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# AnimateDiff Model Checkpoints for A1111 SD WebUI |
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This repository saves all AnimateDiff models in fp16 & safetensors format for A1111 AnimateDiff users, including |
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- motion module (v1-v3) |
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- [motion LoRA](#motion-lora) (v2 only, use like any other LoRAs) |
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- domain adapter (v3 only, use like any other LoRAs) |
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- [sparse ControlNet](#sparse-controlnet) (v3 only, use like any other ControlNets) |
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Unless specified below, you are fine to use models from the [official model repository](https://huggingface.co/guoyww/animatediff/tree/main). I will only convert state dict keys if absolutely necessary. |
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## Motion LoRA |
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Put motion LoRAs to `stable-diffusion-webui/models/Lora` and use motion LoRAs like any other LoRAs you use. |
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`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. |
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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. |
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```python |
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import os, re, torch |
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import safetensors.torch |
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def convert_mm_name_to_compvis(key): |
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sd_module_key, _, network_part = re.split(r'(_lora\.)', key) |
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sd_module_key = sd_module_key.replace("processor.", "").replace("to_out", "to_out.0") |
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sd_module_key = sd_module_key.replace(".", "_") |
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return f'{sd_module_key}.lora_{network_part}' |
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file_path = # replace with path to your own old motion LoRA checkpoint |
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save_path = # replace with path to your own new motion LoRA checkpoint |
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state_dict = safetensors.torch.load_file(file_path) if file_path.endswith(".safetensors") else torch.load(file_path) |
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modified_dict = {convert_mm_name_to_compvis(k): v for k, v in state_dict.items()} |
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safetensors.torch.save_file(modified_dict, save_path) |
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``` |
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## Sparse ControlNet |
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Put Sparse ControlNets to `stable-diffusion-webui/models/ControlNet` and use Sparse ControlNets like any other ControlNets you use. |
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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. |
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```python |
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import torch |
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import safetensors.torch |
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ad_cn_old = "v3_sd15_sparsectrl_scribble.ckpt" # replace with path to your own old sparse ControlNet checkpoint |
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ad_cn_new = "mm_sd15_v3_sparsectrl_scribble.safetensors" # replace with path to your own new sparse ControlNet checkpoint |
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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 |
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ad_cn = safetensors.torch.load_file(file_path) if file_path.endswith(".safetensors") else torch.load(ad_cn_old) |
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normal_cn = safetensors.torch.load_file(normal_cn_path) |
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ad_cn_l, ad_cn_m = {}, {} |
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for k in ad_cn.keys(): |
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if k.startswith("controlnet_cond_embedding"): |
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new_key = k.replace("controlnet_cond_embedding.", "input_hint_block.0.") |
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ad_cn_m[new_key] = ad_cn[k].to(torch.float16) |
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elif not k in normal_cn: |
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if "motion_modules" in k: |
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ad_cn_m[k] = ad_cn[k].to(torch.float16) |
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else: |
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raise Exception(f"{k} not in normal_cn") |
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else: |
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ad_cn_l[k] = ad_cn[k].to(torch.float16) |
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ad_cn_l = convert_from_diffuser_state_dict(ad_cn_l) |
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ad_cn_l.update(ad_cn_m) |
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safetensors.torch.save_file(ad_cn_l, ad_cn_new) |
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``` |
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