Update app.py

app.py

@@ -82,8 +82,12 @@ def update_selection(evt: gr.SelectData, width, height):
 
 @spaces.GPU(duration=30)
 def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
-    pipe.to("cuda")
-    generator = torch.Generator(device="cuda").manual_seed(seed)
+    if torch.cuda.is_available():
+        pipe.to("cuda")
+    else:
+        pipe.to("cpu")
+
+    generator = torch.Generator(device=device).manual_seed(seed)
     with calculateDuration("Generating image"):
         # Generate image
         for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
@@ -128,6 +132,71 @@ def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps
 
     return final_image, temp_file.name, f"data:image/png;base64,{img_base64}"
 
+def get_huggingface_safetensors(link):
+    split_link = link.split("/")
+    if len(split_link) == 2:
+        model_card = ModelCard.load(link)
+        base_model = model_card.data.get("base_model")
+        if base_model not in ["black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-schnell"]:
+            raise Exception("Not a FLUX LoRA!")
+        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:
+            raise Exception(f"Invalid Hugging Face repository: {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)
+            new_lora = {
+                "image": image,
+                "title": title,
+                "repo": repo,
+                "weights": path,
+                "trigger_word": trigger_word
+            }
+            loras.append(new_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>
+            '''
+            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", len(loras) - 1, trigger_word
+        except Exception as e:
+            return gr.update(visible=True, value=f"Error: {e}"), gr.update(visible=False), 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, ""
+
 @spaces.GPU(duration=30)
 def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
     if selected_index is None:
@@ -135,7 +204,7 @@ def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_ind
     selected_lora = loras[selected_index]
     lora_path = selected_lora["repo"]
     trigger_word = selected_lora["trigger_word"]
-    if(trigger_word):
+    if trigger_word:
         if "trigger_position" in selected_lora:
             if selected_lora["trigger_position"] == "prepend":
                 prompt_mash = f"{trigger_word} {prompt}"
@@ -166,7 +235,7 @@ def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_ind
         if randomize_seed:
             seed = random.randint(0, MAX_SEED)
             
-    if(image_input is not None):
+    if image_input is not None:
         final_image, file_path, base64_str = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed)
         yield final_image, seed, file_path, base64_str, gr.update(visible=False)
     else:
@@ -176,7 +245,7 @@ def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_ind
         final_image = None
         step_counter = 0
         for image in image_generator:
-            step_counter+=1
+            step_counter += 1
             final_image = image
             progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
             yield image, seed, None, None, gr.update(value=progress_bar, visible=True)
@@ -279,3 +348,4 @@ with gr.Blocks(theme=gr.themes.Soft(font=font), css=css, delete_cache=(60, 60))
 
 app.queue()
 app.launch()
+