Support convert LoRA safetensors into diffusers format (#2403)
* add lora convertor * Update convert_lora_safetensor_to_diffusers.py * Update README.md * Update convert_lora_safetensor_to_diffusers.py
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Haofan Wang
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@ -467,12 +467,12 @@ image.save("ddpm_generated_image.png")
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- [Unconditional Diffusion with continuous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024)
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**Other Image Notebooks**:
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* [image-to-image generation with Stable Diffusion](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ,
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* [tweak images via repeated Stable Diffusion seeds](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) ,
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* [image-to-image generation with Stable Diffusion](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ,
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* [tweak images via repeated Stable Diffusion seeds](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) ,
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**Diffusers for Other Modalities**:
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* [Molecule conformation generation](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb) ,
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* [Model-based reinforcement learning](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb) ,
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* [Molecule conformation generation](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb) ,
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* [Model-based reinforcement learning](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb) ,
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### Web Demos
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If you just want to play around with some web demos, you can try out the following 🚀 Spaces:
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@ -0,0 +1,130 @@
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# coding=utf-8
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# Copyright 2023, Haofan Wang, Qixun Wang, All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Conversion script for the LoRA's safetensors checkpoints. """
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import argparse
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import torch
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from safetensors.torch import load_file
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from diffusers import StableDiffusionPipeline
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def convert(base_model_path, checkpoint_path, LORA_PREFIX_UNET, LORA_PREFIX_TEXT_ENCODER, alpha):
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# load base model
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pipeline = StableDiffusionPipeline.from_pretrained(base_model_path, torch_dtype=torch.float32)
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# load LoRA weight from .safetensors
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state_dict = load_file(checkpoint_path)
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visited = []
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# directly update weight in diffusers model
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for key in state_dict:
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# it is suggested to print out the key, it usually will be something like below
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# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
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# as we have set the alpha beforehand, so just skip
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if ".alpha" in key or key in visited:
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continue
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if "text" in key:
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layer_infos = key.split(".")[0].split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_")
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curr_layer = pipeline.text_encoder
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else:
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layer_infos = key.split(".")[0].split(LORA_PREFIX_UNET + "_")[-1].split("_")
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curr_layer = pipeline.unet
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# find the target layer
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temp_name = layer_infos.pop(0)
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while len(layer_infos) > -1:
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try:
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curr_layer = curr_layer.__getattr__(temp_name)
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if len(layer_infos) > 0:
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temp_name = layer_infos.pop(0)
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elif len(layer_infos) == 0:
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break
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except Exception:
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if len(temp_name) > 0:
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temp_name += "_" + layer_infos.pop(0)
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else:
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temp_name = layer_infos.pop(0)
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pair_keys = []
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if "lora_down" in key:
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pair_keys.append(key.replace("lora_down", "lora_up"))
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pair_keys.append(key)
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else:
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pair_keys.append(key)
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pair_keys.append(key.replace("lora_up", "lora_down"))
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# update weight
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if len(state_dict[pair_keys[0]].shape) == 4:
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weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32)
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weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32)
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curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
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else:
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weight_up = state_dict[pair_keys[0]].to(torch.float32)
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weight_down = state_dict[pair_keys[1]].to(torch.float32)
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curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down)
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# update visited list
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for item in pair_keys:
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visited.append(item)
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return pipeline
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
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)
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parser.add_argument(
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"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
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)
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parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
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parser.add_argument(
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"--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
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)
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parser.add_argument(
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"--lora_prefix_text_encoder",
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default="lora_te",
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type=str,
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help="The prefix of text encoder weight in safetensors",
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)
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parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
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parser.add_argument(
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"--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
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)
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parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
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args = parser.parse_args()
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base_model_path = args.base_model_path
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checkpoint_path = args.checkpoint_path
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dump_path = args.dump_path
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lora_prefix_unet = args.lora_prefix_unet
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lora_prefix_text_encoder = args.lora_prefix_text_encoder
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alpha = args.alpha
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pipe = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
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pipe = pipe.to(args.device)
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pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
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