bias, self.eps)
        input = rearrange(input, "b c f h w -> (b f) c h w", b=axes_factor)
        return input

@@ -67,10 +70,16 @@ def load_motion_module(model_name: str):
    model_hash = get_model_hash(model_path)
    if model_hash not in motion_modules:
        logger.info(f"Loading motion module {model_name}")
-        motion_module = MotionWrapper.from_pretrained(model_path)
-        if not cli_args.force_fp32:
+        mm_state_dict = load_torch_file(model_path)
+        motion_module = MotionWrapper.from_pretrained(
+            mm_state_dict, model_name)
+
+        params = calculate_parameters(mm_state_dict, "")
+        if model_management.should_use_fp16(model_params=params):
            logger.info(f"Converting motion module to fp16.")
            motion_module.half()
+        offload_device = model_management.unet_offload_device()
+        motion_module = motion_module.to(offload_device)

        motion_modules[model_hash] = motion_module

@@ -215,7 +224,7 @@ class AnimateDiffSampler(KSampler):

    def override_beta_schedule(self, model: BaseModel):
        logger.info(f"Override beta schedule.")
-        self.prev_beta = model.get_buffer("betas")
+        self.prev_beta = model.get_buffer("betas").cpu().clone()
        self.prev_linear_start = model.linear_start
        self.prev_linear_end = model.linear_end
        model.register_schedule(
@@ -245,6 +254,7 @@ class AnimateDiffSampler(KSampler):
        unet = model.model.diffusion_model

        logger.info(f"Injecting motion module with method {inject_method}.")
+        motion_module.set_video_length(frame_number)
        injectors[inject_method](unet, motion_module)
        self.override_beta_schedule(model.model)
        if not motion_module.is_v2:
@@ -258,7 +268,7 @@ class AnimateDiffSampler(KSampler):

        self.restore_beta_schedule(model.model)
        if not unet.motion_module.is_v2:
-            logger.info(f"Restore GroupNorm32 forward function.")
+            logger.info(f"Restore GroupNorm.forward function.")
            torch.nn.GroupNorm.forward = orig_groupnorm_forward

        logger.info(f"Ejecting motion module with method {inject_method}.")
@@ -326,8 +336,11 @@ class AnimateDiffCombine:
                    {"default": 8, "min": 1, "max": 24, "step": 1},
                ),
                "loop_count": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
-                "save_image": (["Enabled", "Disabled"],),
-                "filename_prefix": ("STRING", {"default": "AnimateDiff"}),
+                "save_image": ([True, False],),
+                "filename_prefix": ("STRING", {"default": "animate_diff"}),
+                "format": (["image/gif", "image/webp"] +
+                           ["video/"+x[:-5] for x in folder_paths.get_filename_list("video_formats")],),
+                "pingpong": ([False, True],),
            },
            "hidden": {
                "prompt": "PROMPT",
@@ -345,22 +358,24 @@ class AnimateDiffCombine:
        images,
        frame_rate: int,
        loop_count: int,
-        save_image="Enabled",
+        save_image=True,
        filename_prefix="AnimateDiff",
+        format="image/gif",
+        pingpong=False,
        prompt=None,
        extra_pnginfo=None,
    ):
        # convert images to numpy
-        pil_images: List[Image.Image] = []
+        frames: List[Image.Image] = []
        for image in images:
            img = 255.0 * image.cpu().numpy()
            img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
-            pil_images.append(img)
+            frames.append(img)

        # save image
        output_dir = (
            folder_paths.get_output_directory()
-            if save_image == "Enabled"
+            if save_image
            else folder_paths.get_temp_directory()
        )
        (
@@ -381,43 +396,218 @@ class AnimateDiffCombine:
        # save first frame as png to keep metadata
        file = f"{filename}_{counter:05}_.png"
        file_path = os.path.join(full_output_folder, file)
-        pil_images[0].save(
+        frames[0].save(
            file_path,
            pnginfo=metadata,
            compress_level=4,
        )
+        if pingpong:
+            frames = frames + frames[-2:0:-1]

-        # save gif
-        file = f"{filename}_{counter:05}_.gif"
-        file_path = os.path.join(full_output_folder, file)
-        pil_images[0].save(
-            file_path,
-            save_all=True,
-            append_images=pil_images[1:],
-            duration=round(1000 / frame_rate),
-            loop=loop_count,
-            compress_level=4,
-        )
+        format_type, format_ext = format.split("/")

-        print("Saved gif to", file_path, os.path.exists(file_path))
+        if format_type == "image":
+            file = f"{filename}_{counter:05}_.{format_ext}"
+            file_path = os.path.join(full_output_folder, file)
+            frames[0].save(
+                file_path,
+                format=format_ext.upper(),
+                save_all=True,
+                append_images=frames[1:],
+                duration=round(1000 / frame_rate),
+                loop=loop_count,
+                compress_level=4,
+            )
+        else:
+            # save webm
+            import shutil
+            import subprocess
+
+            ffmpeg_path = shutil.which("ffmpeg")
+            if ffmpeg_path is None:
+                raise ProcessLookupError("Could not find ffmpeg")
+            video_format_path = folder_paths.get_full_path(
+                "video_formats", format_ext + ".json")
+            with open(video_format_path, 'r') as stream:
+                video_format = json.load(stream)
+            file = f"{filename}_{counter:05}_.{video_format['extension']}"
+            file_path = os.path.join(full_output_folder, file)
+            dimensions = f"{frames[0].width}x{frames[0].height}"
+            args = [ffmpeg_path, "-v", "error", "-f", "rawvideo", "-pix_fmt", "rgb24",
+                    "-s", dimensions, "-r", str(frame_rate), "-i", "-"] \
+                + video_format['main_pass'] + [file_path]
+
+            env = os.environ
+            if "environment" in video_format:
+                env.update(video_format["environment"])
+            with subprocess.Popen(args, stdin=subprocess.PIPE, env=env) as proc:
+                for frame in frames:
+                    proc.stdin.write(frame.tobytes())

        previews = [
            {
                "filename": file,
                "subfolder": subfolder,
-                "type": "output" if save_image == "Enabled" else "temp",
+                "type": "output" if save_image else "temp",
+                "format": format,
            }
        ]
-        return {"ui": {"images": previews}}
+        return {"ui": {"videos": previews}}
+
+
+class LoadVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = os.path.join(folder_paths.get_input_directory(), "video")
+        if not os.path.exists(input_dir):
+            os.makedirs(input_dir, exist_ok=True)
+
+        files = [f"video/{f}" for f in os.listdir(input_dir) if os.path.isfile(
+            os.path.join(input_dir, f))]
+
+        return {
+            "required": {
+                "video": (sorted(files), {"video_upload": True}),
+            },
+            "optional": {
+                "frame_start": ("INT", {"default": 0, "min": 0, "max": 0xffffffff, "step": 1}),
+                "frame_limit": ("INT", {"default": 16, "min": 1, "max": 10240, "step": 1}),
+            }
+        }
+
+    CATEGORY = "Animate Diff/Utils"
+    RETURN_TYPES = ("IMAGE", "INT")
+    RETURN_NAMES = ("frames", "frame_count")
+    FUNCTION = "load"
+
+    def load_gif(self, gif_path: str, frame_start: int, frame_limit: int):
+        image = Image.open(gif_path)
+        frames = []
+
+        for i, frame in enumerate(ImageSequence.Iterator(image)):
+            if i < frame_start:
+                continue
+            elif i >= frame_start + frame_limit:
+                break
+            else:
+                frames.append(pil2tensor(frame.copy().convert("RGB")))
+
+        return frames
+
+    def load_video(self, video_path, frame_start: int, frame_limit: int):
+        import cv2
+
+        video = cv2.VideoCapture(video_path)
+        video.set(cv2.CAP_PROP_POS_FRAMES, frame_start)
+
+        frames = []
+        for i in range(frame_limit):
+            # Read the next frame
+            ret, frame = video.read()
+            if ret:
+                # Convert the frame to RGB (OpenCV uses BGR)
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                # Convert the NumPy array to a PIL image and append to list
+                frames.append(pil2tensor(Image.fromarray(frame)))
+            else:
+                break
+
+        video.release()
+
+        return frames
+
+    def load(self, video: str, frame_start=0, frame_limit=16):
+        print("path", video)
+        video_path = folder_paths.get_annotated_filepath(video)
+        (_, ext) = os.path.splitext(video_path)
+
+        if ext.lower() in {".gif", ".webp"}:
+            frames = self.load_gif(video_path, frame_start, frame_limit)
+        elif ext.lower() in {".webp", ".mp4", ".mov", ".avi"}:
+            frames = self.load_video(video_path, frame_start, frame_limit)
+        else:
+            raise ValueError(f"Unsupported video format: {ext}")
+
+        return (torch.cat(frames, dim=0),)
+
+    @classmethod
+    def IS_CHANGED(s, image, *args, **kwargs):
+        image_path = folder_paths.get_annotated_filepath(image)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, video, *args, **kwargs):
+        if not folder_paths.exists_annotated_filepath(video):
+            return "Invalid video file: {}".format(video)
+
+        return True
+
+
+class ImageSizeAndBatchSize:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+        }
+
+    CATEGORY = "Animate Diff/Utils"
+    RETURN_TYPES = ("INT", "INT", "INT")
+    RETURN_NAMES = ("width", "height", "batch_size")
+    FUNCTION = "batch_size"
+
+    def batch_size(self, image: Tensor):
+        (batch_size, height, width) = image.shape[0:3]
+        return (width, height, batch_size)
+
+
+class ImageChunking:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "chunk_size": ("INT", {"default": 16, "min": 1, "max": 1024, "step": 1}),
+                "allow_remainder": ([True, False],),
+            },
+        }
+
+    CATEGORY = "Animate Diff/Utils"
+    RETURN_TYPES = ("IMAGE",)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "chunk"
+
+    def chunk(self, images: Tensor, chunk_size: int, allow_remainder: bool):
+        # Check if tensor is divisible into chunks of chunk_size
+        if images.shape[0] % chunk_size != 0 and not allow_remainder:
+            raise ValueError(
+                "Tensor's first dimension is not divisible by chunk size")
+
+        # Use torch.chunk to divide the tensor
+        chunk_count = images.shape[0] // chunk_size + \
+            images.shape[0] % chunk_size
+
+        print("chunk_count", chunk_count)
+        chunks = torch.chunk(images, chunk_count, dim=0)
+
+        return (list(chunks), )


 NODE_CLASS_MAPPINGS = {
    "AnimateDiffModuleLoader": AnimateDiffModuleLoader,
    "AnimateDiffCombine": AnimateDiffCombine,
    "AnimateDiffSampler": AnimateDiffSampler,
+    "LoadVideo": LoadVideo,
+    "ImageSizeAndBatchSize": ImageSizeAndBatchSize,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
    "AnimateDiffModuleLoader": "Animate Diff Module Loader",
    "AnimateDiffSampler": "Animate Diff Sampler",
    "AnimateDiffCombine": "Animate Diff Combine",
+    "LoadVideo": "Load Video",
+    "ImageSizeAndBatchSize": "Get Image Size + Batch Size",
 }
@@ -0,0 +1,13 @@
+import torch
+import numpy as np
+from PIL import Image
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(
+        np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+    )
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
@@ -0,0 +1,10 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libsvtav1",
+        "-pix_fmt", "yuv420p10le",
+        "-crf", "23"
+    ],
+     "extension": "webm",
+     "environment": {"SVT_LOG": "1"}
+}
@@ -0,0 +1,9 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libx264",
+        "-pix_fmt", "yuv420p",
+        "-crf", "19"
+    ],
+     "extension": "mp4"
+}
@@ -0,0 +1,11 @@
+{
+    "main_pass":
+    [
+        "-n", "-c:v", "libx265",
+        "-pix_fmt", "yuv420p10le",
+        "-preset", "medium",
+        "-crf", "22",
+        "-x265-params", "log-level=quiet"
+    ],
+     "extension": "mp4"
+}
@@ -0,0 +1,9 @@
+{
+    "main_pass":
+    [
+        "-n",
+        "-pix_fmt", "yuv420p",
+        "-crf", "23"
+    ],
+     "extension": "webm"
+}
@@ -0,0 +1,162 @@
+import { app } from "../../../scripts/app.js";
+import { api } from "../../../scripts/api.js";
+
+function offsetDOMWidget(widget, ctx, node, widgetWidth, widgetY, height) {
+  const margin = 10;
+  const elRect = ctx.canvas.getBoundingClientRect();
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(0, widgetY + margin);
+
+  const scale = new DOMMatrix().scaleSelf(transform.a, transform.d);
+  Object.assign(widget.inputEl.style, {
+    transformOrigin: "0 0",
+    transform: scale,
+    left: `${transform.e}px`,
+    top: `${transform.d + transform.f}px`,
+    width: `${widgetWidth}px`,
+    height: `${(height || widget.parent?.inputHeight || 32) - margin}px`,
+    position: "absolute",
+    background: !node.color ? "" : node.color,
+    color: !node.color ? "" : "white",
+    zIndex: 5, //app.graph._nodes.indexOf(node),
+  });
+}
+
+export const hasWidgets = (node) => {
+  if (!node.widgets || !node.widgets?.[Symbol.iterator]) {
+    return false;
+  }
+  return true;
+};
+
+export const cleanupNode = (node) => {
+  if (!hasWidgets(node)) {
+    return;
+  }
+
+  for (const w of node.widgets) {
+    if (w.canvas) {
+      w.canvas.remove();
+    }
+    if (w.inputEl) {
+      w.inputEl.remove();
+    }
+    // calls the widget remove callback
+    w.onRemoved?.();
+  }
+};
+
+export const CreatePreviewElement = (name, val, format, callback) => {
+  const [type] = format.split("/");
+
+  const w = {
+    name,
+    type,
+    value: val,
+    draw: function (ctx, node, widgetWidth, widgetY, height) {
+      const [cw, ch] = this.computeSize(widgetWidth);
+      offsetDOMWidget(this, ctx, node, widgetWidth, widgetY, ch);
+    },
+    computeSize: function (_) {
+      const ratio = this.inputRatio || 1;
+      const width = Math.max(220, this.parent.size[0]);
+      return [width, width / ratio + 10];
+    },
+    onRemoved: function () {
+      if (this.inputEl) {
+        this.inputEl.remove();
+      }
+    },
+  };
+
+  w.inputEl = document.createElement(type === "video" ? "video" : "img");
+  w.inputEl.src = w.value;
+  if (type === "video") {
+    w.inputEl.setAttribute("type", "video/webm");
+    w.inputEl.autoplay = true;
+    w.inputEl.loop = true;
+    w.inputEl.controls = false;
+  }
+  w.inputEl.onload = function () {
+    w.inputRatio = w.inputEl.naturalWidth / w.inputEl.naturalHeight;
+    callback?.();
+  };
+  document.body.appendChild(w.inputEl);
+  return w;
+};
+
+const videoPreview = {
+  name: "AnimateDiff.VideoPreview",
+  async beforeRegisterNodeDef(nodeType, nodeData, app) {
+    const onExecuted = nodeType.prototype.onExecuted;
+    nodeType.prototype.onExecuted = function (message) {
+      const r = onExecuted ? onExecuted.apply(this, message) : undefined;
+
+      if (message?.videos) {
+        this.videos = message.videos;
+      }
+
+      return r;
+    };
+
+    const onDrawBackground = nodeType.prototype.onDrawBackground;
+    nodeType.prototype.onDrawBackground = function (ctx) {
+      const r = onDrawBackground ? onDrawBackground.apply(this, arguments) : undefined;
+      const node = this;
+      const prefix = "ad_video_preview_";
+
+      if (node.videos_rendered === node.videos) {
+        return r;
+      }
+
+      if (node.widgets) {
+        const pos = node.widgets.findIndex((w) => w.name === `${prefix}_0`);
+        if (pos !== -1) {
+          for (let i = pos; i < node.widgets.length; i++) {
+            node.widgets[i].onRemoved?.();
+          }
+          node.widgets.length = pos;
+        }
+      }
+      if (node.videos) {
+        node.videos.forEach((params, i) => {
+          const previewUrl = api.apiURL(
+            "/view?" + new URLSearchParams(params).toString()
+          );
+          const w = node.addCustomWidget(
+            CreatePreviewElement(
+              `${prefix}_${i}`,
+              previewUrl,
+              params.format || "image/gif",
+              node.computeSizeKeepWidth.bind(node)
+            )
+          );
+          w.parent = node;
+        });
+        node.videos_rendered = node.videos;
+      }
+
+      return r;
+    };
+
+    const onRemoved = nodeType.prototype.onRemoved;
+    nodeType.prototype.onRemoved = function () {
+      cleanupNode(this);
+      return onRemoved ? onRemoved.apply(this, arguments) : undefined;
+    };
+
+    nodeType.prototype.computeSizeKeepWidth = function () {
+      this.setSize([
+        this.size[0],
+        this.computeSize([this.size[0], this.size[1]])[1],
+      ]);
+    };
+  },
+};
+
+app.registerExtension(videoPreview);
@@ -0,0 +1,188 @@
+import { app } from "../../../scripts/app.js";
+import { api } from "../../../scripts/api.js";
+import { ComfyWidgets } from "../../../scripts/widgets.js";
+
+const supportedVideoTypes = [
+  "image/gif",
+  "video/webm",
+  "video/mp4",
+  "video/mov",
+];
+
+const VIDEOUPLOAD = (node, inputName, inputData, app) => {
+  const previewWidget = "ad_video_preview";
+  const videoWidget = node.widgets.find((w) => w.name === "video");
+  let uploadWidget;
+
+  const showVideo = (name) => {
+    let folder_separator = name.lastIndexOf("/");
+    let subfolder = "";
+    if (folder_separator > -1) {
+      subfolder = name.substring(0, folder_separator);
+      name = name.substring(folder_separator + 1);
+    }
+    const ext = name.substring(name.lastIndexOf(".") + 1);
+    const format = supportedVideoTypes.find((t) => t.endsWith(ext));
+    node.videos = [
+      {
+        filename: name,
+        type: "input",
+        subfolder: subfolder,
+        format,
+      },
+    ];
+  };
+
+  var default_value = videoWidget.value;
+  Object.defineProperty(videoWidget, "value", {
+    set: function (value) {
+      this._real_value = value;
+    },
+
+    get: function () {
+      let value = "";
+      if (this._real_value) {
+        value = this._real_value;
+      } else {
+        return default_value;
+      }
+
+      if (value.filename) {
+        let real_value = value;
+        value = "";
+        if (real_value.subfolder) {
+          value = real_value.subfolder + "/";
+        }
+
+        value += real_value.filename;
+
+        if (real_value.type && real_value.type !== "input")
+          value += ` [${real_value.type}]`;
+      }
+      return value;
+    },
+  });
+
+  // Add our own callback to the combo widget to render an image when it changes
+  const cb = node.callback;
+  videoWidget.callback = function () {
+    showVideo(videoWidget.value);
+    if (cb) {
+      return cb.apply(this, arguments);
+    }
+  };
+
+  // On load if we have a value then render the image
+  // The value isnt set immediately so we need to wait a moment
+  // No change callbacks seem to be fired on initial setting of the value
+  requestAnimationFrame(() => {
+    if (videoWidget.value) {
+      showVideo(videoWidget.value);
+    }
+  });
+
+  async function uploadFile(file, updateNode, pasted = false) {
+    try {
+      // Wrap file in formdata so it includes filename
+      const body = new FormData();
+      body.append("image", file);
+      body.append("subfolder", "video");
+      const resp = await api.fetchApi("/upload/image", {
+        method: "POST",
+        body,
+      });
+
+      if (resp.status === 200) {
+        const data = await resp.json();
+        // Add the file to the dropdown list and update the widget value
+        let path = data.name;
+        if (data.subfolder) path = data.subfolder + "/" + path;
+
+        if (!videoWidget.options.values.includes(path)) {
+          videoWidget.options.values.push(path);
+        }
+
+        if (updateNode) {
+          showVideo(path);
+          videoWidget.value = path;
+        }
+      } else {
+        alert(resp.status + " - " + resp.statusText);
+      }
+    } catch (error) {
+      alert(error);
+    }
+  }
+
+  const fileInput = document.createElement("input");
+  Object.assign(fileInput, {
+    type: "file",
+    accept: supportedVideoTypes.join(","),
+    style: "display: none",
+    onchange: async () => {
+      if (fileInput.files.length) {
+        await uploadFile(fileInput.files[0], true);
+      }
+    },
+  });
+  document.body.append(fileInput);
+
+  // Create the button widget for selecting the files
+  uploadWidget = node.addWidget(
+    "button",
+    "choose file to upload",
+    "image",
+    () => {
+      fileInput.click();
+    }
+  );
+  uploadWidget.serialize = false;
+
+  // Add handler to check if an image is being dragged over our node
+  node.onDragOver = function (e) {
+    if (e.dataTransfer && e.dataTransfer.items) {
+      const image = [...e.dataTransfer.items].find((f) => f.kind === "file");
+      return !!image;
+    }
+
+    return false;
+  };
+
+  // On drop upload files
+  node.onDragDrop = function (e) {
+    console.log("onDragDrop called");
+    let handled = false;
+    for (const file of e.dataTransfer.files) {
+      if (file.type.startsWith("image/")) {
+        uploadFile(file, !handled); // Dont await these, any order is fine, only update on first one
+        handled = true;
+      }
+    }
+
+    return handled;
+  };
+
+  node.pasteFile = function (file) {
+    if (supportedVideoTypes.indexOf(file.type) > -1) {
+      const is_pasted =
+        file.name === "image.png" && file.lastModified - Date.now() < 2000;
+      uploadFile(file, true, is_pasted);
+      return true;
+    }
+    return false;
+  };
+
+  return { widget: uploadWidget };
+};
+
+ComfyWidgets["VIDEOUPLOAD"] = VIDEOUPLOAD;
+
+// Adds an upload button to the nodes
+app.registerExtension({
+  name: "AnimateDiff.UploadVideo",
+  async beforeRegisterNodeDef(nodeType, nodeData, app) {
+    if (nodeData?.input?.required?.video?.[1]?.video_upload === true) {
+      nodeData.input.required.upload = ["VIDEOUPLOAD"];
+    }
+  },
+});
@@ -0,0 +1,877 @@
+{
+  "last_node_id": 106,
+  "last_link_id": 189,
+  "nodes": [
+    {
+      "id": 16,
+      "type": "AnimateDiffModuleLoader",
+      "pos": [
+        -280,
+        140
+      ],
+      "size": {
+        "0": 310,
+        "1": 60
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MOTION_MODULE",
+          "type": "MOTION_MODULE",
+          "links": [
+            78
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AnimateDiffModuleLoader"
+      },
+      "widgets_values": [
+        "mm-Stabilized_mid.pth"
+      ],
+      "color": "#571a1a",
+      "bgcolor": "#6b2e2e"
+    },
+    {
+      "id": 13,
+      "type": "VAELoader",
+      "pos": [
+        -280,
+        400
+      ],
+      "size": {
+        "0": 310,
+        "1": 60
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            82
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "vae-ft-mse-840000-ema-pruned.safetensors"
+      ],
+      "color": "#571a1a",
+      "bgcolor": "#6b2e2e"
+    },
+    {
+      "id": 45,
+      "type": "AnimateDiffCombine",
+      "pos": [
+        1240,
+        140
+      ],
+      "size": [
+        360,
+        732
+      ],
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 172
+        }
+      ],
+      "outputs": [
+        {
+          "name": "GIF",
+          "type": "GIF",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AnimateDiffCombine"
+      },
+      "widgets_values": [
+        8,
+        0,
+        true,
+        "AnimateDiff",
+        "image/gif",
+        true,
+        "/view?filename=AnimateDiff_00092_.gif&subfolder=&type=output&format=image%2Fgif"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        -280,
+        250
+      ],
+      "size": {
+        "0": 310,
+        "1": 100
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            79
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            3,
+            5
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "SDHK_v4.safetensors"
+      ],
+      "color": "#571a1a",
+      "bgcolor": "#6b2e2e"
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        60,
+        300
+      ],
+      "size": [
+        310,
+        100
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            70
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "embedding:easynegative, embedding:badhandv4, nsfw"
+      ],
+      "color": "#572e1a",
+      "bgcolor": "#6b422e"
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        60,
+        140
+      ],
+      "size": [
+        310,
+        110
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            69
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "(best quality, masterpiece), 1girl, short hair, blue eyes, dancing, city, cloudy"
+      ],
+      "color": "#572e1a",
+      "bgcolor": "#6b422e"
+    },
+    {
+      "id": 41,
+      "type": "AnimateDiffSampler",
+      "pos": [
+        900,
+        140
+      ],
+      "size": [
+        310,
+        330
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "motion_module",
+          "type": "MOTION_MODULE",
+          "link": 78,
+          "slot_index": 0
+        },
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 79,
+          "slot_index": 1
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 176
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 180
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 80
+        },
+        {
+          "name": "frame_number",
+          "type": "INT",
+          "link": 185,
+          "widget": {
+            "name": "frame_number",
+            "config": [
+              "INT",
+              {
+                "default": 16,
+                "min": 2,
+                "max": 32,
+                "step": 1
+              }
+            ]
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            81
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AnimateDiffSampler"
+      },
+      "widgets_values": [
+        "default",
+        16,
+        345029849956754,
+        "fixed",
+        20,
+        8,
+        "euler",
+        "normal",
+        1
+      ],
+      "color": "#57571a",
+      "bgcolor": "#6b6b2e"
+    },
+    {
+      "id": 39,
+      "type": "ControlNetApplyAdvanced",
+      "pos": [
+        471,
+        275
+      ],
+      "size": [
+        300,
+        170
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
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Author	SHA1	Message	Date
Tung Nguyen	91a286cdf6	add new example & fix ImageSizeAndBatchSize node	2023-09-18 18:18:00 +07:00
Tung Nguyen	07f8b8d2a9	fix typos	2023-09-18 17:53:25 +07:00
Tung Nguyen	d849f6c7d6	add video upload node and improve video preview	2023-09-18 17:49:35 +07:00
Tung Nguyen	12ea0093e3	add more example workflows	2023-09-18 17:48:23 +07:00
ArtVenture	4e881671aa	Merge pull request #27 from AustinMroz/upstream_video_format ffmpeg improvements: webm quality, and additional video formats	2023-09-18 14:31:52 +07:00
Austin Mroz	414c5d3bb8	Add additional video formats and config system This ports the video format code written for the upstream changes to the ffmpeg implementation. It improves the quality of webm outputs and adds support for additional codecs (h264, h265, av1) It also improves the logging by passing errors and more selectively blocking the logging of encoders. While h265 has been included, most browsers will be unable to display the resulting video.	2023-09-17 19:45:35 -05:00
ArtVenture	78e04fcdc6	Merge pull request #25 from ArtVentureX/feat/gif_preview Improve GIF preview and support video output	2023-09-17 11:43:51 +07:00
Tung Nguyen	60d14a9840	update README	2023-09-17 11:41:55 +07:00
Tung Nguyen	427cf04893	improve gif preview	2023-09-17 11:08:55 +07:00
Tung Nguyen	87815b7aae	add gif preview & support pingping gif	2023-09-16 17:46:24 +07:00
Tung Nguyen	9ae375fbd8	fix: cannot change frame_number	2023-09-16 17:16:16 +07:00
ArtVenture	d4f5328a47	Merge pull request #23 from ArtVentureX/code-refactor code refactor	2023-09-16 06:28:31 +07:00