import spaces import gradio as gr import torch from PIL import Image from diffusers import DiffusionPipeline import random import uuid from typing import Union, List, Optional import numpy as np import time import zipfile # Description for the app DESCRIPTION = """## Qwen Image Hpc/.""" # Helper functions def save_image(img): unique_name = str(uuid.uuid4()) + ".png" img.save(unique_name) return unique_name def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 2048 # Load Qwen/Qwen-Image pipeline dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" pipe_qwen = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=dtype).to(device) # Aspect ratios aspect_ratios = { "1:1": (1328, 1328), "16:9": (1664, 928), "9:16": (928, 1664), "4:3": (1472, 1140), "3:4": (1140, 1472) } # Generation function for Qwen/Qwen-Image @spaces.GPU 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, progress=gr.Progress(track_tqdm=True), ): if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator(device).manual_seed(seed) start_time = time.time() images = pipe_qwen( prompt=prompt, negative_prompt=negative_prompt if negative_prompt else None, height=height, width=width, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, num_images_per_prompt=num_images, generator=generator, output_type="pil", ).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: 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 return image_paths, seed, f"{duration:.2f}", zip_path # Wrapper function to handle UI logic @spaces.GPU 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, progress=gr.Progress(track_tqdm=True), ): 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, progress=progress, ) # Examples examples = [ "An attractive young woman with blue eyes lying face down on the bed, light white and light amber, timeless beauty, sunrays shine upon it", "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，既有盛开的硕大花瓣，也有含苞待放的花蕾，层次丰富，色彩艳丽而不失典雅。牡丹枝叶舒展，叶片浓绿饱满，脉络清晰可见，与红花相映成趣。一只蓝紫色蝴蝶仿佛被画中花朵吸引，停驻在画面中央的一朵盛开牡丹上，流连忘返，蝶翼轻展，细节逼真，仿佛随时会随风飞舞。整幅画作笔触工整严谨，色彩浓郁鲜明，展现出中国传统工笔画的精妙与神韵，画面充满生机与灵动之感。", "A medium-angle shot of a young woman with long brown hair, wearing glasses, standing in front of purple and white lights", "A capybara wearing a suit holding a sign that reads Hello World" ] css = ''' .gradio-container { max-width: 590px !important; margin: 0 auto !important; } h1 { text-align: center; } footer { visibility: hidden; } ''' # Gradio interface with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: gr.Markdown(DESCRIPTION) with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) run_button = gr.Button("Run", scale=0, variant="primary") result = gr.Gallery(label="Result", columns=1, show_label=False, preview=True) with gr.Row(): aspect_ratio = gr.Dropdown( label="Aspect Ratio", choices=list(aspect_ratios.keys()), value="1:1", ) with gr.Accordion("Additional Options", open=False): use_negative_prompt = gr.Checkbox( label="Use negative prompt", value=False, visible=True ) negative_prompt = gr.Text( label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", visible=False, ) 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(): width = gr.Slider( label="Width", minimum=512, maximum=2048, step=64, value=1024, ) height = gr.Slider( label="Height", minimum=512, maximum=2048, step=64, value=1024, ) 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=50, ) num_images = gr.Slider( label="Number of images", minimum=1, maximum=5, step=1, value=1, ) zip_images = gr.Checkbox(label="Zip generated images", value=False) gr.Markdown("### Output Information") seed_display = gr.Textbox(label="Seed used", interactive=False) generation_time = gr.Textbox(label="Generation time (seconds)", interactive=False) zip_file = gr.File(label="Download ZIP") # Update aspect ratio def set_dimensions(ar): w, h = aspect_ratios[ar] return gr.update(value=w), gr.update(value=h) aspect_ratio.change( fn=set_dimensions, inputs=aspect_ratio, outputs=[width, height] ) # Negative prompt visibility use_negative_prompt.change( fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt, outputs=negative_prompt ) # Run button and prompt submit gr.on( triggers=[prompt.submit, run_button.click], fn=generate, inputs=[ prompt, negative_prompt, use_negative_prompt, seed, width, height, guidance_scale, randomize_seed, num_inference_steps, num_images, zip_images, ], outputs=[result, seed_display, generation_time, zip_file], api_name="run", ) # Examples gr.Examples( examples=examples, inputs=prompt, outputs=[result, seed_display, generation_time, zip_file], fn=generate, cache_examples=False, ) if __name__ == "__main__": demo.queue(max_size=30).launch(mcp_server=True, ssr_mode=False, show_error=True)