Add WebUI format support to Advanced Training Script (#6403)

* Add WebUI format support to Advanced Training Script

* style

---------
Co-authored-by: multimodalart <joaopaulo.passos+multimodal@gmail.com>

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -20,6 +20,7 @@ import itertools
 import logging
 import math
 import os
+import re
 import shutil
 import warnings
 from pathlib import Path
@@ -41,7 +42,7 @@ from peft import LoraConfig
 from peft.utils import get_peft_model_state_dict
 from PIL import Image
 from PIL.ImageOps import exif_transpose
-from safetensors.torch import save_file
+from safetensors.torch import load_file, save_file
 from torch.utils.data import Dataset
 from torchvision import transforms
 from tqdm.auto import tqdm
@@ -58,7 +59,13 @@ from diffusers import (
 from diffusers.loaders import LoraLoaderMixin
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import compute_snr
-from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available
+from diffusers.utils import (
+    check_min_version,
+    convert_all_state_dict_to_peft,
+    convert_state_dict_to_diffusers,
+    convert_state_dict_to_kohya,
+    is_wandb_available,
+)
 from diffusers.utils.import_utils import is_xformers_available
 
 
@@ -93,10 +100,17 @@ def save_model_card(
         img_str += f"""
         - text: '{instance_prompt}'
         """
-
+    embeddings_filename = f"{repo_folder}_emb"
+    instance_prompt_webui = re.sub(r"<s\d+>", "", re.sub(r"<s\d+>", embeddings_filename, instance_prompt, count=1))
+    ti_keys = ", ".join(f'"{match}"' for match in re.findall(r"<s\d+>", instance_prompt))
+    if instance_prompt_webui != embeddings_filename:
+        instance_prompt_sentence = f"For example, `{instance_prompt_webui}`"
+    else:
+        instance_prompt_sentence = ""
     trigger_str = f"You should use {instance_prompt} to trigger the image generation."
     diffusers_imports_pivotal = ""
     diffusers_example_pivotal = ""
+    webui_example_pivotal = ""
     if train_text_encoder_ti:
         trigger_str = (
             "To trigger image generation of trained concept(or concepts) replace each concept identifier "
@@ -105,11 +119,16 @@ def save_model_card(
         diffusers_imports_pivotal = """from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file
         """
-        diffusers_example_pivotal = f"""embedding_path = hf_hub_download(repo_id='{repo_id}', filename="embeddings.safetensors", repo_type="model")
+        diffusers_example_pivotal = f"""embedding_path = hf_hub_download(repo_id='{repo_id}', filename='{embeddings_filename}.safetensors' repo_type="model")
 state_dict = load_file(embedding_path)
-pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
-pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
+pipeline.load_textual_inversion(state_dict["clip_l"], token=[{ti_keys}], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
+pipeline.load_textual_inversion(state_dict["clip_g"], token=[{ti_keys}], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
         """
+        webui_example_pivotal = f"""- *Embeddings*: download **[`{embeddings_filename}.safetensors` here 💾](/{repo_id}/blob/main/{embeddings_filename}.safetensors)**.
+    - Place it on it on your `embeddings` folder
+    - Use it by adding `{embeddings_filename}` to your prompt. {instance_prompt_sentence}
+    (you need both the LoRA and the embeddings as they were trained together for this LoRA)
+    """
         if token_abstraction_dict:
             for key, value in token_abstraction_dict.items():
                 tokens = "".join(value)
@@ -141,9 +160,14 @@ license: openrail++
 
 ### These are {repo_id} LoRA adaption weights for {base_model}.
 
-## Trigger words
+## Download model
 
-{trigger_str}
+### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
+
+- **LoRA**: download **[`{repo_folder}.safetensors` here 💾](/{repo_id}/blob/main/{repo_folder}.safetensors)**.
+    - Place it on your `models/Lora` folder.
+    - On AUTOMATIC1111, load the LoRA by adding `<lora:{repo_folder}:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
+{webui_example_pivotal}
 
 ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
 
@@ -159,16 +183,12 @@ image = pipeline('{validation_prompt if validation_prompt else instance_prompt}'
 
 For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
 
-## Download model
-
-### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
-
-- Download the LoRA *.safetensors [here](/{repo_id}/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder.
-- Download the text embeddings *.safetensors [here](/{repo_id}/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder.
+## Trigger words
 
-All [Files & versions](/{repo_id}/tree/main).
+{trigger_str}
 
 ## Details
+All [Files & versions](/{repo_id}/tree/main).
 
 The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
 
@@ -2035,8 +2055,15 @@ def main(args):
 
         if args.train_text_encoder_ti:
             embedding_handler.save_embeddings(
-                f"{args.output_dir}/embeddings.safetensors",
+                f"{args.output_dir}/{args.output_dir}_emb.safetensors",
             )
+
+        # Conver to WebUI format
+        lora_state_dict = load_file(f"{args.output_dir}/pytorch_lora_weights.safetensors")
+        peft_state_dict = convert_all_state_dict_to_peft(lora_state_dict)
+        kohya_state_dict = convert_state_dict_to_kohya(peft_state_dict)
+        save_file(kohya_state_dict, f"{args.output_dir}/{args.output_dir}.safetensors")
+
         save_model_card(
             model_id if not args.push_to_hub else repo_id,
             images=images,