fix: video rendering on svd.

docs/source/en/using-diffusers/svd.md

@@ -44,7 +44,7 @@ pipe = StableVideoDiffusionPipeline.from_pretrained(
 pipe.enable_model_cpu_offload()
 
 # Load the conditioning image
-image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true")
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png")
 image = image.resize((1024, 576))
 
 generator = torch.manual_seed(42)
@@ -58,6 +58,11 @@ export_to_video(frames, "generated.mp4", fps=7)
   <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket_generated.mp4" type="video/mp4" />
 </video>
 
+| **Source Image** | **Video** |
+|:------------:|:-----:|
+|     ![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png)      |  ![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/output_rocket.gif)  |
+
+
 <Tip>
 Since generating videos is more memory intensive we can use the `decode_chunk_size` argument to control how many frames are decoded at once. This will reduce the memory usage. It's recommended to tweak this value based on your GPU memory.
 Setting `decode_chunk_size=1` will decode one frame at a time and will use the least amount of memory but the video might have some flickering.
@@ -120,7 +125,7 @@ pipe = StableVideoDiffusionPipeline.from_pretrained(
 pipe.enable_model_cpu_offload()
 
 # Load the conditioning image
-image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true")
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png")
 image = image.resize((1024, 576))
 
 generator = torch.manual_seed(42)
@@ -128,7 +133,5 @@ frames = pipe(image, decode_chunk_size=8, generator=generator, motion_bucket_id=
 export_to_video(frames, "generated.mp4", fps=7)
 ```
 
-<video width="1024" height="576" controls>
-  <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket_generated_motion.mp4" type="video/mp4">
-</video>
+![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/output_rocket_with_conditions.gif)
 

scripts/convert_animatediff_motion_lora_to_diffusers.py

@@ -1,51 +0,0 @@
-import argparse
-
-import torch
-from safetensors.torch import save_file
-
-
-def convert_motion_module(original_state_dict):
-    converted_state_dict = {}
-    for k, v in original_state_dict.items():
-        if "pos_encoder" in k:
-            continue
-
-        else:
-            converted_state_dict[
-                k.replace(".norms.0", ".norm1")
-                .replace(".norms.1", ".norm2")
-                .replace(".ff_norm", ".norm3")
-                .replace(".attention_blocks.0", ".attn1")
-                .replace(".attention_blocks.1", ".attn2")
-                .replace(".temporal_transformer", "")
-            ] = v
-
-    return converted_state_dict
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--ckpt_path", type=str, required=True)
-    parser.add_argument("--output_path", type=str, required=True)
-
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    args = get_args()
-
-    state_dict = torch.load(args.ckpt_path, map_location="cpu")
-
-    if "state_dict" in state_dict.keys():
-        state_dict = state_dict["state_dict"]
-
-    conv_state_dict = convert_motion_module(state_dict)
-
-    # convert to new format
-    output_dict = {}
-    for module_name, params in conv_state_dict.items():
-        if type(params) is not torch.Tensor:
-            continue
-        output_dict.update({f"unet.{module_name}": params})
-
-    save_file(output_dict, f"{args.output_path}/diffusion_pytorch_model.safetensors")

scripts/convert_animatediff_motion_module_to_diffusers.py

@@ -1,51 +0,0 @@
-import argparse
-
-import torch
-
-from diffusers import MotionAdapter
-
-
-def convert_motion_module(original_state_dict):
-    converted_state_dict = {}
-    for k, v in original_state_dict.items():
-        if "pos_encoder" in k:
-            continue
-
-        else:
-            converted_state_dict[
-                k.replace(".norms.0", ".norm1")
-                .replace(".norms.1", ".norm2")
-                .replace(".ff_norm", ".norm3")
-                .replace(".attention_blocks.0", ".attn1")
-                .replace(".attention_blocks.1", ".attn2")
-                .replace(".temporal_transformer", "")
-            ] = v
-
-    return converted_state_dict
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--ckpt_path", type=str, required=True)
-    parser.add_argument("--output_path", type=str, required=True)
-    parser.add_argument("--use_motion_mid_block", action="store_true")
-    parser.add_argument("--motion_max_seq_length", type=int, default=32)
-
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    args = get_args()
-
-    state_dict = torch.load(args.ckpt_path, map_location="cpu")
-    if "state_dict" in state_dict.keys():
-        state_dict = state_dict["state_dict"]
-
-    conv_state_dict = convert_motion_module(state_dict)
-    adapter = MotionAdapter(
-        use_motion_mid_block=args.use_motion_mid_block, motion_max_seq_length=args.motion_max_seq_length
-    )
-    # skip loading position embeddings
-    adapter.load_state_dict(conv_state_dict, strict=False)
-    adapter.save_pretrained(args.output_path)
-    adapter.save_pretrained(args.output_path, variant="fp16", torch_dtype=torch.float16)