Update app.py

app.py

@@ -12,13 +12,29 @@ import os
 # Load the JSON data
 with open("sdxl_lora.json", "r") as file:
     data = json.load(file)
-    sdxl_loras_raw = sorted(data, key=lambda x: x["likes"], reverse=True)
+    sdxl_loras_raw = [
+        {
+            "image": item["image"],
+            "title": item["title"],
+            "repo": item["repo"],
+            "trigger_word": item["trigger_word"],
+            "weights": item["weights"],
+            "is_pivotal": item.get("is_pivotal", False),
+            "text_embedding_weights": item.get("text_embedding_weights", None),
+            "likes": item.get("likes", 0),
+        }
+        for item in data
+    ]
+
+# Sort the loras by likes
+sdxl_loras_raw = sorted(sdxl_loras_raw, key=lambda x: x["likes"], reverse=True)
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 model_id = "stabilityai/stable-diffusion-xl-base-1.0"
 
 pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16")
 pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+pipe.load_lora_weights("jasperai/flash-sdxl", adapter_name="lora")
 pipe.to(device=DEVICE, dtype=torch.float16)
 
 MAX_SEED = np.iinfo(np.int32).max
@@ -29,10 +45,6 @@ def update_selection(selected_state: gr.SelectData, gr_sdxl_loras):
     trigger_word = gr_sdxl_loras[selected_state.index]["trigger_word"]
     return lora_id, trigger_word
 
-def load_lora_for_style(style_repo):
-    pipe.unload_lora_weights()
-    pipe.load_lora_weights(style_repo, adapter_name="lora")
-
 def get_image(image_data):
     if isinstance(image_data, str):
         return image_data
@@ -44,22 +56,20 @@ def get_image(image_data):
         print(f"Unexpected image_data format: {type(image_data)}")
         return None
 
-    # Try loading from local path first
     if local_path and os.path.exists(local_path):
         try:
-            Image.open(local_path).verify()  # Verify that it's a valid image
+            Image.open(local_path).verify()
             return local_path
         except Exception as e:
             print(f"Error loading local image {local_path}: {e}")
 
-    # If local path fails or doesn't exist, try URL
     if hf_url:
         try:
             response = requests.get(hf_url)
             if response.status_code == 200:
                 img = Image.open(requests.get(hf_url, stream=True).raw)
-                img.verify()  # Verify that it's a valid image
-                img.save(local_path)  # Save for future use
+                img.verify()
+                img.save(local_path)
                 return local_path
             else:
                 print(f"Failed to fetch image from URL {hf_url}. Status code: {response.status_code}")
@@ -82,7 +92,19 @@ def infer(
     user_lora_weight,
     progress=gr.Progress(track_tqdm=True),
 ):
-    load_lora_for_style(user_lora_selector)
+    flash_sdxl_id = "jasperai/flash-sdxl"
+
+    new_adapter_id = user_lora_selector.replace("/", "_")
+    loaded_adapters = pipe.get_list_adapters()
+
+    if new_adapter_id not in loaded_adapters["unet"]:
+        gr.Info("Swapping LoRA")
+        pipe.unload_lora_weights()
+        pipe.load_lora_weights(flash_sdxl_id, adapter_name="lora")
+        pipe.load_lora_weights(user_lora_selector, adapter_name=new_adapter_id)
+
+    pipe.set_adapters(["lora", new_adapter_id], adapter_weights=[1.0, user_lora_weight])
+    gr.Info("LoRA setup done")
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -140,18 +162,16 @@ h1, h2 {
 with gr.Blocks(css=css) as demo:
     gr.Markdown(
         """
-        # ⚡ FlashDiffusion: Araminta K's FlashLoRA Showcase ⚡
-        
-        This interactive demo showcases [Araminta K's models](https://huggingface.co/alvdansen) using [Flash Diffusion](https://gojasper.github.io/flash-diffusion-project/) technology.
+        # ⚡ FlashDiffusion: FlashLoRA ⚡
+        This is an interactive demo of [Flash Diffusion](https://gojasper.github.io/flash-diffusion-project/) **on top of** existing LoRAs.
         
-        ## Acknowledgments
-        - Original Flash Diffusion technology by the Jasper AI team
-        - Based on the paper: [Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation](http://arxiv.org/abs/2406.02347) by Clément Chadebec, Onur Tasar, Eyal Benaroche and Benjamin Aubin
-        - Models showcased here are created by Araminta K at Alvdansen Labs
-        
-        Explore the power of FlashLoRA with Araminta K's unique artistic styles!
+        The distillation method proposed in [Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation](http://arxiv.org/abs/2406.02347) *by Clément Chadebec, Onur Tasar, Eyal Benaroche and Benjamin Aubin* from Jasper Research.
+        The LoRAs can be added **without** any retraining for similar results in most cases. Feel free to tweak the parameters and use your own LoRAs by giving a look at the [Github Repo](https://github.com/gojasper/flash-diffusion)
         """
     )
+    gr.Markdown(
+        "If you enjoy the space, please also promote *open-source* by giving a ⭐ to our repo [![GitHub Stars](https://img.shields.io/github/stars/gojasper/flash-diffusion?style=social)](https://github.com/gojasper/flash-diffusion)"
+    )
 
     gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
     gr_lora_id = gr.State(value="")
@@ -169,9 +189,18 @@ with gr.Blocks(css=css) as demo:
             
             user_lora_selector = gr.Textbox(
                 label="Current Selected LoRA",
+                max_lines=1,
                 interactive=False,
             )
 
+            user_lora_weight = gr.Slider(
+                label="Selected LoRA Weight",
+                minimum=0.5,
+                maximum=3,
+                step=0.1,
+                value=1,
+            )
+
         with gr.Column(scale=3):
             prompt = gr.Textbox(
                 label="Prompt",
@@ -218,6 +247,12 @@ with gr.Blocks(css=css) as demo:
                     value=1,
                 )
 
+                hint_negative = gr.Markdown(
+                    """💡 _Hint : Negative Prompt will only work with Guidance > 1 but the model was 
+                    trained to be used with guidance = 1 (ie. without guidance).
+                    Can degrade the results, use cautiously._"""
+                )
+
                 negative_prompt = gr.Textbox(
                     label="Negative Prompt",
                     placeholder="Enter a negative Prompt",
@@ -225,7 +260,15 @@ with gr.Blocks(css=css) as demo:
                 )
 
     gr.on(
-        [run_button.click, prompt.submit],
+        [
+            run_button.click,
+            seed.change,
+            randomize_seed.change,
+            prompt.submit,
+            negative_prompt.change,
+            negative_prompt.submit,
+            guidance_scale.change,
+        ],
         fn=infer,
         inputs=[
             pre_prompt,
@@ -236,7 +279,7 @@ with gr.Blocks(css=css) as demo:
             negative_prompt,
             guidance_scale,
             user_lora_selector,
-            gr.Slider(label="Selected LoRA Weight", minimum=0.5, maximum=3, step=0.1, value=1),
+            user_lora_weight,
         ],
         outputs=[result],
     )
@@ -249,17 +292,9 @@ with gr.Blocks(css=css) as demo:
         outputs=[user_lora_selector, pre_prompt],
     )
 
+    gr.Markdown("**Disclaimer:**")
     gr.Markdown(
-        """
-        ## Unleash Your Creativity!
-        
-        This showcase brings together the speed of Flash Diffusion and the artistic flair of Araminta K's models. 
-        Craft your prompts, adjust the settings, and watch as AI brings your ideas to life in stunning detail.
-        
-        Remember to use this tool ethically and respect copyright and individual privacy.
-        
-        Enjoy exploring these unique artistic styles!
-        """
+        "This demo is only for research purpose. Users are solely responsible for any content they create, and it is their obligation to ensure that it adheres to appropriate and ethical standards."
     )
 
 demo.queue().launch()