import spaces import gradio as gr import torch from PIL import Image from diffusers import DiffusionPipeline import random import uuid from typing import Tuple import numpy as np import time import zipfile DESCRIPTION = """## flux realism hpc/. """ 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 base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "strangerzonehf/Flux-Super-Realism-LoRA" trigger_word = "Super Realism" pipe.load_lora_weights(lora_repo) pipe.to("cuda") style_list = [ { "name": "3840 x 2160", "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "", }, { "name": "2560 x 1440", "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "", }, { "name": "HD+", "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "", }, { "name": "Style Zero", "prompt": "{prompt}", "negative_prompt": "", }, ] styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} DEFAULT_STYLE_NAME = "3840 x 2160" STYLE_NAMES = list(styles.keys()) def apply_style(style_name: str, positive: str) -> Tuple[str, str]: p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) return p.replace("{prompt}", positive), n @spaces.GPU def generate( prompt: str, negative_prompt: str = "", use_negative_prompt: bool = False, seed: int = 0, width: int = 1024, height: int = 1024, guidance_scale: float = 3, randomize_seed: bool = False, style_name: str = DEFAULT_STYLE_NAME, num_inference_steps: int = 30, num_images: int = 1, zip_images: bool = False, progress=gr.Progress(track_tqdm=True), ): positive_prompt, style_negative_prompt = apply_style(style_name, prompt) if use_negative_prompt: final_negative_prompt = style_negative_prompt + " " + negative_prompt else: final_negative_prompt = style_negative_prompt final_negative_prompt = final_negative_prompt.strip() if trigger_word: positive_prompt = f"{trigger_word} {positive_prompt}" seed = int(randomize_seed_fn(seed, randomize_seed)) generator = torch.Generator(device="cuda").manual_seed(seed) start_time = time.time() images = pipe( prompt=positive_prompt, negative_prompt=final_negative_prompt if final_negative_prompt else None, width=width, height=height, 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 examples = [ "Super Realism, High-resolution photograph, woman, UHD, photorealistic, shot on a Sony A7III --chaos 20 --ar 1:2 --style raw --stylize 250", "Woman in a red jacket, snowy, in the style of hyper-realistic portraiture, caninecore, mountainous vistas, timeless beauty, palewave, iconic, distinctive noses --ar 72:101 --stylize 750 --v 6", "Super Realism, Headshot of handsome young man, wearing dark gray sweater with buttons and big shawl collar, brown hair and short beard, serious look on his face, black background, soft studio lighting, portrait photography --ar 85:128 --v 6.0 --style", "Super-realism, Purple Dreamy, a medium-angle shot of a young woman with long brown hair, wearing a pair of eye-level glasses, stands in front of a backdrop of purple and white lights. The womans eyes are closed, her lips are slightly parted, as if she is looking up at the sky. Her hair is cascading over her shoulders, framing her face. She is wearing a sleeveless top, adorned with tiny white dots, and a gold chain necklace around her neck. Her left earrings are dangling from her ears, adding a pop of color to the scene." ] css = ''' .gradio-container { max-width: 590px !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(): 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.Accordion("Additional Options", open=False): style_selection = gr.Dropdown( label="Quality Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME, interactive=True, ) use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False) 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=1280, ) height = gr.Slider( label="Height", minimum=512, maximum=2048, step=64, value=832, ) guidance_scale = gr.Slider( label="Guidance Scale", minimum=0.1, maximum=20.0, step=0.1, value=3.0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=40, step=1, value=30, ) 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") gr.Examples( examples=examples, inputs=prompt, outputs=[result, seed_display, generation_time, zip_file], fn=generate, cache_examples=False, ) use_negative_prompt.change( fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt, outputs=negative_prompt, api_name=False, ) gr.on( triggers=[ prompt.submit, run_button.click, ], fn=generate, inputs=[ prompt, negative_prompt, use_negative_prompt, seed, width, height, guidance_scale, randomize_seed, style_selection, num_inference_steps, num_images, zip_images, ], outputs=[result, seed_display, generation_time, zip_file], api_name="run", ) if __name__ == "__main__": demo.queue(max_size=120).launch(mcp_server=True, ssr_mode=False, show_error=True)