#!/usr/bin/env python import os import random import uuid import json import gradio as gr import numpy as np from PIL import Image import spaces import torch from diffusers import DiffusionPipeline bad_words = json.loads(os.getenv('BAD_WORDS', "[]")) bad_words_negative = json.loads(os.getenv('BAD_WORDS_NEGATIVE', "[]")) default_negative = os.getenv("default_negative","") def check_text(prompt, negative=""): for i in bad_words: if i in prompt: return True for i in bad_words_negative: if i in negative: return True return False DESCRIPTION = """# RealVisXL_V3.0""" if not torch.cuda.is_available(): DESCRIPTION += "\n
Running on CPU 🥶 This demo may not work on CPU.
" MAX_SEED = np.iinfo(np.int32).max CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1" MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048")) USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") NUM_IMAGES_PER_PROMPT = 1 if torch.cuda.is_available(): pipe = DiffusionPipeline.from_pretrained( "SG161222/RealVisXL_V3.0", torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False, variant="fp16" ) if ENABLE_CPU_OFFLOAD: pipe.enable_model_cpu_offload() else: pipe.to(device) print("Loaded on Device!") if USE_TORCH_COMPILE: pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) print("Model Compiled!") 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 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, use_resolution_binning: bool = True, progress=gr.Progress(track_tqdm=True)): if check_text(prompt, negative_prompt): return 'NSFW Detection' return _generate( prompt, negative_prompt, use_negative_prompt, seed, width, height, guidance_scale, randomize_seed, use_resolution_binning, progress ) @spaces.GPU(enable_queue=True) 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, use_resolution_binning: bool = True, progress=gr.Progress(track_tqdm=True), ): pipe.to(device) seed = int(randomize_seed_fn(seed, randomize_seed)) generator = torch.Generator().manual_seed(seed) if not use_negative_prompt: negative_prompt = "" # type: ignore negative_prompt += default_negative images = pipe( prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=25, generator=generator, num_images_per_prompt=NUM_IMAGES_PER_PROMPT, use_resolution_binning=use_resolution_binning, output_type="pil", ).images image_paths = [save_image(img) for img in images] return image_paths, seed examples = [ "neon holography crystal cat", "a cat eating a piece of cheese", "an astronaut riding a horse in space", "a cartoon of a boy playing with a tiger", "a cute robot artist painting on an easel, concept art", #"a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone" ] css = ''' .gradio-container{max-width: 560px !important} h1{text-align:center} ''' with gr.Blocks(css=css) as demo: gr.Markdown(DESCRIPTION) gr.DuplicateButton( value="Duplicate Space for private use", elem_id="duplicate-button", visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", ) with gr.Group(): 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=NUM_IMAGES_PER_PROMPT, show_label=False) with gr.Accordion("Advanced options", open=False): with gr.Row(): 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=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(visible=True): width = gr.Slider( label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) height = gr.Slider( label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, ) with gr.Row(): guidance_scale = gr.Slider( label="Guidance Scale", minimum=0.1, maximum=20, step=0.1, value=3.0, ) gr.Examples( examples=examples, inputs=prompt, outputs=[result, seed], fn=generate, cache_examples=CACHE_EXAMPLES, ) 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=[ prompt, negative_prompt, use_negative_prompt, seed, width, height, guidance_scale, randomize_seed, ], outputs=[result, seed], api_name="run", ) if __name__ == "__main__": demo.queue(max_size=20).launch()