README.md

AnimateDiff Model Checkpoints for A1111 SD WebUI

This repository saves all AnimateDiff models in fp16 & safetensors format for A1111 AnimateDiff users, including

• motion module (v1-v3)
• motion LoRA (v2 only, use like any other LoRAs)
• domain adapter (v3 only, use like any other LoRAs)
• sparse ControlNet (v3 only, use like any other ControlNets)

Unless specified below, you are fine to use models from the official model repository. I will only convert state dict keys if absolutely necessary.

Motion LoRA

Put motion LoRAs to stable-diffusion-webui/models/Lora and use motion LoRAs like any other LoRAs you use.

lora_v2 contains motion LoRAs for AnimateDiff-A1111 v2.0.0. Old motion LoRAs won't work for v2.0.0 and later due to maintenance reason. lora will be removed after AnimateDiff-A1111 v2.0.0 is released to master branch.

I converted the original state dict via the following code. You may do so if you want to use a motion LoRA from community. Run the following script to make your own motion LoRA checkpoint compatible with AnimateDiff-A1111 v2.0.0 and later.

import os, re, torch
import safetensors.torch

def convert_mm_name_to_compvis(key):
    sd_module_key, _, network_part = re.split(r'(_lora\.)', key)
    sd_module_key = sd_module_key.replace("processor.", "").replace("to_out", "to_out.0")
    sd_module_key = sd_module_key.replace(".", "_")
    return f'{sd_module_key}.lora_{network_part}'

file_path = # replace with path to your own old motion LoRA checkpoint
save_path = # replace with path to your own new motion LoRA checkpoint
state_dict = safetensors.torch.load_file(file_path) if file_path.endswith(".safetensors") else torch.load(file_path)
modified_dict = {convert_mm_name_to_compvis(k): v for k, v in state_dict.items()}
safetensors.torch.save_file(modified_dict, save_path)

Sparse ControlNet

Put Sparse ControlNets to stable-diffusion-webui/models/ControlNet and use Sparse ControlNets like any other ControlNets you use.

Like Motion LoRA, I also converted state dict keys inside sparse ControlNet. Run the following script to make your own sparse ControlNet checkpoint compatible with AnimateDiff-A1111.

import torch
import safetensors.torch
ad_cn_old = "v3_sd15_sparsectrl_scribble.ckpt" # replace with path to your own old sparse ControlNet checkpoint
ad_cn_new = "mm_sd15_v3_sparsectrl_scribble.safetensors" # replace with path to your own new sparse ControlNet checkpoint
normal_cn_path = "diffusion_pytorch_model.fp16.safetensors" # download https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/diffusion_pytorch_model.fp16.safetensors?download=true and replace with the path to this model
ad_cn = safetensors.torch.load_file(file_path) if file_path.endswith(".safetensors") else torch.load(ad_cn_old)
normal_cn = safetensors.torch.load_file(normal_cn_path)
ad_cn_l, ad_cn_m = {}, {}
for k in ad_cn.keys():
    if k.startswith("controlnet_cond_embedding"):
        new_key = k.replace("controlnet_cond_embedding.", "input_hint_block.0.")
        ad_cn_m[new_key] = ad_cn[k].to(torch.float16)
    elif not k in normal_cn:
        if "motion_modules" in k:
            ad_cn_m[k] = ad_cn[k].to(torch.float16)
        else:
            raise Exception(f"{k} not in normal_cn")
    else:
        ad_cn_l[k] = ad_cn[k].to(torch.float16)
ad_cn_l = convert_from_diffuser_state_dict(ad_cn_l)
ad_cn_l.update(ad_cn_m)
safetensors.torch.save_file(ad_cn_l, ad_cn_new)