fix: compatible with 3.9 python syntax

Fixed code to avoid conflict with other nodes. This code will be executed only inside the AnimateDiffCombine node now.

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -11,6 +11,48 @@
 - 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)
 
+## Update 2023/09/25
+
+#### **Motion LoRA** is now supported!
+
+Download [motion LoRAs](https://huggingface.co/guoyww/animatediff/tree/main) and put them under `comfyui-animatediff/loras/` folder.
+
+Note: LoRAs only work with **AnimateDiff v2** [mm_sd_v15_v2.ckpt](https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15_v2.ckpt) module.
+
+#### New node: `AnimateDiffLoraLoader`
+
+<img width="370" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/7a9f62f7-702e-48a4-934c-bbfe1e23aff2">
+
+Example workflow:
+<img width="1280" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/93e7550f-4648-4482-9961-6cece5132dc9">
+
+Workflow: [lora.json](https://github.com/ArtVentureX/comfyui-animatediff/blob/main/workflows/lora.json)
+
+Samples:
+
+<table>
+<tr>
+<td>
+<img width="512" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/2c5aa25e-0682-481f-8842-066c5b988864">
+</td>
+</tr>
+<tr>
+<td>
+<img width="512" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/adfbad45-3ba5-42e3-9bee-d2b83f43989c">
+</td>
+</tr>
+<tr>
+<td>
+<img width="512" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/8e484c74-c691-4d1c-9514-719dbfe3a0b5">
+</td>
+</tr>
+<tr>
+<td>
+<img width="512" alt="image" src="https://github.com/ArtVentureX/comfyui-animatediff/assets/133728487/4921a335-9207-4a7b-9d66-61a5d76e3179">
+</td>
+</tr>
+</table>
+
 ## Update 2023/09/21
 
 #### **Sliding Window** is now available!

animatediff/model_utils.py

@@ -1,5 +1,6 @@
 import os
 import hashlib
+import torch
 from typing import Dict
 
 import folder_paths
@@ -11,6 +12,7 @@ from .motion_module import MotionWrapper
 
 
 motion_modules: Dict[str, MotionWrapper] = {}
+motion_loras: Dict[str, Dict[str, torch.Tensor]] = {}
 
 
 folder_paths.folder_names_and_paths["AnimateDiff"] = (
@@ -20,19 +22,34 @@ folder_paths.folder_names_and_paths["AnimateDiff"] = (
     ],
     folder_paths.supported_pt_extensions,
 )
+folder_paths.folder_names_and_paths["AnimateDiffLora"] = (
+    [
+        os.path.join(folder_paths.models_dir, "AnimateDiffLora"),
+        os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "loras"),
+    ],
+    folder_paths.supported_pt_extensions,
+)
 
 
 def get_available_models():
     return folder_paths.get_filename_list("AnimateDiff")
 
 
+def get_available_loras():
+    return folder_paths.get_filename_list("AnimateDiffLora")
+
+
 def get_model_path(model_name):
     return folder_paths.get_full_path("AnimateDiff", model_name)
 
 
+def get_lora_path(lora_name):
+    return folder_paths.get_full_path("AnimateDiffLora", lora_name)
+
+
 def get_model_hash(file_path):
     with open(file_path, "rb") as f:
-        bytes = f.read()  # read entire file as bytes
+        bytes = f.read(1024 * 1024)  # read entire file as bytes
         return hashlib.sha256(bytes).hexdigest()
 
 
@@ -54,3 +71,30 @@ def load_motion_module(model_name: str):
         motion_modules[model_hash] = motion_module
 
     return motion_modules[model_hash]
+
+
+def load_lora(lora_name: str):
+    lora_path = get_lora_path(lora_name)
+    lora_hash = get_model_hash(lora_path)
+    if lora_hash not in motion_modules:
+        logger.info(f"Loading lora {lora_name}")
+        state_dict = load_torch_file(lora_path)
+        updated_state_dict: Dict[str, torch.Tensor] = {}
+
+        for key in state_dict:
+            # only process lora down key
+            if "up." in key:
+                continue
+
+            up_key = key.replace(".down.", ".up.")
+            model_key = key.replace("processor.", "").replace("_lora", "").replace("down.", "").replace("up.", "")
+            model_key = model_key.replace("to_out.", "to_out.0.")
+            combined_key = ".".join(model_key.split(".")[:-1])
+
+            weight_down = state_dict[key]
+            weight_up = state_dict[up_key]
+            updated_state_dict[combined_key] = torch.mm(weight_up, weight_down).to("cpu")
+
+        motion_loras[lora_hash] = updated_state_dict
+
+    return motion_loras[lora_hash]

animatediff/motion_module.py

@@ -6,25 +6,30 @@ from einops import rearrange, repeat
 
 import comfy.model_management as model_management
 from comfy.ldm.modules.attention import (
+    default,
     FeedForward,
     CrossAttention as ComfyCrossAttention,
-    CrossAttentionDoggettx,
+    attention_basic,
-    CrossAttentionBirchSan,
+    attention_pytorch,
+    attention_split,
+    attention_sub_quad,
 )
 from comfy.cli_args import args
 
 from .logger import logger
 
-CrossAttention = ComfyCrossAttention
+attention = attention_basic
 
 if model_management.xformers_enabled():
     logger.warn("xformers is enabled but it has a bug that can cause issue while using with AnimateDiff.")
+
+if model_management.pytorch_attention_enabled():
+    attention = attention_pytorch
+else:
     if args.use_split_cross_attention:
-        logger.warn("Using split optimization for AnimateDiff cross attention instead.")
+        attention = attention_split
-        CrossAttention = CrossAttentionDoggettx
     else:
-        logger.warn("Using sub quadratic optimization for AnimateDiff cross attention instead.")
+        attention = attention_sub_quad
-        CrossAttention = CrossAttentionBirchSan
 
 
 def zero_module(module):
@@ -51,6 +56,24 @@ def has_mid_block(mm_state_dict: dict[str, Tensor]):
     return False
 
 
+class CrossAttention(ComfyCrossAttention):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x, context=None, value=None, mask=None):
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        if value is not None:
+            v = self.to_v(value)
+            del value
+        else:
+            v = self.to_v(context)
+
+        out = attention(q, k, v, self.heads, mask)
+        return self.to_out(out)
+
+
 class MotionWrapper(nn.Module):
     def __init__(self, mm_type: str, encoding_max_len: int = 24, is_v2=False):
         super().__init__()
@@ -156,7 +179,7 @@ class VanillaTemporalModule(nn.Module):
     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):
+    def forward(self, input_tensor, encoder_hidden_states=None, attention_mask=None):
         return self.temporal_transformer(input_tensor, encoder_hidden_states, attention_mask)
 
 

animatediff/nodes.py

@@ -3,16 +3,18 @@ import json
 import torch
 import numpy as np
 import hashlib
-from typing import List
+from typing import List, Dict, Tuple
 from torch import Tensor
 from PIL import Image, ImageSequence
 from PIL.PngImagePlugin import PngInfo
 
 import folder_paths
 
-from .model_utils import get_available_models, load_motion_module
+from .motion_module import MotionWrapper
+from .model_utils import get_available_models, load_motion_module, get_available_loras, load_lora
 from .utils import pil2tensor, ensure_opencv
 from .sampler import AnimateDiffSampler, AnimateDiffSlidingWindowOptions
+from .logger import logger
 
 
 SLIDING_CONTEXT_LENGTH = 16
@@ -28,21 +30,99 @@ class AnimateDiffModuleLoader:
             "required": {
                 "model_name": (get_available_models(),),
             },
+            "optional": {
+                "lora_stack": ("MOTION_LORA_STACK",),
+            },
         }
 
     RETURN_TYPES = ("MOTION_MODULE",)
     CATEGORY = "Animate Diff"
     FUNCTION = "load_motion_module"
 
+    def inject_loras(self, motion_module: MotionWrapper, lora_stack: List[Tuple[Dict[str, Tensor], float]]):
+        for lora in lora_stack:
+            (state_dict, alpha) = lora
+
+            for key in state_dict:
+                layer_infos = key.split(".")
+
+                curr_layer = motion_module
+                while len(layer_infos) > 0:
+                    temp_name = layer_infos.pop(0)
+                    curr_layer = curr_layer.__getattr__(temp_name)
+
+                curr_layer.weight.data += alpha * state_dict[key].to(curr_layer.weight.data.device)
+
+    def eject_loras(self, motion_module: MotionWrapper, lora_stack: List[Tuple[float, Dict[str, Tensor]]]):
+        lora_stack.reverse() # should not matter but just in case
+        for lora in lora_stack:
+            (state_dict, alpha) = lora
+
+            for key in state_dict:
+                layer_infos = key.split(".")
+
+                curr_layer = motion_module
+                while len(layer_infos) > 0:
+                    temp_name = layer_infos.pop(0)
+                    curr_layer = curr_layer.__getattr__(temp_name)
+
+                curr_layer.weight.data -= alpha * state_dict[key].to(curr_layer.weight.data.device)
+
     def load_motion_module(
         self,
         model_name: str,
+        lora_stack: List = None,
     ):
         motion_module = load_motion_module(model_name)
 
+        # inject loras
+        if motion_module.is_v2:
+            if hasattr(motion_module, "lora_stack") and isinstance(motion_module.lora_stack, list):
+                self.eject_loras(motion_module, motion_module.lora_stack)
+                delattr(motion_module, "lora_stack")
+
+            if isinstance(lora_stack, list):
+                self.inject_loras(motion_module, lora_stack)
+                setattr(motion_module, "lora_stack", lora_stack)
+
+        elif isinstance(lora_stack, list):
+            logger.warning("LoRA is provided but only motion module v2 is supported.")
+
         return (motion_module,)
 
 
+class AnimateDiffLoraLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "lora_name": (get_available_loras(),),
+                "alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
+            },
+            "optional": {
+                "lora_stack": ("MOTION_LORA_STACK",),
+            },
+        }
+
+    RETURN_TYPES = ("MOTION_LORA_STACK",)
+    CATEGORY = "Animate Diff"
+    FUNCTION = "load_lora"
+
+    def load_lora(
+        self,
+        lora_name: str,
+        alpha: float,
+        lora_stack: List = None,
+    ):
+        if not lora_stack:
+            lora_stack = []
+
+        lora = load_lora(lora_name)
+        lora_stack.append((lora, alpha))
+
+        return (lora_stack,)
+
+
 class AnimateDiffCombine:
     @classmethod
     def INPUT_TYPES(s):
@@ -324,6 +404,7 @@ class ImageChunking:
 
 NODE_CLASS_MAPPINGS = {
     "AnimateDiffModuleLoader": AnimateDiffModuleLoader,
+    "AnimateDiffLoraLoader": AnimateDiffLoraLoader,
     "AnimateDiffCombine": AnimateDiffCombine,
     "AnimateDiffSampler": AnimateDiffSampler,
     "AnimateDiffSlidingWindowOptions": AnimateDiffSlidingWindowOptions,
@@ -332,6 +413,7 @@ NODE_CLASS_MAPPINGS = {
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "AnimateDiffModuleLoader": "Animate Diff Module Loader",
+    "AnimateDiffLoraLoader": "Animate Diff Lora Loader",
     "AnimateDiffSampler": "Animate Diff Sampler",
     "AnimateDiffSlidingWindowOptions": "Sliding Window Options",
     "AnimateDiffCombine": "Animate Diff Combine",

animatediff/sampler.py

@@ -4,9 +4,7 @@ from torch.nn.functional import group_norm
 from einops import rearrange
 
 import comfy.ldm.modules.diffusionmodules.openaimodel as openaimodel
-import comfy.model_management as model_management
+from comfy.model_base import BaseModel, model_sampling
-from comfy.model_base import BaseModel
-from comfy.ldm.modules.attention import SpatialTransformer
 from nodes import KSampler
 
 from .logger import logger
@@ -18,19 +16,19 @@ from .sliding_context_sampling import SlidingContext, inject_sampling_function,
 SLIDING_CONTEXT_LENGTH = 16
 
 
-def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None):
+class ModelSamplingConfig:
+    def __init__(self, beta_schedule: str):
+        self.sampling_settings = {}
+        self.sampling_settings["beta_schedule"] = beta_schedule
+
+
+def forward_timestep_embed(ts, x, emb, context=None, *args, **kwargs):
     for layer in ts:
-        if isinstance(layer, openaimodel.TimestepBlock):
+        if isinstance(layer, VanillaTemporalModule):
-            x = layer(x, emb)
-        elif isinstance(layer, VanillaTemporalModule):
             x = layer(x, context)
-        elif isinstance(layer, SpatialTransformer):
-            x = layer(x, context, transformer_options)
-            transformer_options["current_index"] += 1
-        elif isinstance(layer, openaimodel.Upsample):
-            x = layer(x, output_shape=output_shape)
         else:
-            x = layer(x)
+            x = orig_forward_timestep_embed([layer], x, emb, context, *args, **kwargs)
+
     return x
 
 
@@ -49,7 +47,6 @@ def groupnorm_mm_factory(video_length: int):
 
 
 orig_forward_timestep_embed = openaimodel.forward_timestep_embed
-orig_maximum_batch_area = model_management.maximum_batch_area
 orig_groupnorm_forward = torch.nn.GroupNorm.forward
 
 
@@ -181,32 +178,17 @@ class AnimateDiffSampler(KSampler):
 
     def __init__(self) -> None:
         super().__init__()
-        self.prev_beta = None
+        self.model_sampling = None
-        self.prev_linear_start = None
-        self.prev_linear_end = None
 
     def override_beta_schedule(self, model: BaseModel):
-        self.prev_beta = model.get_buffer("betas").cpu().clone().detach()
+        self.model_sampling = model.model_sampling
-        self.prev_linear_start = model.linear_start
+        model.model_sampling = model_sampling(
-        self.prev_linear_end = model.linear_end
+            ModelSamplingConfig(beta_schedule="sqrt_linear"), model_type=model.model_type
-        model.register_schedule(
-            given_betas=None,
-            beta_schedule="sqrt_linear",
-            timesteps=1000,
-            linear_start=0.00085,
-            linear_end=0.012,
-            cosine_s=8e-3,
         )
 
     def restore_beta_schedule(self, model: BaseModel):
-        model.register_schedule(
+        model.model_sampling = self.model_sampling
-            given_betas=self.prev_beta,
+        self.model_sampling = None
-            linear_start=self.prev_linear_start,
-            linear_end=self.prev_linear_end,
-        )
-        self.prev_beta = None
-        self.prev_linear_start = None
-        self.prev_linear_end = None
 
     def inject_motion_module(self, model, motion_module: MotionWrapper, inject_method: str, frame_number: int):
         model = model.clone()

animatediff/sliding_context_sampling.py

@@ -1,6 +1,7 @@
+import math
 import torch
 from torch import Tensor
-import math
+from typing import List, Dict
 
 import comfy.utils
 import comfy.sample
@@ -17,6 +18,10 @@ orig_comfy_sample = comfy.sample.sample
 orig_sampling_function = comfy_samplers.sampling_function
 
 
+def lcm(a, b):
+    return abs(a * b) // math.gcd(a, b)
+
+
 class SlidingContext:
     def __init__(
         self,
@@ -70,47 +75,34 @@ def __sliding_sample_factory(ctx: SlidingContext):
 
             ctx.current_step = start_step + step + 1
 
-        try:
+        return orig_comfy_sample(model, *args, **kwargs, callback=callback)
-            return orig_comfy_sample(model, *args, **kwargs, callback=callback)
-        except RuntimeError as e:
-            if str(e).startswith("CUDA error: invalid configuration argument"):
-                raise RuntimeError(
-                    f"An xformers bug was encountered in AnimateDiff - to run your workflow, \
-                                    disable xformers in ComfyUI using '--disable-xformers' startup argument."
-                )
-            raise
 
-    def sampling_function(
+    def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
-        model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}, seed=None
+        def get_area_and_mult(conds, x_in, timestep_in):
-    ):
-        def get_area_and_mult(cond, x_in, cond_concat_in, timestep_in):
             area = (x_in.shape[2], x_in.shape[3], 0, 0)
             strength = 1.0
-            if "timestep_start" in cond[1]:
+
-                timestep_start = cond[1]["timestep_start"]
+            if "timestep_start" in conds:
+                timestep_start = conds["timestep_start"]
                 if timestep_in[0] > timestep_start:
                     return None
-            if "timestep_end" in cond[1]:
+            if "timestep_end" in conds:
-                timestep_end = cond[1]["timestep_end"]
+                timestep_end = conds["timestep_end"]
                 if timestep_in[0] < timestep_end:
                     return None
-            if "area" in cond[1]:
+            if "area" in conds:
-                area = cond[1]["area"]
+                area = conds["area"]
-            if "strength" in cond[1]:
+            if "strength" in conds:
-                strength = cond[1]["strength"]
+                strength = conds["strength"]
-
-            adm_cond = None
-            if "adm_encoded" in cond[1]:
-                adm_cond = cond[1]["adm_encoded"]
 
             input_x = x_in[:, :, area[2] : area[0] + area[2], area[3] : area[1] + area[3]]
-            if "mask" in cond[1]:
+            if "mask" in conds:
                 # Scale the mask to the size of the input
                 # The mask should have been resized as we began the sampling process
                 mask_strength = 1.0
-                if "mask_strength" in cond[1]:
+                if "mask_strength" in conds:
-                    mask_strength = cond[1]["mask_strength"]
+                    mask_strength = conds["mask_strength"]
-                mask = cond[1]["mask"]
+                mask = conds["mask"]
                 assert mask.shape[1] == x_in.shape[2]
                 assert mask.shape[2] == x_in.shape[3]
                 mask = mask[:, area[2] : area[0] + area[2], area[3] : area[1] + area[3]] * mask_strength
@@ -119,7 +111,7 @@ def __sliding_sample_factory(ctx: SlidingContext):
                 mask = torch.ones_like(input_x)
             mult = mask * strength
 
-            if "mask" not in cond[1]:
+            if "mask" not in conds:
                 rr = 8
                 if area[2] != 0:
                     for t in range(rr):
@@ -135,24 +127,17 @@ def __sliding_sample_factory(ctx: SlidingContext):
                         mult[:, :, :, area[1] - 1 - t : area[1] - t] *= (1.0 / rr) * (t + 1)
 
             conditionning = {}
-            conditionning["c_crossattn"] = cond[0]
+            model_conds = conds["model_conds"]
-            if cond_concat_in is not None and len(cond_concat_in) > 0:
+            for c in model_conds:
-                cropped = []
+                conditionning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
-                for x in cond_concat_in:
-                    cr = x[:, :, area[2] : area[0] + area[2], area[3] : area[1] + area[3]]
-                    cropped.append(cr)
-                conditionning["c_concat"] = torch.cat(cropped, dim=1)
-
-            if adm_cond is not None:
-                conditionning["c_adm"] = adm_cond
 
             control = None
-            if "control" in cond[1]:
+            if "control" in conds:
-                control = cond[1]["control"]
+                control = conds["control"]
 
             patches = None
-            if "gligen" in cond[1]:
+            if "gligen" in conds:
-                gligen = cond[1]["gligen"]
+                gligen = conds["gligen"]
                 patches = {}
                 gligen_type = gligen[0]
                 gligen_model = gligen[1]
@@ -170,24 +155,8 @@ def __sliding_sample_factory(ctx: SlidingContext):
                 return True
             if c1.keys() != c2.keys():
                 return False
-            if "c_crossattn" in c1:
+            for k in c1:
-                s1 = c1["c_crossattn"].shape
+                if not c1[k].can_concat(c2[k]):
-                s2 = c2["c_crossattn"].shape
-                if s1 != s2:
-                    if s1[0] != s2[0] or s1[2] != s2[2]:  # these 2 cases should not happen
-                        return False
-
-                    mult_min = comfy_samplers.lcm(s1[1], s2[1])
-                    diff = mult_min // min(s1[1], s2[1])
-                    if (
-                        diff > 4
-                    ):  # arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
-                        return False
-            if "c_concat" in c1:
-                if c1["c_concat"].shape != c2["c_concat"].shape:
-                    return False
-            if "c_adm" in c1:
-                if c1["c_adm"].shape != c2["c_adm"].shape:
                     return False
             return True
 
@@ -216,55 +185,41 @@ def __sliding_sample_factory(ctx: SlidingContext):
             c_concat = []
             c_adm = []
             crossattn_max_len = 0
-            for x in c_list:
-                if "c_crossattn" in x:
-                    c = x["c_crossattn"]
-                    if crossattn_max_len == 0:
-                        crossattn_max_len = c.shape[1]
-                    else:
-                        crossattn_max_len = comfy_samplers.lcm(crossattn_max_len, c.shape[1])
-                    c_crossattn.append(c)
-                if "c_concat" in x:
-                    c_concat.append(x["c_concat"])
-                if "c_adm" in x:
-                    c_adm.append(x["c_adm"])
-            out = {}
-            c_crossattn_out = []
-            for c in c_crossattn:
-                if c.shape[1] < crossattn_max_len:
-                    c = c.repeat(1, crossattn_max_len // c.shape[1], 1)  # padding with repeat doesn't change result
-                c_crossattn_out.append(c)
 
-            if len(c_crossattn_out) > 0:
+            temp = {}
-                out["c_crossattn"] = torch.cat(c_crossattn_out)
+            for x in c_list:
-            if len(c_concat) > 0:
+                for k in x:
-                out["c_concat"] = torch.cat(c_concat)
+                    cur = temp.get(k, [])
-            if len(c_adm) > 0:
+                    cur.append(x[k])
-                out["c_adm"] = torch.cat(c_adm)
+                    temp[k] = cur
+
+            out = {}
+            for k in temp:
+                conds = temp[k]
+                out[k] = conds[0].concat(conds[1:])
+
             return out
 
-        def calc_cond_uncond_batch(
+        def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
-            model_function, cond, uncond, x_in, timestep, max_total_area, cond_concat_in, model_options
-        ):
             out_cond = torch.zeros_like(x_in)
-            out_count = torch.ones_like(x_in) / 100000.0
+            out_count = torch.ones_like(x_in) * 1e-37
 
             out_uncond = torch.zeros_like(x_in)
-            out_uncond_count = torch.ones_like(x_in) / 100000.0
+            out_uncond_count = torch.ones_like(x_in) * 1e-37
 
             COND = 0
             UNCOND = 1
 
             to_run = []
             for x in cond:
-                p = get_area_and_mult(x, x_in, cond_concat_in, timestep)
+                p = get_area_and_mult(x, x_in, timestep)
                 if p is None:
                     continue
 
                 to_run += [(p, COND)]
             if uncond is not None:
                 for x in uncond:
-                    p = get_area_and_mult(x, x_in, cond_concat_in, timestep)
+                    p = get_area_and_mult(x, x_in, timestep)
                     if p is None:
                         continue
 
@@ -281,9 +236,11 @@ def __sliding_sample_factory(ctx: SlidingContext):
                 to_batch_temp.reverse()
                 to_batch = to_batch_temp[:1]
 
+                free_memory = model_management.get_free_memory(x_in.device)
                 for i in range(1, len(to_batch_temp) + 1):
                     batch_amount = to_batch_temp[: len(to_batch_temp) // i]
-                    if len(batch_amount) * first_shape[0] * first_shape[2] * first_shape[3] < max_total_area:
+                    input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
+                    if model.memory_required(input_shape) < free_memory:
                         to_batch = batch_amount
                         break
 
@@ -333,11 +290,11 @@ def __sliding_sample_factory(ctx: SlidingContext):
 
                 if "model_function_wrapper" in model_options:
                     output = model_options["model_function_wrapper"](
-                        model_function,
+                        model.apply_model,
                         {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond},
                     ).chunk(batch_chunks)
                 else:
-                    output = model_function(input_x, timestep_, **c).chunk(batch_chunks)
+                    output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
                 del input_x
 
                 for o in range(batch_chunks):
@@ -361,14 +318,11 @@ def __sliding_sample_factory(ctx: SlidingContext):
             del out_count
             out_uncond /= out_uncond_count
             del out_uncond_count
-
             return out_cond, out_uncond
 
         # sliding_calc_cond_uncond_batch inspired by ashen's initial hack for 16-frame sliding context:
         # https://github.com/comfyanonymous/ComfyUI/compare/master...ashen-sensored:ComfyUI:master
-        def sliding_calc_cond_uncond_batch(
+        def sliding_calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
-            model_function, cond, uncond, x_in, timestep, max_total_area, cond_concat_in, model_options
-        ):
             # figure out how input is split
             axes_factor = x.size(0) // ctx.video_length
 
@@ -384,37 +338,29 @@ def __sliding_sample_factory(ctx: SlidingContext):
                 control.full_latent_length = ctx.video_length
                 control.context_length = ctx.context_length
 
-            def get_resized_cond(cond_in, full_idxs) -> list:
+            def get_resized_cond(cond_in: List[Dict], full_idxs) -> list:
                 # reuse or resize cond items to match context requirements
                 resized_cond = []
                 # cond object is a list containing a list - outer list is irrelevant, so just loop through it
                 for actual_cond in cond_in:
-                    resized_actual_cond = []
+                    new_cond_item = actual_cond.copy()
-                    # now we are in the inner list - index 0 is tensor, index 1 is dictionary
+                    for key, cond_item in new_cond_item.items():
-                    for cond_idx, cond_item in enumerate(actual_cond):
                         if isinstance(cond_item, Tensor):
                             # check that tensor is the expected length - x.size(0)
                             if cond_item.size(0) == x.size(0):
-                                pass
                                 # if so, it's subsetting time - tell controls the expected indeces so they can handle them
                                 actual_cond_item = cond_item[full_idxs]
-                                resized_actual_cond.append(actual_cond_item)
+                                new_cond_item[key] = actual_cond_item
+                        elif key == "control":
+                            control_item = cond_item
+                            if hasattr(control_item, "sub_idxs"):
+                                prepare_control_objects(control_item, full_idxs)
                             else:
-                                resized_actual_cond.append(cond_item)
+                                raise ValueError(
-                        elif isinstance(cond_item, dict):
+                                    f"Control type {type(control_item).__name__} may not support required features for sliding context window; use Control objects from Kosinkadink/Advanced-ControlNet nodes."
-                            # when in dictionary, look for control
+                                )
-                            if "control" in cond_item:
+                            new_cond_item[key] = cond_item
-                                control_item = cond_item["control"]
+                    resized_cond.append(new_cond_item)
-                                if hasattr(control_item, "sub_idxs"):
-                                    prepare_control_objects(control_item, full_idxs)
-                                else:
-                                    raise ValueError(
-                                        f"Control type {type(control_item).__name__} may not support required features for sliding context window; use Control objects from Kosinkadink/Advanced-ControlNet nodes."
-                                    )
-                            resized_actual_cond.append(cond_item)
-                        else:
-                            resized_actual_cond.append(cond_item)
-                    resized_cond.append(resized_actual_cond)
                 return resized_cond
 
             # perform calc_cond_uncond_batch per context window
@@ -437,16 +383,13 @@ def __sliding_sample_factory(ctx: SlidingContext):
                 sub_timestep = timestep[full_idxs]
                 sub_cond = get_resized_cond(cond, full_idxs) if cond is not None else None
                 sub_uncond = get_resized_cond(uncond, full_idxs) if uncond is not None else None
-                sub_cond_concat = get_resized_cond(cond_concat, full_idxs) if cond_concat is not None else None
 
                 sub_cond_out, sub_uncond_out = calc_cond_uncond_batch(
-                    model_function,
+                    model,
                     sub_cond,
                     sub_uncond,
                     sub_x,
                     sub_timestep,
-                    max_total_area,
-                    sub_cond_concat,
                     model_options,
                 )
 
@@ -459,17 +402,21 @@ def __sliding_sample_factory(ctx: SlidingContext):
             uncond_final /= out_count_final
             return cond_final, uncond_final
 
-        max_total_area = model_management.maximum_batch_area()
         if math.isclose(cond_scale, 1.0):
             uncond = None
 
-        cond, uncond = sliding_calc_cond_uncond_batch(
+        cond, uncond = sliding_calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options)
-            model_function, cond, uncond, x, timestep, max_total_area, cond_concat, model_options
-        )
 
         if "sampler_cfg_function" in model_options:
-            args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep}
+            args = {
-            return model_options["sampler_cfg_function"](args)
+                "cond": x - cond,
+                "uncond": x - uncond,
+                "cond_scale": cond_scale,
+                "timestep": timestep,
+                "input": x,
+                "sigma": timestep,
+            }
+            return x - model_options["sampler_cfg_function"](args)
         else:
             return uncond + (cond - uncond) * cond_scale
 
@@ -477,6 +424,10 @@ def __sliding_sample_factory(ctx: SlidingContext):
 
 
 def inject_sampling_function(ctx: SlidingContext):
+    global orig_comfy_sample, orig_sampling_function
+    orig_comfy_sample = comfy.sample.sample
+    orig_sampling_function = comfy_samplers.sampling_function
+
     (sample, sampling_function) = __sliding_sample_factory(ctx)
     comfy.sample.sample = sample
     comfy_samplers.sampling_function = sampling_function

animatediff/sliding_schedule.py

@@ -142,14 +142,12 @@ def uniform_constant(
             # yield if not skipped
             yield to_yield
 
-
 def get_context_scheduler(name: str) -> Callable:
-    match name:
+    if name == ContextSchedules.UNIFORM:
-        case ContextSchedules.UNIFORM:
+        return uniform
-            return uniform
+    elif name == ContextSchedules.UNIFORM_CONSTANT:
-        case ContextSchedules.UNIFORM_CONSTANT:
+        return uniform_constant
-            return uniform_constant
+    elif name == ContextSchedules.UNIFORM_V2:
-        case ContextSchedules.UNIFORM_V2:
+        return uniform_v2
-            return uniform_v2
+    else:
-        case _:
+        raise ValueError(f"Unknown context_overlap policy {name}")
-            raise ValueError(f"Unknown context_overlap policy {name}")

web/js/vid_preview.js

@@ -1,162 +1,245 @@
-import { app } from "../../../scripts/app.js";
+import { app, ANIM_PREVIEW_WIDGET } from '../../../scripts/app.js';
 import { api } from "../../../scripts/api.js";
+import { $el } from '../../../scripts/ui.js';
+import { createImageHost } from "../../../scripts/ui/imagePreview.js"
 
-function offsetDOMWidget(widget, ctx, node, widgetWidth, widgetY, height) {
+const URL_REGEX = /^(https?:\/\/|\/view\?|data:image\/)/;
-  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);
+const style = `
-  Object.assign(widget.inputEl.style, {
+.comfy-img-preview video {
-    transformOrigin: "0 0",
+  object-fit: contain;
-    transform: scale,
+  width: var(--comfy-img-preview-width);
-    left: `${transform.e}px`,
+  height: var(--comfy-img-preview-height);
-    top: `${transform.d + transform.f}px`,
+}
-    width: `${widgetWidth}px`,
+`;
-    height: `${(height || widget.parent?.inputHeight || 32) - margin}px`,
+
-    position: "absolute",
+export function chainCallback(object, property, callback) {
-    background: !node.color ? "" : node.color,
+  if (object == undefined) {
-    color: !node.color ? "" : "white",
+    //This should not happen.
-    zIndex: 5, //app.graph._nodes.indexOf(node),
+    console.error("Tried to add callback to non-existant object");
+    return;
+  }
+  if (property in object) {
+    const callback_orig = object[property];
+    object[property] = function () {
+      const r = callback_orig.apply(this, arguments);
+      callback.apply(this, arguments);
+      return r;
+    };
+  } else {
+    object[property] = callback;
+  }
+};
+
+export function formatUploadedUrl(params) {
+  if (params.url) {
+    return params.url;
+  }
+
+  params = { ...params };
+
+  if (!params.filename && params.name) {
+    params.filename = params.name;
+    delete params.name;
+  }
+
+  return api.apiURL("/view?" + new URLSearchParams(params));
+};
+
+export function addVideoPreview(nodeType, options = {}) {
+  const createVideoNode = (url) => {
+    return new Promise((cb) => {
+      const videoEl = document.createElement('video');
+      Object.defineProperty(videoEl, 'naturalWidth', {
+        get: () => {
+          return videoEl.videoWidth;
+        },
+      });
+      Object.defineProperty(videoEl, 'naturalHeight', {
+        get: () => {
+          return videoEl.videoHeight;
+        },
+      });
+      videoEl.addEventListener('loadedmetadata', () => {
+        videoEl.controls = false;
+        videoEl.loop = true;
+        videoEl.muted = true;
+        cb(videoEl);
+      });
+      videoEl.addEventListener('error', () => {
+        cb();
+      });
+      videoEl.src = url;
+    });
+  };
+
+  const createImageNode = (url) => {
+    return new Promise((cb) => {
+      const imgEl = document.createElement('img');
+      imgEl.onload = () => {
+        cb(imgEl);
+      };
+      imgEl.addEventListener('error', () => {
+        cb();
+      });
+      imgEl.src = url;
+    });
+  };
+
+  nodeType.prototype.onDrawBackground = function (ctx) {
+    if (this.flags.collapsed) return;
+
+    let imageURLs = (this.images ?? []).map((i) =>
+      typeof i === 'string' ? i : formatUploadedUrl(i),
+    );
+    let imagesChanged = false;
+
+    if (JSON.stringify(this.displayingImages) !== JSON.stringify(imageURLs)) {
+      this.displayingImages = imageURLs;
+      imagesChanged = true;
+    }
+
+    if (!imagesChanged) return;
+    if (!imageURLs.length) {
+      this.imgs = null;
+      this.animatedImages = false;
+      return;
+    }
+
+    const promises = imageURLs.map((url) => {
+      if (url.startsWith('/view')) {
+        url = window.location.origin + url;
+      }
+
+      const u = new URL(url);
+      const filename =
+        u.searchParams.get('filename') || u.searchParams.get('name') || u.pathname.split('/').pop();
+      const ext = filename.split('.').pop();
+      const format = ['gif', 'webp', 'avif'].includes(ext) ? 'image' : 'video';
+      if (format === 'video') {
+        return createVideoNode(url);
+      } else {
+        return createImageNode(url);
+      }
+    });
+
+    Promise.all(promises)
+      .then((imgs) => {
+        this.imgs = imgs.filter(Boolean);
+      })
+      .then(() => {
+        if (!this.imgs.length) return;
+
+        this.animatedImages = true;
+        const widgetIdx = this.widgets?.findIndex((w) => w.name === ANIM_PREVIEW_WIDGET);
+
+        // Instead of using the canvas we'll use a IMG
+        if (widgetIdx > -1) {
+          // Replace content
+          const widget = this.widgets[widgetIdx];
+          widget.options.host.updateImages(this.imgs);
+        } else {
+          const host = createImageHost(this);
+          this.setSizeForImage(true);
+          const widget = this.addDOMWidget(ANIM_PREVIEW_WIDGET, 'img', host.el, {
+            host,
+            getHeight: host.getHeight,
+            onDraw: host.onDraw,
+            hideOnZoom: false,
+          });
+          widget.serializeValue = () => ({
+            height: host.el.clientHeight,
+          });
+          // widget.computeSize = (w) => ([w, 220]);
+
+          widget.options.host.updateImages(this.imgs);
+        }
+
+        this.imgs.forEach((img) => {
+          if (img instanceof HTMLVideoElement) {
+            img.muted = true;
+            img.autoplay = true;
+            img.play();
+          }
+        });
+      });
+  };
+
+  const { textWidget, comboWidget } = options;
+
+  if (textWidget) {
+    chainCallback(nodeType.prototype, 'onNodeCreated', function () {
+      const pathWidget = this.widgets.find((w) => w.name === textWidget);
+      pathWidget._value = pathWidget.value;
+      Object.defineProperty(pathWidget, 'value', {
+        set: (value) => {
+          pathWidget._value = value;
+          pathWidget.inputEl.value = value;
+          this.images = (value ?? '').split('\n').filter((url) => URL_REGEX.test(url));
+        },
+        get: () => {
+          return pathWidget._value;
+        },
+      });
+      pathWidget.inputEl.addEventListener('change', (e) => {
+        const value = e.target.value;
+        pathWidget._value = value;
+        this.images = (value ?? '').split('\n').filter((url) => URL_REGEX.test(url));
+      });
+
+      // Set value to ensure preview displays on initial add.
+      pathWidget.value = pathWidget._value;
+    });
+  }
+
+  if (comboWidget) {
+    chainCallback(nodeType.prototype, 'onNodeCreated', function () {
+      const pathWidget = this.widgets.find((w) => w.name === comboWidget);
+      pathWidget._value = pathWidget.value;
+      Object.defineProperty(pathWidget, 'value', {
+        set: (value) => {
+          pathWidget._value = value;
+          if (!value) {
+            return this.images = []
+          }
+
+          const parts = value.split("/")
+          const filename = parts.pop()
+          const subfolder = parts.join("/")
+          const extension = filename.split(".").pop();
+          const format = (["gif", "webp", "avif"].includes(extension)) ? 'image' : 'video'
+          this.images = [formatUploadedUrl({ filename, subfolder, type: "input", format: format })]
+        },
+        get: () => {
+          return pathWidget._value;
+        },
+      });
+
+      // Set value to ensure preview displays on initial add.
+      pathWidget.value = pathWidget._value;
+    });
+  }
+
+  chainCallback(nodeType.prototype, "onExecuted", function (message) {
+    if (message?.videos) {
+      this.images = message?.videos.map(formatUploadedUrl);
+    }
   });
 }
 
-export const hasWidgets = (node) => {
+app.registerExtension({
-  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) {
+  init() {
-    const onExecuted = nodeType.prototype.onExecuted;
+    $el('style', {
-    nodeType.prototype.onExecuted = function (message) {
+      textContent: style,
-      const r = onExecuted ? onExecuted.apply(this, message) : undefined;
+      parent: document.head,
-
+    });
-      if (message?.videos) {
-        this.videos = message.videos;
-      }
-
-      return r;
-    };
-
-    const onDrawBackground = nodeType.prototype.onDrawBackground;
-    nodeType.prototype.onDrawBackground = function (ctx) {
-      const r = onDrawBackground ? onDrawBackground.apply(this, arguments) : undefined;
-      const node = this;
-      const prefix = "ad_video_preview_";
-
-      if (node.videos_rendered === node.videos) {
-        return r;
-      }
-
-      if (node.widgets) {
-        const pos = node.widgets.findIndex((w) => w.name === `${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],
-      ]);
-    };
   },
-};
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (nodeData.name !== "AnimateDiffCombine") {
+      return;
+    }
 
-app.registerExtension(videoPreview);
+    addVideoPreview(nodeType);
+  },
+});

web/js/vid_upload.js

@@ -1,188 +1,90 @@
 import { app } from "../../../scripts/app.js";
 import { api } from "../../../scripts/api.js";
-import { ComfyWidgets } from "../../../scripts/widgets.js";
 
-const supportedVideoTypes = [
+import {
-  "image/gif",
+  chainCallback,
-  "video/webm",
+  addVideoPreview,
-  "video/mp4",
+} from "./vid_preview.js";
-  "video/mov",
-];
 
-const VIDEOUPLOAD = (node, inputName, inputData, app) => {
+async function uploadFile(file) {
-  const previewWidget = "ad_video_preview";
+  try {
-  const videoWidget = node.widgets.find((w) => w.name === "video");
+    // Wrap file in formdata so it includes filename
-  let uploadWidget;
+    const body = new FormData();
+    const new_file = new File([file], file.name, {
+      type: file.type,
+      lastModified: file.lastModified,
+    });
+    body.append("image", new_file);
+    body.append("subfolder", "video");
+    const resp = await api.fetchApi("/upload/image", {
+      method: "POST",
+      body,
+    });
 
-  const showVideo = (name) => {
+    if (resp.status === 200 || resp.status === 201) {
-    let folder_separator = name.lastIndexOf("/");
+      return resp.json();
-    let subfolder = "";
+    } else {
-    if (folder_separator > -1) {
+      alert(`Upload failed: ${resp.statusText}`);
-      subfolder = name.substring(0, folder_separator);
-      name = name.substring(folder_separator + 1);
-    }
-    const ext = name.substring(name.lastIndexOf(".") + 1);
-    const format = supportedVideoTypes.find((t) => t.endsWith(ext));
-    node.videos = [
-      {
-        filename: name,
-        type: "input",
-        subfolder: subfolder,
-        format,
-      },
-    ];
-  };
-
-  var default_value = videoWidget.value;
-  Object.defineProperty(videoWidget, "value", {
-    set: function (value) {
-      this._real_value = value;
-    },
-
-    get: function () {
-      let value = "";
-      if (this._real_value) {
-        value = this._real_value;
-      } else {
-        return default_value;
-      }
-
-      if (value.filename) {
-        let real_value = value;
-        value = "";
-        if (real_value.subfolder) {
-          value = real_value.subfolder + "/";
-        }
-
-        value += real_value.filename;
-
-        if (real_value.type && real_value.type !== "input")
-          value += ` [${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);
     }
+  } catch (error) {
+    alert(`Upload failed: ${error}`);
   }
+}
 
-  const fileInput = document.createElement("input");
+function addUploadWidget(nodeType, widgetName) {
-  Object.assign(fileInput, {
+  chainCallback(nodeType.prototype, "onNodeCreated", function () {
-    type: "file",
+    const pathWidget = this.widgets.find((w) => w.name === widgetName);
-    accept: supportedVideoTypes.join(","),
+    if (pathWidget.element) {
-    style: "display: none",
+      pathWidget.options.getMinHeight = () => 50;
-    onchange: async () => {
+      pathWidget.options.getMaxHeight = () => 150;
-      if (fileInput.files.length) {
+    }
-        await uploadFile(fileInput.files[0], true);
+
+    const fileInput = document.createElement("input");
+    chainCallback(this, "onRemoved", () => {
+      fileInput?.remove();
+    });
+
+    Object.assign(fileInput, {
+      type: "file",
+      accept: "video/webm,video/mp4,video/mkv,image/gif,image/webp",
+      style: "display: none",
+      onchange: async () => {
+        if (fileInput.files.length) {
+          const params = await uploadFile(fileInput.files[0]);
+          if (!params) {
+            // upload failed and file can not be added to options
+            return;
+          }
+
+          fileInput.value = "";
+          const filename = [params.subfolder, params.name || params.filename].filter(Boolean).join('/')
+          pathWidget.value = filename;
+          pathWidget.options.values.push(filename);
+        }
+      },
+    });
+
+    document.body.append(fileInput);
+    let uploadWidget = this.addWidget(
+      "button",
+      "choose video to upload",
+      "image",
+      () => {
+        app.canvas.node_widget = null;
+        fileInput.click();
       }
-    },
+    );
+    uploadWidget.options.serialize = false;
   });
-  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"];
+      addUploadWidget(nodeType, 'video');
+      addVideoPreview(nodeType, { comboWidget: 'video' });
     }
   },
 });

workflows/lora.json

@@ -0,0 +1,515 @@
+{
+  "last_node_id": 21,
+  "last_link_id": 38,
+  "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": [
+        "photo of coastline, rocks, storm weather, wind, waves, lightning, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
+      "pos": [
+        1253,
+        191
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 8,
+      "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": [
+        315,
+        507
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 19
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AnimateDiffCombine"
+      },
+      "widgets_values": [
+        8,
+        0,
+        false,
+        "AnimateDiff",
+        "image/gif",
+        false,
+        "/view?filename=AnimateDiff_00003_.gif&subfolder=&type=temp&format=image%2Fgif"
+      ]
+    },
+    {
+      "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": [
+        "blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation"
+      ]
+    },
+    {
+      "id": 20,
+      "type": "EmptyLatentImage",
+      "pos": [
+        522,
+        621
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 0,
+      "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
+      ]
+    },
+    {
+      "id": 13,
+      "type": "VAELoader",
+      "pos": [
+        28,
+        223
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            20
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "vae-ft-mse-840000-ema-pruned.safetensors"
+      ]
+    },
+    {
+      "id": 15,
+      "type": "AnimateDiffSampler",
+      "pos": [
+        882,
+        192
+      ],
+      "size": {
+        "0": 315,
+        "1": 350
+      },
+      "flags": {},
+      "order": 7,
+      "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
+        },
+        {
+          "name": "sliding_window_opts",
+          "type": "SLIDING_WINDOW_OPTS",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            28
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AnimateDiffSampler"
+      },
+      "widgets_values": [
+        "default",
+        14,
+        45987230,
+        "fixed",
+        25,
+        7.5,
+        "ddim",
+        "ddim_uniform",
+        1
+      ]
+    },
+    {
+      "id": 16,
+      "type": "AnimateDiffModuleLoader",
+      "pos": [
+        27,
+        345
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "lora_stack",
+          "type": "MOTION_LORA_STACK",
+          "link": 38,
+          "slot_index": 0
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MOTION_MODULE",
+          "type": "MOTION_MODULE",
+          "links": [
+            24
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AnimateDiffModuleLoader"
+      },
+      "widgets_values": [
+        "mm_sd_v15_v2.ckpt"
+      ]
+    },
+    {
+      "id": 21,
+      "type": "AnimateDiffLoraLoader",
+      "pos": [
+        -317,
+        350
+      ],
+      "size": [
+        310,
+        80
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "lora_stack",
+          "type": "MOTION_LORA_STACK",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MOTION_LORA_STACK",
+          "type": "MOTION_LORA_STACK",
+          "links": [
+            38
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AnimateDiffLoraLoader"
+      },
+      "widgets_values": [
+        "v2_lora_ZoomIn.ckpt",
+        1
+      ]
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        28,
+        457
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 2,
+      "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
+        }
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