fix missing block_type when init MotionModule

Support animatediff v2 & improve image quality

README.md

@@ -7,18 +7,31 @@
 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.
 
+#### Update 2023/09/15
+
+- 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
+
+<img width="506" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/f22d6b36-ce36-44cc-80e8-dffe6f77b296">
+
+#### Example Workflow
+
+<img width="1311" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/b7164539-bc58-4ef9-b178-d914e833805e">
+
+
+Workflow file: https://github.com/ArtVentureX/comfyui-animatediff/blob/main/workflow.json
+
 ## Samples
 
-### txt2img
+![23b44c29-29e8-4f48-ab3c-4df87c90c13f](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/97efb96f-3d3d-4976-8789-78b88f89b2eb)
 
-<img width="1254" alt="ComfyUI AnimateDiff Usage" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/a88e2141-c55f-4bdb-b6ca-9155b6639114">
-
-![AnimateDiff_00001](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/e48f148a-886b-4a0d-b589-9fa795b06936)
-
-### img2img
-<img width="1121" alt="Screenshot 2023-07-22 at 22 08 00" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/600f96b0-df21-4437-917f-7eda35ab6363">
-
-![AnimateDiff_00002](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/c78d64b9-b308-41ec-9804-bbde654d0b47)
+![25f6c60c-f8ac-4abe-984f-1559c355d7f6](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/c39b26f7-a2af-4dc4-902f-c363e2e6f39a)
 
 ## Known Issues
 
@@ -26,11 +39,23 @@
 
 ![AnimateDiff_00007_](https://github.com/ArtVentureX/comfyui-animatediff/assets/8894763/e6cd53cb-9878-45da-a58a-a15851882386)
 
-This is usually due to memory (VRAM) is not enough to process the whole image batch at the same time. Try reduce the image size and frame number.
+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.
 
 ### GIF has Wartermark after update to the latest version
 
-See https://github.com/continue-revolution/sd-webui-animatediff/issues/31
+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.
 
@@ -49,7 +74,4 @@ As mentioned in the issue thread, it seems to be due to the training dataset. Th
     </tr>
 </table>
 
-
-I played around with both version and found that the watermark only present in some models, not always. So I've brought back the old method and also created a new node with the new method. You can try both to find the best fit for each model.
-
-![Screenshot 2023-07-28 at 18 14 14](https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/25cf6092-3e67-435e-86cc-43614ca7d6aa)
+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.

animatediff/model_utils.py

@@ -3,8 +3,6 @@ import hashlib
 
 import folder_paths
 
-from .logger import logger
-
 
 folder_paths.folder_names_and_paths["AnimateDiff"] = (
     [
@@ -14,17 +12,6 @@ folder_paths.folder_names_and_paths["AnimateDiff"] = (
     folder_paths.supported_pt_extensions,
 )
 
-known_models = {
-    "aa7fd8a200a89031edd84487e2a757c5315460eca528fa70d4b3885c399bffd5": "mm_sd_v14.ckpt",
-    "cf16ea656cb16124990c8e2c70a29c793f9841f3a2223073fac8bd89ebd9b69a": "mm_sd_v15.ckpt",
-    "0aaf157b9c51a0ae07cb5d9ea7c51299f07bddc6f52025e1f9bb81cd763631df": "mm-Stabilized_high.pth",
-    "39de8b71b1c09f10f4602f5d585d82771a60d3cf282ba90215993e06afdfe875": "mm-Stabilized_mid.pth",
-    "3cb569f7ce3dc6a10aa8438e666265cb9be3120d8f205de6a456acf46b6c99f4": "temporaldiff-v1-animatediff.ckpt",
-    "69ed0f5fef82b110aca51bcab73b21104242bc65d6ab4b8b2a2a94d31cad1bf0": "mm_sd_v15_v2.ckpt",
-}
-
-v2_models = ["69ed0f5fef82b110aca51bcab73b21104242bc65d6ab4b8b2a2a94d31cad1bf0"]
-
 
 def get_available_models():
     return folder_paths.get_filename_list("AnimateDiff")
@@ -34,25 +21,7 @@ def get_model_path(model_name):
     return folder_paths.get_full_path("AnimateDiff", model_name)
 
 
-def sha256_file(file_path):
+def get_model_hash(file_path):
     with open(file_path, "rb") as f:
         bytes = f.read()  # read entire file as bytes
         return hashlib.sha256(bytes).hexdigest()
-
-
-def validate_mm_model(model_name):
-    model_path = get_model_path(model_name)
-    model_hash = sha256_file(model_path)
-
-    if model_hash in known_models:
-        logger.info(f"You are using {model_name}, which has been tested and supported.")
-    else:
-        logger.warn(
-            f"Your model {model_name} has not been tested and supported."
-            "Either your download is incomplete or your model has not been tested. "
-            "Please use at your own risk."
-        )
-
-    using_v2 = model_hash in v2_models
-
-    return (model_hash, using_v2)

animatediff/motion_module.py

@@ -1,12 +1,12 @@
+import os
 import torch
-import torch.nn.functional as F
-from torch import nn
+from torch import Tensor, nn
 
 import math
 from einops import rearrange, repeat
 
-from comfy.ldm.modules.attention import FeedForward
-from .attention_processor import Attention as CrossAttention
+from comfy.utils import load_torch_file
+from comfy.ldm.modules.attention import FeedForward, CrossAttention
 
 
 def zero_module(module):
@@ -15,41 +15,108 @@ def zero_module(module):
         p.detach().zero_()
     return module
 
+
+# Merge from https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved
+def get_encoding_max_len(mm_state_dict: dict[str, Tensor]) -> int:
+    # use pos_encoder.pe entries to determine max length - [1, {max_length}, {320|640|1280}]
+    for key in mm_state_dict.keys():
+        if key.endswith("pos_encoder.pe"):
+            return mm_state_dict[key].size(1)  # get middle dim
+    raise ValueError(f"No pos_encoder.pe found in mm_state_dict")
+
+
+def has_mid_block(mm_state_dict: dict[str, Tensor]):
+    # check if keys contain mid_block
+    for key in mm_state_dict.keys():
+        if key.startswith("mid_block."):
+            return True
+    return False
+
+
 class MotionWrapper(nn.Module):
-    def __init__(self, mm_hash, is_v2 = False):
+    def __init__(self, mm_type: str, encoding_max_len: int = 24, is_v2=False):
         super().__init__()
-        if is_v2:
-            max_len = 32
-        else:
-            max_len = 24
+        self.mm_type = mm_type
+        self.is_v2 = is_v2
 
         self.down_blocks = nn.ModuleList([])
         self.up_blocks = nn.ModuleList([])
+        self.mid_block = None
+
         for c in (320, 640, 1280, 1280):
-            self.down_blocks.append(MotionModule(c, max_len=max_len))
+            self.down_blocks.append(
+                MotionModule(c, BlockType.DOWN, encoding_max_len=encoding_max_len)
+            )
         for c in (1280, 1280, 640, 320):
-            self.up_blocks.append(MotionModule(c, is_up=True, max_len=max_len))
+            self.up_blocks.append(
+                MotionModule(c, BlockType.UP, encoding_max_len=encoding_max_len)
+            )
         if is_v2:
-            self.mid_block = MotionModule(1280, max_len=max_len, is_mid=is_v2)
-        self.mm_hash = mm_hash
-        self.is_v2 = is_v2
+            self.mid_block = MotionModule(
+                1280, BlockType.MID, encoding_max_len=encoding_max_len
+            )
+
+    @classmethod
+    def from_pretrained(cls, checkpoint_path: str):
+        mm_state_dict = load_torch_file(checkpoint_path)
+        mm_type = os.path.basename(checkpoint_path)
+        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)
+        return mm
+
+    def set_video_length(self, video_length: int):
+        for block in self.down_blocks:
+            block.set_video_length(video_length)
+        for block in self.up_blocks:
+            block.set_video_length(video_length)
+        if self.mid_block is not None:
+            self.mid_block.set_video_length(video_length)
+
+
+class BlockType:
+    UP = "up"
+    DOWN = "down"
+    MID = "mid"
 
 
 class MotionModule(nn.Module):
-    def __init__(self, in_channels, is_up=False, is_mid=False, max_len=24):
+    def __init__(
+        self,
+        in_channels,
+        block_type: BlockType,
+        encoding_max_len=24,
+    ):
         super().__init__()
-        if is_mid:
-            self.motion_modules = nn.ModuleList([get_motion_module(in_channels, max_len)])
+        self.block_type = block_type
+
+        if block_type == BlockType.MID:
+            self.motion_modules = nn.ModuleList(
+                [get_motion_module(in_channels, encoding_max_len)]
+            )
         else:
             self.motion_modules = nn.ModuleList(
-                [get_motion_module(in_channels, max_len), get_motion_module(in_channels, max_len)]
+                [
+                    get_motion_module(in_channels, encoding_max_len),
+                    get_motion_module(in_channels, encoding_max_len),
+                ]
             )
-            if is_up:
-                self.motion_modules.append(get_motion_module(in_channels, max_len))
+            if block_type == BlockType.UP:
+                self.motion_modules.append(
+                    get_motion_module(in_channels, encoding_max_len)
+                )
+
+    def set_video_length(self, video_length: int):
+        for motion_module in self.motion_modules:
+            motion_module.set_video_length(video_length)
 
 
 def get_motion_module(in_channels, max_len):
-    return VanillaTemporalModule(in_channels=in_channels, temporal_position_encoding_max_len=max_len)
+    return VanillaTemporalModule(
+        in_channels=in_channels, temporal_position_encoding_max_len=max_len
+    )
 
 
 class VanillaTemporalModule(nn.Module):
@@ -85,8 +152,13 @@ class VanillaTemporalModule(nn.Module):
                 self.temporal_transformer.proj_out
             )
 
+    def set_video_length(self, video_length: int):
+        self.temporal_transformer.set_video_length(video_length)
+
     def forward(self, input_tensor, encoder_hidden_states, attention_mask=None):
-        return self.temporal_transformer(input_tensor, encoder_hidden_states, attention_mask)
+        return self.temporal_transformer(
+            input_tensor, encoder_hidden_states, attention_mask
+        )
 
 
 class TemporalTransformer3DModel(nn.Module):
@@ -140,10 +212,12 @@ class TemporalTransformer3DModel(nn.Module):
             ]
         )
         self.proj_out = nn.Linear(inner_dim, in_channels)
+        self.video_length = 16
+
+    def set_video_length(self, video_length: int):
+        self.video_length = video_length
 
     def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
-        video_length = hidden_states.shape[0] // 2 # TODO: config this value in scripts
-
         batch, channel, height, weight = hidden_states.shape
         residual = hidden_states
 
@@ -159,7 +233,7 @@ class TemporalTransformer3DModel(nn.Module):
             hidden_states = block(
                 hidden_states,
                 encoder_hidden_states=encoder_hidden_states,
-                video_length=video_length,
+                video_length=self.video_length,
             )
 
         # output
@@ -204,15 +278,15 @@ class TemporalTransformerBlock(nn.Module):
             attention_blocks.append(
                 VersatileAttention(
                     attention_mode=block_name.split("_")[0],
-                    cross_attention_dim=cross_attention_dim
+                    context_dim=cross_attention_dim
                     if block_name.endswith("_Cross")
                     else None,
                     query_dim=dim,
                     heads=num_attention_heads,
                     dim_head=attention_head_dim,
                     dropout=dropout,
-                    bias=attention_bias,
-                    upcast_attention=upcast_attention,
+                    # bias=attention_bias, # remove for Comfy CrossAttention
+                    # upcast_attention=upcast_attention, # remove for Comfy CrossAttention
                     cross_frame_attention_mode=cross_frame_attention_mode,
                     temporal_position_encoding=temporal_position_encoding,
                     temporal_position_encoding_max_len=temporal_position_encoding_max_len,
@@ -284,7 +358,7 @@ class VersatileAttention(CrossAttention):
         assert attention_mode == "Temporal"
 
         self.attention_mode = attention_mode
-        self.is_cross_attention = kwargs["cross_attention_dim"] is not None
+        self.is_cross_attention = kwargs["context_dim"] is not None
 
         self.pos_encoder = (
             PositionalEncoding(
@@ -327,8 +401,8 @@ class VersatileAttention(CrossAttention):
         hidden_states = super().forward(
             hidden_states,
             encoder_hidden_states,
-            attention_mask,
-            **cross_attention_kwargs,
+            value=None,
+            mask=attention_mask,
         )
 
         hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)

animatediff/nodes.py

@@ -1,9 +1,10 @@
 import os
 import json
-import hashlib
 import torch
 import numpy as np
-from typing import Dict, List, Tuple
+from typing import Dict, List
+from torch import Tensor
+from torch.nn.functional import group_norm
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
 from einops import rearrange
@@ -11,19 +12,14 @@ from einops import rearrange
 import folder_paths
 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.ldm.modules.diffusionmodules.util import GroupNorm32
-from comfy.utils import load_torch_file, calculate_parameters
-from comfy.model_patcher import ModelPatcher
+from comfy.cli_args import args as cli_args
 from nodes import KSampler
 
 from .logger import logger
 from .motion_module import MotionWrapper, VanillaTemporalModule
-from .model_utils import get_available_models, get_model_path, validate_mm_model
-
-
-orig_forward_timestep_embed = openaimodel.forward_timestep_embed
-groupnorm32_original_forward = GroupNorm32.forward
+from .model_utils import get_available_models, get_model_path, get_model_hash
 
 
 def forward_timestep_embed(
@@ -44,44 +40,38 @@ def forward_timestep_embed(
     return x
 
 
-def groupnorm32_mm_forward(self, x):
-    x = rearrange(x, "(b f) c h w -> b c f h w", b=2)
-    x = groupnorm32_original_forward(self, x)
-    x = rearrange(x, "b c f h w -> (b f) c h w", b=2)
-    return x
+def groupnorm_mm_factory(video_length: int):
+    def groupnorm_mm_forward(self, input: Tensor) -> Tensor:
+        # axes_factor normalizes batch based on total conds and unconds passed in batch;
+        # the conds and unconds per batch can change based on VRAM optimizations that may kick in
+        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 = rearrange(input, "b c f h w -> (b f) c h w", b=axes_factor)
+        return input
+
+    return groupnorm_mm_forward
 
 
+orig_forward_timestep_embed = openaimodel.forward_timestep_embed
+orig_maximum_batch_area = model_management.maximum_batch_area
+orig_groupnorm_forward = torch.nn.GroupNorm.forward
 openaimodel.forward_timestep_embed = forward_timestep_embed
 
 motion_modules: Dict[str, MotionWrapper] = {}
-original_model_hashs = set()
-injected_model_hashs: Dict[str, Tuple[str, str]] = {}
-
-
-def calculate_model_hash(unet):
-    t = unet.input_blocks[1]
-    m = hashlib.sha256()
-    for buf in t.buffers():
-        m.update(buf.cpu().numpy().view(np.uint8))
-    return m.hexdigest()
 
 
 def load_motion_module(model_name: str):
     model_path = get_model_path(model_name)
-    model_hash, is_v2 = validate_mm_model(model_name)
+    model_hash = get_model_hash(model_path)
     if model_hash not in motion_modules:
         logger.info(f"Loading motion module {model_name}")
-        mm_state_dict = load_torch_file(model_path)
-        motion_module = MotionWrapper(model_name, is_v2=is_v2)
-
-        parameters = calculate_parameters(mm_state_dict, "")
-        usefp16 = model_management.should_use_fp16(model_params=parameters)
-        if usefp16:
-            logger.info("Using fp16, converting motion module to fp16")
+        motion_module = MotionWrapper.from_pretrained(model_path)
+        if not cli_args.force_fp32:
+            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_module.load_state_dict(mm_state_dict)
+
         motion_modules[model_hash] = motion_module
 
     return motion_modules[model_hash]
@@ -176,81 +166,6 @@ ejectors = {
 }
 
 
-class AnimateDiffLoaderLegacy:
-    def __init__(self) -> None:
-        self.version = "legacy"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "model_name": (get_available_models(),),
-                "width": ("INT", {"default": 512, "min": 64, "max": 1024, "step": 8}),
-                "height": ("INT", {"default": 512, "min": 64, "max": 1024, "step": 8}),
-                "frame_number": (
-                    "INT",
-                    {"default": 16, "min": 2, "max": 24, "step": 1},
-                ),
-            },
-            "optional": {
-                "init_latent": ("LATENT",),
-            },
-        }
-
-    @classmethod
-    def IS_CHANGED(s, model: ModelPatcher):
-        unet = model.model.diffusion_model
-        # return calculate_model_hash(unet) not in injected_model_hashs
-        return hasattr(unet, "motion_module") and unet.motion_module is not None
-
-    RETURN_TYPES = ("MODEL", "LATENT")
-    CATEGORY = "Animate Diff"
-    FUNCTION = "inject_motion_modules"
-
-    def inject_motion_modules(
-        self,
-        model: ModelPatcher,
-        model_name: str,
-        width: int,
-        height: int,
-        frame_number=16,
-        init_latent: Dict[str, torch.Tensor] = None,
-    ):
-        motion_module = load_motion_module(model_name)
-
-        model = model.clone()
-        unet = model.model.diffusion_model
-        unet_hash = calculate_model_hash(unet)
-        need_inject = unet_hash not in injected_model_hashs
-
-        if unet_hash in injected_model_hashs:
-            (mm_hash, version) = injected_model_hashs[unet_hash]
-            if version != self.version or mm_hash != motion_module.mm_hash:
-                # injected by another motion module, unload first
-                logger.info(f"Ejecting motion module {mm_hash} version {version}.")
-                ejectors[version](unet)
-                need_inject = True
-            else:
-                logger.info(f"Motion module already injected, skipping injection.")
-
-        if need_inject:
-            logger.info(f"Injecting motion module {model_name} version {self.version}.")
-            injectors[self.version](unet, motion_module)
-            unet_hash = calculate_model_hash(unet)
-            injected_model_hashs[unet_hash] = (motion_module.mm_hash, self.version)
-
-        if init_latent is None:
-            latent = torch.zeros([frame_number, 4, height // 8, width // 8]).cpu()
-        else:
-            # clone value of first frame
-            latent = init_latent["samples"][:1, :, :, :].clone().cpu()
-            # repeat for all frames
-            latent = latent.repeat(frame_number, 1, 1, 1)
-
-        return (model, {"samples": latent})
-
-
 class AnimateDiffModuleLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -273,105 +188,6 @@ class AnimateDiffModuleLoader:
         return (motion_module,)
 
 
-class AnimateDiffLoader:
-    def __init__(self) -> None:
-        self.version = "v1"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "init_latent": ("LATENT",),
-                "model_name": (get_available_models(),),
-                "frame_number": (
-                    "INT",
-                    {"default": 16, "min": 2, "max": 32, "step": 1},
-                ),
-            },
-        }
-
-    @classmethod
-    def IS_CHANGED(s, model: ModelPatcher, _):
-        unet = model.model.diffusion_model
-        # return calculate_model_hash(unet) not in injected_model_hashs
-        return hasattr(unet, "motion_module") and unet.motion_module is not None
-
-    RETURN_TYPES = ("MODEL", "LATENT")
-    CATEGORY = "Animate Diff"
-    FUNCTION = "inject_motion_modules"
-
-    def inject_motion_modules(
-        self,
-        model: ModelPatcher,
-        init_latent: Dict[str, torch.Tensor],
-        model_name: str,
-        frame_number=16,
-    ):
-        motion_module = load_motion_module(model_name)
-
-        model = model.clone()
-        unet = model.model.diffusion_model
-        unet_hash = calculate_model_hash(unet)
-        need_inject = unet_hash not in injected_model_hashs
-
-        if unet_hash in injected_model_hashs:
-            (mm_type, version) = injected_model_hashs[unet_hash]
-            if version != self.version or mm_type != motion_module.mm_hash:
-                # injected by another motion module, unload first
-                logger.info(f"Ejecting motion module {mm_type} version {version}.")
-                ejectors[version](unet)
-                need_inject = True
-            else:
-                logger.info(f"Motion module already injected, skipping injection.")
-
-        if need_inject:
-            logger.info(f"Injecting motion module {model_name} version {self.version}.")
-            injectors[self.version](unet, motion_module)
-            unet_hash = calculate_model_hash(unet)
-            injected_model_hashs[unet_hash] = (motion_module.mm_hash, self.version)
-
-        init_frames = len(init_latent["samples"])
-        samples = init_latent["samples"][:init_frames, :, :, :].clone().cpu()
-
-        if init_frames < frame_number:
-            last_frame = samples[-1].unsqueeze(0)
-            repeated_last_frames = last_frame.repeat(
-                frame_number - init_frames, 1, 1, 1
-            )
-            samples = torch.cat((samples, repeated_last_frames), dim=0)
-
-        return (model, {"samples": samples})
-
-
-class AnimateDiffUnload:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"model": ("MODEL",)}}
-
-    @classmethod
-    def IS_CHANGED(s, model: ModelPatcher):
-        unet = model.model.diffusion_model
-        return calculate_model_hash(unet) in injected_model_hashs
-
-    RETURN_TYPES = ("MODEL",)
-    CATEGORY = "Animate Diff"
-    FUNCTION = "unload_motion_modules"
-
-    def unload_motion_modules(self, model: ModelPatcher):
-        model = model.clone()
-        unet = model.model.diffusion_model
-        model_hash = calculate_model_hash(unet)
-        if model_hash in injected_model_hashs:
-            (model_name, version) = injected_model_hashs[model_hash]
-            logger.info(f"Ejecting motion module {model_name} version {version}.")
-            ejectors[version](unet)
-        else:
-            logger.info(f"Motion module not injected, skip unloading.")
-
-        return (model,)
-
-
 class AnimateDiffSampler(KSampler):
     @classmethod
     def INPUT_TYPES(s):
@@ -394,66 +210,56 @@ class AnimateDiffSampler(KSampler):
     def __init__(self) -> None:
         super().__init__()
         self.prev_beta = None
-        self.prev_alpha_cumprod = None
-        self.prev_alpha_cumprod_prev = None
+        self.prev_linear_start = None
+        self.prev_linear_end = None
 
-    def override_ddim_alpha(self, model):
-        logger.info(f"Setting DDIM alpha.")
-        device = model_management.unet_offload_device()
-
-        beta_start = 0.00085
-        beta_end = 0.012
-        betas = torch.linspace(
-            beta_start,
-            beta_end,
-            model.num_timesteps,
-            dtype=torch.float32,
-            device=device,
+    def override_beta_schedule(self, model: BaseModel):
+        logger.info(f"Override beta schedule.")
+        self.prev_beta = model.get_buffer("betas")
+        self.prev_linear_start = model.linear_start
+        self.prev_linear_end = model.linear_end
+        model.register_schedule(
+            given_betas=None,
+            beta_schedule="sqrt_linear",
+            timesteps=1000,
+            linear_start=0.00085,
+            linear_end=0.012,
+            cosine_s=8e-3,
         )
-        alphas = 1.0 - betas
-        alphas_cumprod = torch.cumprod(alphas, dim=0)
-        alphas_cumprod_prev = torch.cat(
-            (
-                torch.tensor([1.0], dtype=torch.float32, device=device),
-                alphas_cumprod[:-1],
-            )
-        )
-        self.prev_beta = model.betas
-        model.betas = betas
-        self.prev_alpha_cumprod = model.alphas_cumprod
-        model.alphas_cumprod = alphas_cumprod
-        self.prev_alpha_cumprod_prev = model.alphas_cumprod_prev
-        model.alphas_cumprod_prev = alphas_cumprod_prev
 
-    def restore_ddim_alpha(self, model):
-        logger.info(f"Restoring DDIM alpha.")
-        model.betas = self.prev_beta
-        model.alphas_cumprod = self.prev_alpha_cumprod
-        model.alphas_cumprod_prev = self.prev_alpha_cumprod_prev
+    def restore_beta_schedule(self, model: BaseModel):
+        logger.info(f"Restoring beta schedule.")
+        model.register_schedule(
+            given_betas=self.prev_beta,
+            linear_start=self.prev_linear_start,
+            linear_end=self.prev_linear_end,
+        )
         self.prev_beta = None
-        self.prev_alpha_cumprod = None
-        self.prev_alpha_cumprod_prev = None
+        self.prev_linear_start = None
+        self.prev_linear_end = None
 
-    def inject_motion_module(self, model, motion_module, inject_method):
+    def inject_motion_module(
+        self, model, motion_module: MotionWrapper, inject_method: str, frame_number: int
+    ):
         model = model.clone()
         unet = model.model.diffusion_model
 
         logger.info(f"Injecting motion module with method {inject_method}.")
         injectors[inject_method](unet, motion_module)
-        self.override_ddim_alpha(model.model)
+        self.override_beta_schedule(model.model)
         if not motion_module.is_v2:
-            logger.info(f"Hacking GroupNorm32 forward function.")
-            GroupNorm32.forward = groupnorm32_mm_forward
+            logger.info(f"Hacking GroupNorm.forward function.")
+            torch.nn.GroupNorm.forward = groupnorm_mm_factory(frame_number)
 
         return model
 
     def eject_motion_module(self, model, inject_method):
         unet = model.model.diffusion_model
 
-        self.restore_ddim_alpha(model.model)
+        self.restore_beta_schedule(model.model)
         if not unet.motion_module.is_v2:
             logger.info(f"Restore GroupNorm32 forward function.")
-            GroupNorm32.forward = groupnorm32_original_forward
+            torch.nn.GroupNorm.forward = orig_groupnorm_forward
 
         logger.info(f"Ejecting motion module with method {inject_method}.")
         ejectors[inject_method](unet)
@@ -474,7 +280,9 @@ class AnimateDiffSampler(KSampler):
         latent_image,
         denoise=1.0,
     ):
-        model = self.inject_motion_module(model, motion_module, inject_method)
+        model = self.inject_motion_module(
+            model, motion_module, inject_method, frame_number
+        )
 
         init_frames = len(latent_image["samples"])
         samples = latent_image["samples"][:init_frames, :, :, :].clone().cpu()
@@ -488,22 +296,23 @@ class AnimateDiffSampler(KSampler):
 
         latent_image = {"samples": samples}
 
-        results = super().sample(
-            model,
-            seed,
-            steps,
-            cfg,
-            sampler_name,
-            scheduler,
-            positive,
-            negative,
-            latent_image,
-            denoise=1.0,
-        )
-
-        self.eject_motion_module(model, inject_method)
-
-        return results
+        try:
+            return super().sample(
+                model,
+                seed,
+                steps,
+                cfg,
+                sampler_name,
+                scheduler,
+                positive,
+                negative,
+                latent_image,
+                denoise=denoise,
+            )
+        except:
+            raise
+        finally:
+            self.eject_motion_module(model, inject_method)
 
 
 class AnimateDiffCombine:
@@ -603,17 +412,11 @@ class AnimateDiffCombine:
 
 
 NODE_CLASS_MAPPINGS = {
-    # "AnimateDiffLoader": AnimateDiffLoaderLegacy,
-    # "AnimateDiffLoader_v2": AnimateDiffLoader,
-    # "AnimateDiffUnload": AnimateDiffUnload,
     "AnimateDiffModuleLoader": AnimateDiffModuleLoader,
     "AnimateDiffCombine": AnimateDiffCombine,
     "AnimateDiffSampler": AnimateDiffSampler,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
-    # "AnimateDiffLoader": "[DEPRECATED] Animate Diff Loader Legacy",
-    # "AnimateDiffLoader_v2": "[DEPRECATED] Animate Diff Loader",
-    # "AnimateDiffUnload": "[DEPRECATED] Animate Diff Unload",
     "AnimateDiffModuleLoader": "Animate Diff Module Loader",
     "AnimateDiffSampler": "Animate Diff Sampler",
     "AnimateDiffCombine": "Animate Diff Combine",

workflow.json

@@ -0,0 +1,451 @@
+{
+  "last_node_id": 20,
+  "last_link_id": 35,
+  "nodes": [
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        415,
+        186
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            29
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
+      "pos": [
+        1253,
+        191
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 28
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 20
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            19
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 12,
+      "type": "AnimateDiffCombine",
+      "pos": [
+        1254,
+        290
+      ],
+      "size": {
+        "0": 315,
+        "1": 342
+      },
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 19
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AnimateDiffCombine"
+      },
+      "widgets_values": [
+        8,
+        0,
+        "Enabled",
+        "AnimateDiff"
+      ]
+    },
+    {
+      "id": 16,
+      "type": "AnimateDiffModuleLoader",
+      "pos": [
+        27,
+        345
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MOTION_MODULE",
+          "type": "MOTION_MODULE",
+          "links": [
+            24
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AnimateDiffModuleLoader"
+      },
+      "widgets_values": [
+        "mm-Stabilized_mid.pth"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        26,
+        474
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            25
+          ],
+          "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": [
+        "AnimeLike25D_v11.safetensors"
+      ]
+    },
+    {
+      "id": 13,
+      "type": "VAELoader",
+      "pos": [
+        28,
+        223
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            20
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "klF8Anime2.ckpt"
+      ]
+    },
+    {
+      "id": 15,
+      "type": "AnimateDiffSampler",
+      "pos": [
+        882,
+        192
+      ],
+      "size": {
+        "0": 315,
+        "1": 330
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "motion_module",
+          "type": "MOTION_MODULE",
+          "link": 24,
+          "slot_index": 0
+        },
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 25,
+          "slot_index": 1
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 29
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 30
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 35
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            28
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AnimateDiffSampler"
+      },
+      "widgets_values": [
+        "default",
+        16,
+        345029849956677,
+        "fixed",
+        20,
+        8,
+        "euler",
+        "normal",
+        0.8
+      ]
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        413,
+        389
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 5
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            30
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "embedding:easynegative, embedding:badhandv4, "
+      ]
+    },
+    {
+      "id": 20,
+      "type": "EmptyLatentImage",
+      "pos": [
+        522,
+        621
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            35
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    }
+  ],
+  "links": [
+    [
+      3,
+      4,
+      1,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
+      5,
+      4,
+      1,
+      7,
+      0,
+      "CLIP"
+    ],
+    [
+      19,
+      8,
+      0,
+      12,
+      0,
+      "IMAGE"
+    ],
+    [
+      20,
+      13,
+      0,
+      8,
+      1,
+      "VAE"
+    ],
+    [
+      24,
+      16,
+      0,
+      15,
+      0,
+      "MOTION_MODULE"
+    ],
+    [
+      25,
+      4,
+      0,
+      15,
+      1,
+      "MODEL"
+    ],
+    [
+      28,
+      15,
+      0,
+      8,
+      0,
+      "LATENT"
+    ],
+    [
+      29,
+      6,
+      0,
+      15,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      30,
+      7,
+      0,
+      15,
+      3,
+      "CONDITIONING"
+    ],
+    [
+      35,
+      20,
+      0,
+      15,
+      4,
+      "LATENT"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}