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

README.md

@@ -467,12 +467,12 @@ image.save("ddpm_generated_image.png")
 - [Unconditional Diffusion with continuous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024)
 
 **Other Image Notebooks**:
-* [image-to-image generation with Stable Diffusion](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
-* [tweak images via repeated Stable Diffusion seeds](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+* [image-to-image generation with Stable Diffusion](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb),
+* [tweak images via repeated Stable Diffusion seeds](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) ![Open In Colab](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb),
 
 **Diffusers for Other Modalities**:
-* [Molecule conformation generation](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
-* [Model-based reinforcement learning](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+* [Molecule conformation generation](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb) ![Open In Colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb),
+* [Model-based reinforcement learning](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb),
 
 ### Web Demos
 If you just want to play around with some web demos, you can try out the following 🚀 Spaces:

scripts/convert_lora_safetensor_to_diffusers.py

+# coding=utf-8
+# Copyright 2023, Haofan Wang, Qixun Wang, All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" Conversion script for the LoRA's safetensors checkpoints. """
+
+import argparse
+
+import torch
+from safetensors.torch import load_file
+
+from diffusers import StableDiffusionPipeline
+
+
+def convert(base_model_path, checkpoint_path, LORA_PREFIX_UNET, LORA_PREFIX_TEXT_ENCODER, alpha):
+
+    # load base model
+    pipeline = StableDiffusionPipeline.from_pretrained(base_model_path, torch_dtype=torch.float32)
+
+    # load LoRA weight from .safetensors
+    state_dict = load_file(checkpoint_path)
+
+    visited = []
+
+    # directly update weight in diffusers model
+    for key in state_dict:
+
+        # it is suggested to print out the key, it usually will be something like below
+        # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
+
+        # as we have set the alpha beforehand, so just skip
+        if ".alpha" in key or key in visited:
+            continue
+
+        if "text" in key:
+            layer_infos = key.split(".")[0].split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_")
+            curr_layer = pipeline.text_encoder
+        else:
+            layer_infos = key.split(".")[0].split(LORA_PREFIX_UNET + "_")[-1].split("_")
+            curr_layer = pipeline.unet
+
+        # find the target layer
+        temp_name = layer_infos.pop(0)
+        while len(layer_infos) > -1:
+            try:
+                curr_layer = curr_layer.__getattr__(temp_name)
+                if len(layer_infos) > 0:
+                    temp_name = layer_infos.pop(0)
+                elif len(layer_infos) == 0:
+                    break
+            except Exception:
+                if len(temp_name) > 0:
+                    temp_name += "_" + layer_infos.pop(0)
+                else:
+                    temp_name = layer_infos.pop(0)
+
+        pair_keys = []
+        if "lora_down" in key:
+            pair_keys.append(key.replace("lora_down", "lora_up"))
+            pair_keys.append(key)
+        else:
+            pair_keys.append(key)
+            pair_keys.append(key.replace("lora_up", "lora_down"))
+
+        # update weight
+        if len(state_dict[pair_keys[0]].shape) == 4:
+            weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32)
+            weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32)
+            curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
+        else:
+            weight_up = state_dict[pair_keys[0]].to(torch.float32)
+            weight_down = state_dict[pair_keys[1]].to(torch.float32)
+            curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down)
+
+        # update visited list
+        for item in pair_keys:
+            visited.append(item)
+
+    return pipeline
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
+    )
+    parser.add_argument(
+        "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
+    )
+    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
+    parser.add_argument(
+        "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
+    )
+    parser.add_argument(
+        "--lora_prefix_text_encoder",
+        default="lora_te",
+        type=str,
+        help="The prefix of text encoder weight in safetensors",
+    )
+    parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
+    parser.add_argument(
+        "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
+    )
+    parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
+
+    args = parser.parse_args()
+
+    base_model_path = args.base_model_path
+    checkpoint_path = args.checkpoint_path
+    dump_path = args.dump_path
+    lora_prefix_unet = args.lora_prefix_unet
+    lora_prefix_text_encoder = args.lora_prefix_text_encoder
+    alpha = args.alpha
+
+    pipe = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
+
+    pipe = pipe.to(args.device)
+    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)