#!/usr/bin/env python 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 css = ''' .gradio-container{max-width: 570px !important} h1{text-align:center} footer { visibility: hidden } ''' DESCRIPTIONXX = """ ## REALVISXL V5 + V5 LIGHTNING ⚡ """ examples = [ "Illustration of A starry night camp in the mountains, 4k, cinematic --ar 85:128 --v 6.0 --style raw", "A delicious ceviche cheesecake slice, 4k, octane render, ray tracing, Ultra-High-Definition" ] MODEL_OPTIONS = { "REALVISXL V5.0": "SG161222/RealVisXL_V4.0_Lightning", "V5.0 LIGHTNING": "SG161222/RealVisXL_V3.0_Turbo", } 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") 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, 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) 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_images(): # predefined_images = [ # "assets/1.png", # "assets/2.png", # "assets/3.png", # "assets/4.png", # "assets/5.png", # "assets/6.png", # "assets/7.png", #"assets/8.png", #"assets/9.png", #"assets/10.png", #"assets/11.png", #"assets/12.png", #] #return predefined_images with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: 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=True): 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="(octane render, render, drawing, anime, bad photo, bad photography:1.3), (worst quality, low quality, blurry:1.2), (bad teeth, deformed teeth, deformed lips), (bad anatomy, bad proportions:1.1), (deformed iris, deformed pupils), (deformed eyes, bad eyes), (deformed face, ugly face, bad face), (deformed hands, bad hands, fused fingers), morbid, mutilated, mutation, disfigured", 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=40, step=1, value=23, ) 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, seed, width, height, guidance_scale, num_inference_steps, randomize_seed, num_images ], outputs=[result, seed], api_name="run", ) gr.Markdown("⚡Models used in the playground [[REALVISXL V5.0]](https://huggingface.co/SG161222/RealVisXL_V5.0), [[REALVISXL V5.0 LIGHTNING]](https://huggingface.co/SG161222/RealVisXL_V5.0_Lightning) for image generation. stable diffusion xl piped (sdxl) model HF. This is the demo space for generating images using the Stable Diffusion XL models, with multi different variants available.") gr.Markdown("⚡This is the demo space for generating images using Stable Diffusion XL with quality styles, different models and types. Try the sample prompts to generate higher quality images. Try the sample prompts for generating higher quality images.Try prompts.") gr.Markdown("⚠️ users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.") # with gr.Column(scale=3): # gr.Markdown("### Image Gallery") # predefined_gallery = gr.Gallery(label="Image Gallery", columns=3, show_label=False, value=load_predefined_images()) if __name__ == "__main__": demo.queue(max_size=20).launch(show_api=False)