Update app.py

app.py

@@ -159,9 +159,228 @@ def randomize_loras(selected_indices):
     lora_image_2 = lora2['image']
     return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2
 
-# ... (rest of your code remains unchanged)
+@spaces.GPU(duration=70)
+def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress):
+    pipe.to("cuda")
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    with calculateDuration("Generating image"):
+        # Generate image
+        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+            prompt=prompt_mash,
+            num_inference_steps=steps,
+            guidance_scale=cfg_scale,
+            width=width,
+            height=height,
+            generator=generator,
+            joint_attention_kwargs={"scale": 1.0},
+            output_type="pil",
+            good_vae=good_vae,
+        ):
+            yield img
+
+@spaces.GPU(duration=70)
+def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    pipe_i2i.to("cuda")
+    image_input = load_image(image_input_path)
+    final_image = pipe_i2i(
+        prompt=prompt_mash,
+        image=image_input,
+        strength=image_strength,
+        num_inference_steps=steps,
+        guidance_scale=cfg_scale,
+        width=width,
+        height=height,
+        generator=generator,
+        joint_attention_kwargs={"scale": 1.0},
+        output_type="pil",
+    ).images[0]
+    return final_image 
+    
+def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, progress=gr.Progress(track_tqdm=True)):
+    if not selected_indices:
+        raise gr.Error("You must select at least one LoRA before proceeding.")
+
+    selected_loras = [loras[idx] for idx in selected_indices]
+
+    # Build the prompt with trigger words
+    prompt_mash = prompt
+    for lora in selected_loras:
+        trigger_word = lora.get('trigger_word', '')
+        if trigger_word:
+            if lora.get("trigger_position") == "prepend":
+                prompt_mash = f"{trigger_word} {prompt_mash}"
+            else:
+                prompt_mash = f"{prompt_mash} {trigger_word}"
+
+    # Unload previous LoRA weights
+    with calculateDuration("Unloading LoRA"):
+        pipe.unload_lora_weights()
+        pipe_i2i.unload_lora_weights()
+
+    # Load LoRA weights with respective scales
+    with calculateDuration("Loading LoRA weights"):
+        for idx, lora in enumerate(selected_loras):
+            lora_path = lora['repo']
+            scale = lora_scale_1 if idx == 0 else lora_scale_2
+            if image_input is not None:
+                if "weights" in lora:
+                    pipe_i2i.load_lora_weights(lora_path, weight_name=lora["weights"], multiplier=scale)
+                else:
+                    pipe_i2i.load_lora_weights(lora_path, multiplier=scale)
+            else:
+                if "weights" in lora:
+                    pipe.load_lora_weights(lora_path, weight_name=lora["weights"], multiplier=scale)
+                else:
+                    pipe.load_lora_weights(lora_path, multiplier=scale)
+
+    # Set random seed for reproducibility
+    with calculateDuration("Randomizing seed"):
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
+
+    # Generate image
+    if image_input is not None:
+        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed)
+        yield final_image, seed, gr.update(visible=False)
+    else:
+        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
+        # Consume the generator to get the final image
+        final_image = None
+        step_counter = 0
+        for image in image_generator:
+            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, gr.update(value=progress_bar, visible=True)
+        yield final_image, seed, gr.update(value=progress_bar, visible=False)
+
+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")
+        print(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()
+        safetensors_name = None
+        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)
+            raise Exception("Invalid Hugging Face repository with a *.safetensors LoRA")
+        if not safetensors_name:
+            raise Exception("No *.safetensors file found in the repository")
+        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, selected_indices):
+    global loras
+    if custom_lora:
+        try:
+            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
+            print(f"Loaded custom LoRA: {repo}")
+            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 existing_item_index is None:
+                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)
+
+            # Update gallery
+            gallery_items = [(item["image"], item["title"]) for item in loras]
+            # Update selected_indices if there's room
+            if len(selected_indices) < 2:
+                selected_indices.append(existing_item_index)
+                selected_info_1 = ""
+                selected_info_2 = ""
+                lora_scale_1 = 0.95
+                lora_scale_2 = 0.95
+                lora_image_1 = None
+                lora_image_2 = None
+                if len(selected_indices) >= 1:
+                    lora1 = loras[selected_indices[0]]
+                    selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
+                    lora_image_1 = lora1['image']
+                if len(selected_indices) >= 2:
+                    lora2 = loras[selected_indices[1]]
+                    selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
+                    lora_image_2 = lora2['image']
+                return (gr.update(visible=True, value=card), gr.update(visible=True), gr.update(value=gallery_items),
+                        selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2)
+            else:
+                return (gr.update(visible=True, value=card), gr.update(visible=True), gr.update(value=gallery_items),
+                        gr.NoChange(), gr.NoChange(), selected_indices, gr.NoChange(), gr.NoChange(), gr.NoChange(), gr.NoChange())
+        except Exception as e:
+            print(e)
+            return gr.update(visible=True, value=str(e)), gr.update(visible=True), gr.NoChange(), gr.NoChange(), gr.NoChange(), selected_indices, gr.NoChange(), gr.NoChange(), gr.NoChange(), gr.NoChange()
+    else:
+        return gr.update(visible=False), gr.update(visible=False), gr.NoChange(), gr.NoChange(), gr.NoChange(), selected_indices, gr.NoChange(), gr.NoChange(), gr.NoChange(), gr.NoChange()
+
+def remove_custom_lora(custom_lora_info, custom_lora_button, selected_indices):
+    global loras
+    if loras:
+        custom_lora_repo = loras[-1]['repo']
+        # Remove from loras list
+        loras = loras[:-1]
+        # Remove from selected_indices if selected
+        custom_lora_index = len(loras)
+        if custom_lora_index in selected_indices:
+            selected_indices.remove(custom_lora_index)
+    # Update gallery
+    gallery_items = [(item["image"], item["title"]) for item in loras]
+    # Update selected_info and images
+    selected_info_1 = ""
+    selected_info_2 = ""
+    lora_scale_1 = 0.95
+    lora_scale_2 = 0.95
+    lora_image_1 = None
+    lora_image_2 = None
+    if len(selected_indices) >= 1:
+        lora1 = loras[selected_indices[0]]
+        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
+        lora_image_1 = lora1['image']
+    if len(selected_indices) >= 2:
+        lora2 = loras[selected_indices[1]]
+        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
+        lora_image_2 = lora2['image']
+    return gr.update(visible=False), gr.update(visible=False), gr.update(value=gallery_items), selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2
 
-# Update your UI components to include image previews
 run_lora.zerogpu = True
 
 css = '''
@@ -194,13 +413,13 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app:
             generate_button = gr.Button("Generate", variant="primary")
     with gr.Row():
         with gr.Column(scale=1):
-            randomize_button = gr.Button("🎲", variant="secondary", scale=1, min_width=50)
-        with gr.Column(scale=4):
+            randomize_button = gr.Button("🎲", variant="secondary", scale=1)
+        with gr.Column(scale=3):
             lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False)
             selected_info_1 = gr.Markdown("Select a LoRA 1")
             lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=0.95)
             remove_button_1 = gr.Button("Remove LoRA 1")
-        with gr.Column(scale=4):
+        with gr.Column(scale=3):
             lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False)
             selected_info_2 = gr.Markdown("Select a LoRA 2")
             lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=0.95)