Add custom LoRA loading

app.py

@@ -1,3 +1,4 @@
+import os
 import gradio as gr
 import json
 import logging
@@ -5,6 +6,7 @@ import torch
 from PIL import Image
 import spaces
 from diffusers import DiffusionPipeline
+from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
 import copy
 import random
 import time
@@ -35,7 +37,6 @@ class calculateDuration:
  else:
  print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
 
-
 def update_selection(evt: gr.SelectData, width, height):
  selected_lora = loras[evt.index]
  new_placeholder = f"Type a prompt for {selected_lora['title']}"
@@ -86,9 +87,10 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
  with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
  if "weights" in selected_lora:
  pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
+ #pipe.fuse_lora()
  else:
  pipe.load_lora_weights(lora_path)
- 
+ #pipe.fuse_lora()
  # Set random seed for reproducibility
  with calculateDuration("Randomizing seed"):
  if randomize_seed:
@@ -96,9 +98,80 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
  
  image = generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress)
  pipe.to("cpu")
+ #pipe.unfuse_lora()
  pipe.unload_lora_weights()
  return image, seed 
 
+def get_huggingface_safetensors(link):
+ split_link = link.split("/")
+ if(len(split_link) == 2):
+ model_card = ModelCard.load(link)
+ image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
+ trigger_word = model_card.data.get("instance_prompt", "")
+ image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
+ fs = HfFileSystem()
+ try:
+ list_of_files = fs.ls(link, detail=False)
+ for file in list_of_files:
+ if(file.endswith(".safetensors")):
+ safetensors_name = file.split("/")[-1]
+ if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
+ image_elements = file.split("/")
+ image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
+ except Exception as e:
+ print(e)
+ gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA {e}")
+ raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA {e}")
+ return split_link[1], link, safetensors_name, trigger_word, image_url
+
+def check_custom_model(link):
+ if(link.startswith("https://")):
+ if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
+ link_split = link.split("huggingface.co/")
+ return get_huggingface_safetensors(link_split[1])
+ else: 
+ return get_huggingface_safetensors(link)
+
+def add_custom_lora(custom_lora):
+ global loras
+ if(custom_lora):
+ try:
+ title, repo, path, trigger_word, image = check_custom_model(custom_lora)
+ card = f'''
+ <div class="custom_lora_card">
+ <span>Loaded custom LoRA:</span>
+ <div class="card_internal">
+ <img src="{image}" />
+ <div>
+ <h3>{title}</h3>
+ <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
+ </div>
+ </div>
+ </div>
+ '''
+ existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
+ if(not existing_item_index):
+ new_item = {
+ "image": image,
+ "title": title,
+ "repo": repo,
+ "weights": path,
+ "trigger_word": trigger_word
+ }
+ print(new_item)
+ existing_item_index = len(loras)
+ loras.append(new_item)
+ 
+ return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index
+ except Exception as e:
+ gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA")
+ return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=True), gr.update(), "", None
+ else:
+ return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None
+
+def remove_custom_lora():
+ return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
+
 run_lora.zerogpu = True
 
 css = '''
@@ -107,6 +180,10 @@ css = '''
 #title h1{font-size: 3em; display:inline-flex; align-items:center}
 #title img{width: 100px; margin-right: 0.5em}
 #gallery .grid-wrap{height: 10vh}
+#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
+.card_internal{display: flex;height: 100px;margin-top: .5em}
+.card_internal img{margin-right: 1em}
+.styler{--form-gap-width: 0px !important}
 '''
 with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
  title = gr.HTML(
@@ -129,7 +206,11 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
  columns=3,
  elem_id="gallery"
  )
- 
+ with gr.Group():
+ custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux")
+ gr.Markdown("[Check the list of FLUX LoRas](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
+ custom_lora_info = gr.HTML(visible=False)
+ custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
  with gr.Column(scale=4):
  result = gr.Image(label="Generated Image")
 
@@ -154,7 +235,15 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
  inputs=[width, height],
  outputs=[prompt, selected_info, selected_index, width, height]
  )
-
+ custom_lora.input(
+ add_custom_lora,
+ inputs=[custom_lora],
+ outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index]
+ )
+ custom_lora_button.click(
+ remove_custom_lora,
+ outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
+ )
  gr.on(
  triggers=[generate_button.click, prompt.submit],
  fn=run_lora,