Delete app.py

app.py

@@ -1,181 +0,0 @@
-import spaces
-import gradio as gr
-import torch
-from PIL import Image
-from diffusers import DiffusionPipeline
-import random
-import uuid
-import numpy as np
-import time
-import os
-
-# Description for the app
-DESCRIPTION = """
-# Qwen Image Upscaler
-
-Upload a low-quality or small image, and this app will use the Qwen-Image model to generate a higher-resolution, more detailed version.
-"""
-
-# --- Helper functions ---
-
-def save_image(img: Image.Image) -> str:
-    """Saves an image to a unique filename and returns the path."""
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
-
-MAX_SEED = np.iinfo(np.int32).max
-
-# --- Load the Qwen/Qwen-Image pipeline ---
-# This single pipeline is used for both text-to-image and image-to-image (upscaling)
-
-print("Loading Qwen-Image model...")
-dtype = torch.bfloat16
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-pipe_qwen = DiffusionPipeline.from_pretrained(
-    "Qwen/Qwen-Image", 
-    torch_dtype=dtype
-).to(device)
-print("Model loaded successfully.")
-
-
-# --- The main upscaler function ---
-
-@spaces.GPU(duration=120)
-def upscale_image(
-    image: Image.Image,
-    prompt: str,
-    negative_prompt: str,
-    seed: int,
-    guidance_scale: float,
-    randomize_seed: bool,
-    num_inference_steps: int,
-    progress=gr.Progress(track_tqdm=True)
-):
-    """
-    Takes a low-resolution image and upscales it using the Qwen-Image model.
-    """
-    if image is None:
-        raise gr.Error("No image uploaded. Please upload an image to upscale.")
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    
-    generator = torch.Generator(device).manual_seed(seed)
-
-    start_time = time.time()
-    
-    # The pipeline automatically handles upscaling when an `image` argument is provided.
-    upscaled_image = pipe_qwen(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        image=image,  # Providing the input image triggers the upscaling/img2img mode
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        output_type="pil",
-    ).images[0]
-
-    end_time = time.time()
-    duration = end_time - start_time
-    
-    image_path = save_image(upscaled_image)
-
-    print(f"Upscaling finished in {duration:.2f} seconds. Seed used: {seed}")
-
-    return image_path, seed, f"{duration:.2f}"
-
-
-# --- Gradio User Interface ---
-
-css = '''
-.gradio-container {
-    max-width: 840px !important;
-    margin: 0 auto !important;
-}
-h1 {
-    text-align: center;
-}
-footer {
-    visibility: hidden;
-}
-'''
-
-with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
-    gr.Markdown(DESCRIPTION)
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            # FIXED LINE: Removed the `tool='editor'` argument
-            image_upload = gr.Image(
-                label="Upload Low-Resolution Image", 
-                type="pil"
-            )
-            prompt = gr.Textbox(
-                label="Prompt", 
-                value="ultra-detailed, high quality, 4k, 8k, masterpiece",
-                placeholder="Describe the desired result (e.g., 'photorealistic, sharp focus')."
-            )
-            upscale_button = gr.Button("Upscale Image", variant="primary")
-
-        with gr.Column(scale=1):
-            upscaled_image_result = gr.Image(label="Upscaled Image")
-            
-    with gr.Accordion("Upscaler Options", open=False):
-        negative_prompt = gr.Text(
-            label="Negative Prompt",
-            max_lines=1,
-            placeholder="Enter concepts to avoid (e.g., 'blurry, pixelated').",
-            value="blurry, low resolution, text, watermark, jpeg artifacts, compression",
-        )
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
-        )
-        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-        guidance_scale = gr.Slider(
-            label="Guidance Scale",
-            minimum=0.0,
-            maximum=20.0,
-            step=0.1,
-            value=4.0,
-        )
-        num_inference_steps = gr.Slider(
-            label="Number of Inference Steps",
-            minimum=1,
-            maximum=100,
-            step=1,
-            value=25, # Upscaling often requires fewer steps than generation from scratch
-        )
-
-    with gr.Accordion("Output Information", open=True):
-         with gr.Row():
-            seed_display = gr.Textbox(label="Seed used", interactive=False)
-            generation_time = gr.Textbox(label="Generation time (seconds)", interactive=False)
-            
-    # Connect the button to the function
-    upscale_button.click(
-        fn=upscale_image,
-        inputs=[
-            image_upload,
-            prompt,
-            negative_prompt,
-            seed,
-            guidance_scale,
-            randomize_seed,
-            num_inference_steps
-        ],
-        outputs=[
-            upscaled_image_result,
-            seed_display,
-            generation_time,
-        ],
-        api_name="upscale"
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch(share=False, debug=True, show_error=True)