metadata
pipeline_tag: text-to-video
AnimateDiff is a method that allows you to create videos using pre-existing Stable Diffusion Text to Image models.
Converted https://huggingface.co/guoyww/animatediff/blob/main/mm_sdxl_v10_beta.ckpt to Huggingface Diffusers format using following script based Diffuser's convetion script (available https://github.com/huggingface/diffusers/blob/main/scripts/convert_animatediff_motion_module_to_diffusers.py)
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)
parser.add_argument("--save_fp16", action="store_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)
adapter = MotionAdapter(
use_motion_mid_block=False,
motion_max_seq_length=32,
block_out_channels=(320, 640, 1280),
)
# skip loading position embeddings
adapter.load_state_dict(conv_state_dict, strict=False)
adapter.save_pretrained(args.output_path)
if args.save_fp16:
adapter.to(torch.float16).save_pretrained(args.output_path, variant="fp16")
The following example demonstrates how you can utilize the motion modules with an existing Stable Diffusion text to image model.
#TODO