Update app.py

app.py

@@ -1,14 +1,44 @@
 import json
 import random
 import requests
-import os
-from PIL import Image
-
 import gradio as gr
 import numpy as np
 import spaces
 import torch
 from diffusers import DiffusionPipeline, LCMScheduler
+from PIL import Image
+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)
+
+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.to(device=DEVICE, dtype=torch.float16)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+def update_selection(selected_state: gr.SelectData, gr_sdxl_loras):
+    lora_id = gr_sdxl_loras[selected_state.index]["repo"]
+    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("jasperai/flash-sdxl", adapter_name="lora")
+    pipe.set_adapters(["lora", new_adapter_id], adapter_weights=[1.0, user_lora_weight])
+
+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)
 
 def get_image(image_data):
     if isinstance(image_data, str):
@@ -21,20 +51,22 @@ 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()
+            Image.open(local_path).verify()  # Verify that it's a valid image
             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()
-                img.save(local_path)
+                img.verify()  # Verify that it's a valid image
+                img.save(local_path)  # Save for future use
                 return local_path
             else:
                 print(f"Failed to fetch image from URL {hf_url}. Status code: {response.status_code}")
@@ -44,51 +76,6 @@ def get_image(image_data):
     print(f"Failed to load image for {image_data}")
     return None
 
-with open("sdxl_lora.json", "r") as file:
-    data = json.load(file)
-    sdxl_loras_raw = [
-        {
-            "image": get_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
-    ]
-
-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")
-
-# Create LCMScheduler with default config
-lcm_scheduler = LCMScheduler.from_config(pipe.scheduler.config)
-
-# Remove the 'skip_prk_steps' if it exists in the config
-if hasattr(lcm_scheduler.config, 'skip_prk_steps'):
-    delattr(lcm_scheduler.config, 'skip_prk_steps')
-
-pipe.scheduler = lcm_scheduler
-pipe.to(device=DEVICE, dtype=torch.float16)
-
-# Load Flash SDXL LoRA
-flash_sdxl_id = "jasperai/flash-sdxl"
-pipe.load_lora_weights(flash_sdxl_id, adapter_name="flash_lora")
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def update_selection(selected_state: gr.SelectData, gr_sdxl_loras):
-    lora_id = gr_sdxl_loras[selected_state.index]["repo"]
-    trigger_word = gr_sdxl_loras[selected_state.index]["trigger_word"]
-    return lora_id, trigger_word
-
 @spaces.GPU
 def infer(
     pre_prompt,
@@ -102,168 +89,150 @@ def infer(
     user_lora_weight,
     progress=gr.Progress(track_tqdm=True),
 ):
-    try:
-        # Load the user-selected LoRA
-        new_adapter_id = user_lora_selector.replace("/", "_")
-        pipe.load_lora_weights(user_lora_selector, adapter_name=new_adapter_id)
-
-        # Set adapter weights
-        pipe.set_adapters(["flash_lora", new_adapter_id], adapter_weights=[1.0, user_lora_weight])
-        gr.Info("LoRA setup complete")
-
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
-
-        generator = torch.Generator().manual_seed(seed)
-
-        if pre_prompt != "":
-            prompt = f"{pre_prompt} {prompt}"
-
-        # Use Flash Diffusion settings
-        image = pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            guidance_scale=1.0,  # Flash Diffusion typically uses guidance_scale=1
-            num_inference_steps=4,  # Flash Diffusion uses fewer steps
-            generator=generator,
-        ).images[0]
-
-        return image
-    except Exception as e:
-        gr.Error(f"An error occurred: {str(e)}")
-        return None
+    load_lora_for_style(user_lora_selector)
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator().manual_seed(seed)
+
+    if pre_prompt != "":
+        prompt = f"{pre_prompt} {prompt}"
+
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+    ).images[0]
+
+    return image
 
 css = """
-h1 {
+body {
+    background-color: #1a1a1a;
+    color: #ffffff;
+}
+.container {
+    max-width: 900px;
+    margin: auto;
+    padding: 20px;
+}
+h1, h2 {
+    color: #4CAF50;
     text-align: center;
-    display:block;
 }
-p {
-    text-align: justify;
-    display:block;
+.gallery {
+    display: flex;
+    flex-wrap: wrap;
+    justify-content: center;
+}
+.gallery img {
+    margin: 10px;
+    border-radius: 10px;
+    transition: transform 0.3s ease;
+}
+.gallery img:hover {
+    transform: scale(1.05);
+}
+.gradio-slider input[type="range"] {
+    background-color: #4CAF50;
+}
+.gradio-button {
+    background-color: #4CAF50 !important;
 }
 """
 
 with gr.Blocks(css=css) as demo:
     gr.Markdown(
-        f"""
-    # ⚡ FlashDiffusion: FlashLoRA ⚡
-    This is an interactive demo of [Flash Diffusion](https://gojasper.github.io/flash-diffusion-project/) **on top of** existing LoRAs.
-    
-    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)"
+        """
+        # ⚡ 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.
+        
+        ## 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!
+        """
     )
 
     gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
     gr_lora_id = gr.State(value="")
 
     with gr.Row():
-        with gr.Blocks():
-            with gr.Column():
-                user_lora_selector = gr.Textbox(
-                    label="Current Selected LoRA",
-                    max_lines=1,
-                    interactive=False,
-                )
+        with gr.Column(scale=2):
+            gallery = gr.Gallery(
+                value=[(get_image(item["image"]), item["title"]) for item in sdxl_loras_raw if get_image(item["image"]) is not None],
+                label="SDXL LoRA Gallery",
+                show_label=False,
+                elem_id="gallery",
+                columns=3,
+                height=600,
+            )
+            
+            user_lora_selector = gr.Textbox(
+                label="Current Selected LoRA",
+                interactive=False,
+            )
+
+        with gr.Column(scale=3):
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="Enter your prompt",
+                lines=3,
+            )
 
-                user_lora_weight = gr.Slider(
-                    label="Selected LoRA Weight",
-                    minimum=0.5,
-                    maximum=3,
-                    step=0.1,
-                    value=1,
-                )
-
-                gallery = gr.Gallery(
-                    value=[(item["image"], item["title"]) for item in sdxl_loras_raw],
-                    label="SDXL LoRA Gallery",
-                    allow_preview=False,
-                    columns=3,
-                    elem_id="gallery",
-                    show_share_button=False,
-                )
-
-        with gr.Column():
             with gr.Row():
-                prompt = gr.Text(
-                    label="Prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                    container=False,
-                    scale=5,
-                )
+                run_button = gr.Button("Run", variant="primary")
+                clear_button = gr.Button("Clear")
 
-                run_button = gr.Button("Run", scale=1)
-
-            result = gr.Image(label="Result", show_label=False)
+            result = gr.Image(label="Result", height=512)
 
             with gr.Accordion("Advanced Settings", open=False):
-                pre_prompt = gr.Text(
+                pre_prompt = gr.Textbox(
                     label="Pre-Prompt",
-                    show_label=True,
-                    max_lines=1,
                     placeholder="Pre Prompt from the LoRA config",
-                    container=True,
-                    scale=5,
+                    lines=2,
                 )
 
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=MAX_SEED,
-                    step=1,
-                    value=0,
-                )
-
-                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
                 with gr.Row():
-                    num_inference_steps = gr.Slider(
-                        label="Number of inference steps",
-                        minimum=4,
-                        maximum=8,
+                    seed = gr.Slider(
+                        label="Seed",
+                        minimum=0,
+                        maximum=MAX_SEED,
                         step=1,
-                        value=4,
+                        value=0,
                     )
+                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
-                with gr.Row():
-                    guidance_scale = gr.Slider(
-                        label="Guidance Scale",
-                        minimum=1,
-                        maximum=6,
-                        step=0.5,
-                        value=1,
-                    )
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=4,
+                    maximum=8,
+                    step=1,
+                    value=4,
+                )
 
-                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._"""
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1,
+                    maximum=6,
+                    step=0.5,
+                    value=1,
                 )
 
-                negative_prompt = gr.Text(
+                negative_prompt = gr.Textbox(
                     label="Negative Prompt",
-                    show_label=False,
-                    max_lines=1,
                     placeholder="Enter a negative Prompt",
-                    container=False,
+                    lines=2,
                 )
 
     gr.on(
-        [
-            run_button.click,
-            seed.change,
-            randomize_seed.change,
-            prompt.submit,
-            negative_prompt.change,
-            negative_prompt.submit,
-            guidance_scale.change,
-        ],
+        [run_button.click, prompt.submit],
         fn=infer,
         inputs=[
             pre_prompt,
@@ -274,24 +243,30 @@ with gr.Blocks(css=css) as demo:
             negative_prompt,
             guidance_scale,
             user_lora_selector,
-            user_lora_weight,
+            gr.Slider(label="Selected LoRA Weight", minimum=0.5, maximum=3, step=0.1, value=1),
         ],
         outputs=[result],
     )
 
+    clear_button.click(lambda: "", outputs=[prompt, result])
+
     gallery.select(
         fn=update_selection,
         inputs=[gr_sdxl_loras],
-        outputs=[
-            user_lora_selector,
-            pre_prompt,
-        ],
-        show_progress="hidden",
+        outputs=[user_lora_selector, pre_prompt],
     )
 
-    gr.Markdown("**Disclaimer:**")
     gr.Markdown(
-        "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."
+        """
+        ## 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!
+        """
     )
 
 demo.queue().launch()