Delete app.py

app.py

@@ -1,394 +0,0 @@
-import spaces
-import gradio as gr
-import torch
-from PIL import Image
-from diffusers import DiffusionPipeline, QwenImageEditPipeline, FlowMatchEulerDiscreteScheduler
-import random
-import uuid
-import numpy as np
-import time
-import zipfile
-import os
-import requests
-from urllib.parse import urlparse
-import tempfile
-import shutil
-import math
-
-# --- App Description ---
-DESCRIPTION = """## Qwen Image Hpc/."""
-
-# --- Helper Functions for Both Tabs ---
-MAX_SEED = np.iinfo(np.int32).max
-
-def save_image(img):
-    """Saves a PIL image to a temporary file with a unique name."""
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    """Returns a random seed if randomize_seed is True, otherwise returns the original seed."""
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-# --- Model Loading ---
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# --- Qwen-Image-Gen Model ---
-pipe_qwen_gen = DiffusionPipeline.from_pretrained(
-    "Qwen/Qwen-Image",
-    torch_dtype=dtype
-).to(device)
-
-# --- Qwen-Image-Edit Model with Lightning LoRA ---
-scheduler_config = {
-    "base_image_seq_len": 256,
-    "base_shift": math.log(3),
-    "invert_sigmas": False,
-    "max_image_seq_len": 8192,
-    "max_shift": math.log(3),
-    "num_train_timesteps": 1000,
-    "shift": 1.0,
-    "shift_terminal": None,
-    "stochastic_sampling": False,
-    "time_shift_type": "exponential",
-    "use_beta_sigmas": False,
-    "use_dynamic_shifting": True,
-    "use_exponential_sigmas": False,
-    "use_karras_sigmas": False,
-}
-scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
-
-pipe_qwen_edit = QwenImageEditPipeline.from_pretrained(
-    "Qwen/Qwen-Image-Edit",
-    scheduler=scheduler,
-    torch_dtype=dtype
-).to(device)
-
-try:
-    pipe_qwen_edit.load_lora_weights(
-        "lightx2v/Qwen-Image-Lightning",
-        weight_name="Qwen-Image-Lightning-8steps-V1.1.safetensors"
-    )
-    pipe_qwen_edit.fuse_lora()
-    print("Successfully loaded Lightning LoRA weights for Qwen-Image-Edit")
-except Exception as e:
-    print(f"Warning: Could not load Lightning LoRA weights for Qwen-Image-Edit: {e}")
-    print("Continuing with the base Qwen-Image-Edit model...")
-
-
-# --- Qwen-Image-Gen Functions ---
-aspect_ratios = {
-    "1:1": (1328, 1328),
-    "16:9": (1664, 928),
-    "9:16": (928, 1664),
-    "4:3": (1472, 1140),
-    "3:4": (1140, 1472)
-}
-
-def load_lora_opt(pipe, lora_input):
-    """Loads a LoRA from a local path, Hugging Face repo, or URL."""
-    lora_input = lora_input.strip()
-    if not lora_input:
-        return
-
-    if "/" in lora_input and not lora_input.startswith("http"):
-        pipe.load_lora_weights(lora_input, adapter_name="default")
-        return
-
-    if lora_input.startswith("http"):
-        url = lora_input
-        if "huggingface.co" in url and "/blob/" not in url and "/resolve/" not in url:
-            repo_id = urlparse(url).path.strip("/")
-            pipe.load_lora_weights(repo_id, adapter_name="default")
-            return
-
-        if "/blob/" in url:
-            url = url.replace("/blob/", "/resolve/")
-
-        tmp_dir = tempfile.mkdtemp()
-        local_path = os.path.join(tmp_dir, os.path.basename(urlparse(url).path))
-
-        try:
-            print(f"Downloading LoRA from {url}...")
-            resp = requests.get(url, stream=True)
-            resp.raise_for_status()
-            with open(local_path, "wb") as f:
-                for chunk in resp.iter_content(chunk_size=8192):
-                    f.write(chunk)
-            print(f"Saved LoRA to {local_path}")
-            pipe.load_lora_weights(local_path, adapter_name="default")
-        finally:
-            shutil.rmtree(tmp_dir, ignore_errors=True)
-
-@spaces.GPU(duration=120)
-def generate_qwen(
-    prompt: str,
-    negative_prompt: str = "",
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 4.0,
-    randomize_seed: bool = False,
-    num_inference_steps: int = 50,
-    num_images: int = 1,
-    zip_images: bool = False,
-    lora_input: str = "",
-    lora_scale: float = 1.0,
-    progress=gr.Progress(track_tqdm=True),
-):
-    """Main generation function for Qwen-Image-Gen."""
-    seed = randomize_seed_fn(seed, randomize_seed)
-    generator = torch.Generator(device).manual_seed(seed)
-
-    start_time = time.time()
-
-    current_adapters = pipe_qwen_gen.get_list_adapters()
-    for adapter in current_adapters:
-        pipe_qwen_gen.delete_adapters(adapter)
-    pipe_qwen_gen.disable_lora()
-
-    if lora_input and lora_input.strip() != "":
-        load_lora_opt(pipe_qwen_gen, lora_input)
-        pipe_qwen_gen.set_adapters(["default"], adapter_weights=[lora_scale])
-
-    images = pipe_qwen_gen(
-        prompt=prompt,
-        negative_prompt=negative_prompt if negative_prompt else " ",
-        height=height,
-        width=width,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        num_images_per_prompt=num_images,
-        generator=generator,
-    ).images
-
-    end_time = time.time()
-    duration = end_time - start_time
-
-    image_paths = [save_image(img) for img in images]
-    zip_path = None
-    if zip_images and len(image_paths) > 0:
-        zip_name = str(uuid.uuid4()) + ".zip"
-        with zipfile.ZipFile(zip_name, 'w') as zipf:
-            for i, img_path in enumerate(image_paths):
-                zipf.write(img_path, arcname=f"Img_{i}.png")
-        zip_path = zip_name
-
-    current_adapters = pipe_qwen_gen.get_list_adapters()
-    for adapter in current_adapters:
-        pipe_qwen_gen.delete_adapters(adapter)
-    pipe_qwen_gen.disable_lora()
-
-    return image_paths, seed, f"{duration:.2f}", zip_path
-
-@spaces.GPU(duration=120)
-def generate(
-    prompt: str,
-    negative_prompt: str,
-    use_negative_prompt: bool,
-    seed: int,
-    width: int,
-    height: int,
-    guidance_scale: float,
-    randomize_seed: bool,
-    num_inference_steps: int,
-    num_images: int,
-    zip_images: bool,
-    lora_input: str,
-    lora_scale: float,
-    progress=gr.Progress(track_tqdm=True),
-):
-    """UI wrapper for the Qwen-Image-Gen generation function."""
-    final_negative_prompt = negative_prompt if use_negative_prompt else ""
-    return generate_qwen(
-        prompt=prompt,
-        negative_prompt=final_negative_prompt,
-        seed=seed,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        randomize_seed=randomize_seed,
-        num_inference_steps=num_inference_steps,
-        num_images=num_images,
-        zip_images=zip_images,
-        lora_input=lora_input,
-        lora_scale=lora_scale,
-        progress=progress,
-    )
-
-# --- Qwen-Image-Edit Functions ---
-@spaces.GPU(duration=60)
-def infer_edit(
-    image,
-    prompt,
-    seed=42,
-    randomize_seed=False,
-    true_guidance_scale=1.0,
-    num_inference_steps=8,
-    progress=gr.Progress(track_tqdm=True),
-):
-    """Main inference function for Qwen-Image-Edit."""
-    if image is None:
-        raise gr.Error("Please upload an image to edit.")
-
-    negative_prompt = " "
-    seed = randomize_seed_fn(seed, randomize_seed)
-    generator = torch.Generator(device=device).manual_seed(seed)
-
-    print(f"Original prompt: '{prompt}'")
-    print(f"Negative Prompt: '{negative_prompt}'")
-    print(f"Seed: {seed}, Steps: {num_inference_steps}, Guidance: {true_guidance_scale}")
-
-    try:
-        images = pipe_qwen_edit(
-            image,
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            num_inference_steps=num_inference_steps,
-            generator=generator,
-            true_cfg_scale=true_guidance_scale,
-            num_images_per_prompt=1
-        ).images
-        return images[0], seed
-    except Exception as e:
-        print(f"Error during inference: {e}")
-        raise gr.Error(f"An error occurred during image editing: {e}")
-
-# --- Gradio UI ---
-css = '''
-.gradio-container {
-    max-width: 800px !important;
-    margin: 0 auto !important;
-}
-h1 {
-    text-align: center;
-}
-footer {
-    visibility: hidden;
-}
-'''
-
-with gr.Blocks(css=css, theme="bethecloud/storj_theme", delete_cache=(240, 240)) as demo:
-    gr.Markdown(DESCRIPTION)
-
-    with gr.Tabs():
-        with gr.TabItem("Qwen-Image-Gen"):
-            with gr.Column():
-                with gr.Row():
-                    prompt_gen = gr.Text(
-                        label="Prompt",
-                        show_label=False,
-                        max_lines=1,
-                        placeholder="✦︎ Enter your prompt for generation",
-                        container=False,
-                    )
-                    run_button_gen = gr.Button("Generate", scale=0, variant="primary")
-                result_gen = gr.Gallery(label="Result", columns=2, show_label=False, preview=True, height="auto")
-
-                with gr.Row():
-                    aspect_ratio_gen = gr.Dropdown(
-                        label="Aspect Ratio",
-                        choices=list(aspect_ratios.keys()),
-                        value="1:1",
-                    )
-                    lora_gen = gr.Textbox(label="Optional LoRA", placeholder="Enter Hugging Face repo ID or URL...")
-
-                with gr.Accordion("Additional Options", open=False):
-                    use_negative_prompt_gen = gr.Checkbox(label="Use negative prompt", value=True)
-                    negative_prompt_gen = gr.Text(
-                        label="Negative prompt",
-                        max_lines=1,
-                        placeholder="Enter a negative prompt",
-                        value="text, watermark, copyright, blurry, low resolution",
-                    )
-                    seed_gen = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-                    randomize_seed_gen = gr.Checkbox(label="Randomize seed", value=True)
-                    with gr.Row():
-                        width_gen = gr.Slider(label="Width", minimum=512, maximum=2048, step=64, value=1328)
-                        height_gen = gr.Slider(label="Height", minimum=512, maximum=2048, step=64, value=1328)
-                    guidance_scale_gen = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=20.0, step=0.1, value=4.0)
-                    num_inference_steps_gen = gr.Slider("Number of inference steps", 1, 100, 50, step=1)
-                    num_images_gen = gr.Slider("Number of images", 1, 5, 1, step=1)
-                    zip_images_gen = gr.Checkbox(label="Zip generated images", value=False)
-                    with gr.Row():
-                        lora_scale_gen = gr.Slider(label="LoRA Scale", minimum=0, maximum=2, step=0.01, value=1)
-
-                    gr.Markdown("### Output Information")
-                    seed_display_gen = gr.Textbox(label="Seed used", interactive=False)
-                    generation_time_gen = gr.Textbox(label="Generation time (seconds)", interactive=False)
-                    zip_file_gen = gr.File(label="Download ZIP")
-            
-            # --- Gen Tab Logic ---
-            def set_dimensions(ar):
-                w, h = aspect_ratios[ar]
-                return gr.update(value=w), gr.update(value=h)
-
-            aspect_ratio_gen.change(fn=set_dimensions, inputs=aspect_ratio_gen, outputs=[width_gen, height_gen])
-            use_negative_prompt_gen.change(fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt_gen, outputs=negative_prompt_gen)
-
-            gen_inputs = [
-                prompt_gen, negative_prompt_gen, use_negative_prompt_gen, seed_gen, width_gen, height_gen,
-                guidance_scale_gen, randomize_seed_gen, num_inference_steps_gen, num_images_gen,
-                zip_images_gen, lora_gen, lora_scale_gen
-            ]
-            gen_outputs = [result_gen, seed_display_gen, generation_time_gen, zip_file_gen]
-
-            gr.on(triggers=[prompt_gen.submit, run_button_gen.click], fn=generate, inputs=gen_inputs, outputs=gen_outputs)
-
-            gen_examples = [
-                "A decadent slice of layered chocolate cake on a ceramic plate with a drizzle of chocolate syrup and powdered sugar dusted on top.",
-                "A young girl wearing school uniform stands in a classroom, writing on a chalkboard. The text 'Introducing Qwen-Image' appears in neat white chalk.",
-                "一幅精致细腻的工笔画，画面中心是一株蓬勃生长的红色牡丹，花朵繁茂。",
-                "Realistic still life photography style: A single, fresh apple, resting on a clean, soft-textured surface.",
-            ]
-            gr.Examples(examples=gen_examples, inputs=prompt_gen, outputs=gen_outputs, fn=generate, cache_examples=False)
-
-        with gr.TabItem("Qwen-Image-Edit"):
-            with gr.Column():
-                with gr.Row():
-                    input_image_edit = gr.Image(label="Input Image", type="pil", height=400)
-                    result_edit = gr.Image(label="Result", type="pil", height=400)
-                
-                with gr.Row():
-                    prompt_edit = gr.Text(
-                        label="Edit Instruction",
-                        show_label=False,
-                        placeholder="Describe the edit you want to make",
-                        container=False,
-                    )
-                    run_button_edit = gr.Button("Edit", variant="primary")
-
-                with gr.Accordion("Advanced Settings", open=False):
-                    seed_edit = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42)
-                    randomize_seed_edit = gr.Checkbox(label="Randomize seed", value=True)
-                    with gr.Row():
-                        true_guidance_scale_edit = gr.Slider(
-                            label="True guidance scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0
-                        )
-                        num_inference_steps_edit = gr.Slider(
-                            label="Inference steps (Lightning LoRA)", minimum=4, maximum=28, step=1, value=8
-                        )
-            
-            # --- Edit Tab Logic ---
-            edit_inputs = [
-                input_image_edit, prompt_edit, seed_edit, randomize_seed_edit,
-                true_guidance_scale_edit, num_inference_steps_edit
-            ]
-            edit_outputs = [result_edit, seed_edit]
-            
-            gr.on(triggers=[prompt_edit.submit, run_button_edit.click], fn=infer_edit, inputs=edit_inputs, outputs=edit_outputs)
-            
-            edit_examples = [
-                ["image-edit/cat.png", "make the cat wear sunglasses"],
-                ["image-edit/girl.png", "change her hair to blonde"],
-            ]
-       
-            gr.Examples(examples=edit_examples, inputs=[input_image_edit, prompt_edit], outputs=edit_outputs, fn=infer_edit, cache_examples=True)
-
-
-if __name__ == "__main__":
-    demo.queue(max_size=50).launch(share=False, debug=True)