import os import random import uuid import gradio as gr import numpy as np from PIL import Image import spaces import torch from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler from typing import Tuple css = """ footer { visibility: hidden; } """ DESCRIPTIONXX = """ ## REALVISXL V5.0 ⚡ """ examples = [ "Many strawberry splashed with drops of water within a fancy bowl 4k, hdr --v 6.0 --style raw", "A lemon slice falling into a glass of water, droplets suspended mid-flight, 4K, HDR, --v 6.0 --style raw", "A honey dipper dripping golden honey, thick droplets captured mid-pour, 4K, HDR, --v 6.0 --style raw", "A splash of milk erupting as a cookie drops into a glass, dynamic and crisp, 4K, HDR, --v 6.0 --style raw", "A coffee bean explosion mid-grind, fine powder and whole beans suspended, 4K, HDR, --v 6.0 --style raw", "An orange being squeezed, juice droplets scattering outward in vivid detail, 4K, HDR, --v 6.0 --style raw", "A watermelon slice splashing into a pool of water, vibrant droplets and waves captured mid-motion, 4K, HDR, --v 6.0 --style raw", ] MODEL_OPTIONS = { "REALVISXL V5.0": "SG161222/RealVisXL_V5.0", # "LIGHTNING V5.0": "SG161222/RealVisXL_V5.0_Lightning", } MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096")) USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1")) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") style_list = [ { "name": "3840 x 2160", "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", }, { "name": "2560 x 1440", "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", }, { "name": "HD+", "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", }, { "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, negative: str = "") -> Tuple[str, str]: if style_name in styles: p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) else: p, n = styles[DEFAULT_STYLE_NAME] if not negative: negative = "" return p.replace("{prompt}", positive), n + negative def load_and_prepare_model(model_id): pipe = StableDiffusionXLPipeline.from_pretrained( model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, use_safetensors=True, add_watermarker=False, ).to(device) pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) if USE_TORCH_COMPILE: pipe.compile() if ENABLE_CPU_OFFLOAD: pipe.enable_model_cpu_offload() return pipe # Preload and compile both models models = {key: load_and_prepare_model(value) for key, value in MODEL_OPTIONS.items()} MAX_SEED = np.iinfo(np.int32).max 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 @spaces.GPU(duration=60, enable_queue=True) def generate( model_choice: str, prompt: str, negative_prompt: str = "", use_negative_prompt: bool = False, style_selection: str = DEFAULT_STYLE_NAME, seed: int = 1, width: int = 1024, height: int = 1024, guidance_scale: float = 3, num_inference_steps: int = 25, randomize_seed: bool = False, use_resolution_binning: bool = True, num_images: int = 1, progress=gr.Progress(track_tqdm=True), ): global models pipe = models[model_choice] seed = int(randomize_seed_fn(seed, randomize_seed)) generator = torch.Generator(device=device).manual_seed(seed) prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt) options = { "prompt": [prompt] * num_images, "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps, "generator": generator, "output_type": "pil", } if use_resolution_binning: options["use_resolution_binning"] = True images = [] for i in range(0, num_images, BATCH_SIZE): batch_options = options.copy() batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE] if "negative_prompt" in batch_options: batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE] images.extend(pipe(**batch_options).images) image_paths = [save_image(img) for img in images] return image_paths, seed def load_predefined_images1(): predefined_images1 = [ "assets/7.png", "assets/8.png", "assets/9.png", "assets/1.png", "assets/2.png", "assets/3.png", "assets/4.png", "assets/5.png", "assets/6.png", ] return predefined_images1 with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as demo: gr.HTML('
😄Image to Video Explore: https://huggingface.co/spaces/ginigen/theater
') gr.Markdown(DESCRIPTIONXX) 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.Row(): model_choice = gr.Dropdown( label="Model Selection🔻", choices=list(MODEL_OPTIONS.keys()), value="REALVISXL V5.0" ) with gr.Accordion("Advanced options", open=False, visible=False): style_selection = gr.Radio( show_label=True, container=True, interactive=True, choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME, label="Quality Style", ) num_images = gr.Slider( label="Number of Images", minimum=1, maximum=5, step=1, value=1, ) with gr.Row(): with gr.Column(scale=1): use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) negative_prompt = gr.Text( label="Negative prompt", max_lines=5, lines=4, placeholder="Enter a negative prompt", value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation", visible=True, ) 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=MAX_IMAGE_SIZE, step=64, value=1024, ) height = gr.Slider( label="Height", minimum=512, maximum=MAX_IMAGE_SIZE, step=64, value=1024, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance Scale", minimum=0.1, maximum=6, step=0.1, value=3.0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=60, step=1, value=28, ) gr.Examples( examples=examples, inputs=prompt, 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, negative_prompt.submit, run_button.click, ], fn=generate, inputs=[ model_choice, prompt, negative_prompt, use_negative_prompt, style_selection, seed, width, height, guidance_scale, num_inference_steps, randomize_seed, num_images, ], outputs=[result, seed], ) gr.Markdown("### REALVISXL V5.0") predefined_gallery = gr.Gallery(label="REALVISXL V5.0", columns=3, show_label=False, value=load_predefined_images1()) #gr.Markdown("### LIGHTNING V5.0") #predefined_gallery = gr.Gallery(label="LIGHTNING V5.0", columns=3, show_label=False, value=load_predefined_images()) if __name__ == "__main__": demo.queue(max_size=50).launch(show_api=False)