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Running
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Zero
File size: 7,178 Bytes
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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 DESCRIPTIONz = """## FLUX REALISM 🔥""" DESCRIPTIONy = """ <p align="left"> <a title="Github" href="https://github.com/PRITHIVSAKTHIUR/FLUX-REALPIX" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> <img src="https://img.shields.io/github/stars/PRITHIVSAKTHIUR/FLUX-REALPIX?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars"> </a> </p> """ 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 if not torch.cuda.is_available(): DESCRIPTIONz += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>" base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "prithivMLmods/Canopus-LoRA-Flux-FaceRealism" trigger_word = "Realism" # Leave trigger_word blank if not used. 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", }, { "name": "2560 x 1440", "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", }, { "name": "HD+", "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", }, { "name": "Style Zero", "prompt": "{prompt}", }, ] styles = {k["name"]: k["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) -> str: return styles.get(style_name, styles[DEFAULT_STYLE_NAME]).replace("{prompt}", positive) @spaces.GPU(duration=60, enable_queue=True) def generate( prompt: str, seed: int = 0, width: int = 1024, height: int = 1024, guidance_scale: float = 3, randomize_seed: bool = False, style_name: str = DEFAULT_STYLE_NAME, progress=gr.Progress(track_tqdm=True), ): seed = int(randomize_seed_fn(seed, randomize_seed)) positive_prompt = apply_style(style_name, prompt) if trigger_word: positive_prompt = f"{trigger_word} {positive_prompt}" images = pipe( prompt=positive_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=16, num_images_per_prompt=1, output_type="pil", ).images image_paths = [save_image(img) for img in images] print(image_paths) return image_paths, seed def load_predefined_images(): predefined_images = [ "assets/11.png", "assets/22.png", "assets/33.png", "assets/44.png", "assets/55.webp", "assets/66.png", "assets/77.png", "assets/88.png", "assets/99.png", ] return predefined_images examples = [ "A portrait of an attractive woman in her late twenties with light brown hair and purple, wearing large a a yellow sweater. She is looking directly at the camera, standing outdoors near trees.. --ar 128:85 --v 6.0 --style raw", "A photo of the model wearing a white bodysuit and beige trench coat, posing in front of a train station with hands on head, soft light, sunset, fashion photography, high resolution, 35mm lens, f/22, natural lighting, global illumination. --ar 85:128 --v 6.0 --style raw", ] css = ''' .gradio-container{max-width: 575px !important} h1{text-align:center} footer { visibility: hidden } ''' with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: gr.Markdown(DESCRIPTIONz) gr.Markdown(DESCRIPTIONy) 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) result = gr.Gallery(label="Result", columns=1, show_label=False) with gr.Accordion("Advanced options", open=False, visible=True): seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, visible=True ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Row(visible=True): 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, ) with gr.Row(): 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=16, ) style_selection = gr.Radio( show_label=True, container=True, interactive=True, choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME, label="Quality Style", ) gr.Examples( examples=examples, inputs=prompt, outputs=[result, seed], fn=generate, cache_examples=False, ) gr.on( triggers=[ prompt.submit, run_button.click, ], fn=generate, inputs=[ prompt, seed, width, height, guidance_scale, randomize_seed, style_selection, ], outputs=[result, seed], api_name="run", ) gr.Markdown("### Generated Images") predefined_gallery = gr.Gallery(label="Generated Images", columns=3, show_label=False, value=load_predefined_images()) gr.Markdown("**Disclaimer/Note:**") gr.Markdown("🔥This space provides realistic image generation, which works better for human faces and portraits. Realistic trigger works properly, better for photorealistic trigger words, close-up shots, face diffusion, male, female characters.") gr.Markdown("🔥users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.") if __name__ == "__main__": demo.queue(max_size=40).launch() |