Update Gradio app with multiple files

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+from gradio_image_slider import ImageSlider
 import numpy as np
 import random
 import torch
@@ -29,11 +30,11 @@ pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509",
                                                                                                          torch_dtype=dtype,
                                                                                                          device_map='cuda'),torch_dtype=dtype).to(device)
 
-pipe.load_lora_weights("autoweeb/Qwen-Image-Edit-2509-Photo-to-Anime", 
-                       weight_name="Qwen-Image-Edit-2509-Photo-to-Anime_000001000.safetensors",
-                       adapter_name="anime")
-pipe.set_adapters(["anime"], adapter_weights=[1.])
-pipe.fuse_lora(adapter_names=["anime"], lora_scale=1.0)
+pipe.load_lora_weights("vafipas663/Qwen-Edit-2509-Upscale-LoRA", 
+                       weight_name="qwen-edit-enhance_64-v3_000001500.safetensors",
+                       adapter_name="upscale")
+pipe.set_adapters(["upscale"], adapter_weights=[1.])
+pipe.fuse_lora(adapter_names=["upscale"], lora_scale=1.0)
 pipe.unload_lora_weights()
 
 pipe.transformer.__class__ = QwenImageTransformer2DModel
@@ -44,7 +45,7 @@ optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB",
 MAX_SEED = np.iinfo(np.int32).max
 
 @spaces.GPU
-def convert_to_anime(
+def upscale_image(
     image,
     seed,
     randomize_seed,
@@ -54,7 +55,7 @@ def convert_to_anime(
     width,
     progress=gr.Progress(track_tqdm=True)
 ):
-    prompt = "Convert this photo to anime style"
+    prompt = "Upscale and enhance this image with high quality details"
     
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -81,7 +82,7 @@ def convert_to_anime(
         num_images_per_prompt=1,
     ).images[0]
 
-    return result, seed
+    return (image, result), seed
 
 
 # --- UI ---
@@ -153,10 +154,10 @@ def update_dimensions_on_upload(image):
 
 with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("# 🎨 Photo to Anime", elem_id="title")
+        gr.Markdown("# 🔍 Image Upscaler", elem_id="title")
         gr.Markdown(
             """
-            Transform your photos into beautiful anime-style images ✨
+            Upscale and enhance your images with AI-powered quality improvement ✨
             <br>
             <div style='text-align: center; margin-top: 1rem;'>
                 <a href='https://huggingface.co/spaces/akhaliq/anycoder' target='_blank' style='color: #0071e3; text-decoration: none; font-weight: 500;'>Built with anycoder</a>
@@ -165,30 +166,28 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
             elem_id="description"
         )
 
-        with gr.Row():
-            with gr.Column(scale=1):
-                image = gr.Image(
-                    label="Upload Photo", 
-                    type="pil",
-                    elem_classes="image-container"
-                )
-                
-                with gr.Accordion("⚙️ Advanced Settings", open=False):
-                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-                    randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-                    true_guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
-                    num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=40, step=1, value=4)
-                    height = gr.Slider(label="Height", minimum=256, maximum=2048, step=8, value=1024, visible=False)
-                    width = gr.Slider(label="Width", minimum=256, maximum=2048, step=8, value=1024, visible=False)
-                
-                convert_btn = gr.Button("Convert to Anime", variant="primary", elem_id="convert-btn", size="lg")
-
-            with gr.Column(scale=1):
-                result = gr.Image(
-                    label="Anime Result", 
-                    interactive=False,
-                    elem_classes="image-container"
-                )
+        with gr.Column():
+            image = gr.Image(
+                label="Upload Image", 
+                type="pil",
+                elem_classes="image-container"
+            )
+            
+            with gr.Accordion("⚙️ Advanced Settings", open=False):
+                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+                true_guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
+                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=40, step=1, value=4)
+                height = gr.Slider(label="Height", minimum=256, maximum=2048, step=8, value=1024, visible=False)
+                width = gr.Slider(label="Width", minimum=256, maximum=2048, step=8, value=1024, visible=False)
+            
+            upscale_btn = gr.Button("Upscale Image", variant="primary", elem_id="convert-btn", size="lg")
+
+            result = ImageSlider(
+                label="Before / After", 
+                interactive=False,
+                elem_classes="image-container"
+            )
     
     inputs = [
         image, seed, randomize_seed, true_guidance_scale, 
@@ -196,9 +195,9 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
     ]
     outputs = [result, seed]
 
-    # Convert button click
-    convert_btn.click(
-        fn=convert_to_anime, 
+    # Upscale button click
+    upscale_btn.click(
+        fn=upscale_image, 
         inputs=inputs, 
         outputs=outputs
     )

requirements.txt

@@ -7,4 +7,4 @@ dashscope
 kernels
 torchvision
 peft
-torchao==0.11.0
+torchao==0.11.0