import os import gc import gradio as gr import numpy as np import torch import json import spaces import config import utils import logging from PIL import Image, PngImagePlugin from datetime import datetime from diffusers.models import AutoencoderKL from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) DESCRIPTION = "Juggernaut XL" if not torch.cuda.is_available(): DESCRIPTION += "\n

Running on CPU 🥶 This demo does not work on CPU.

" IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1" HF_TOKEN = os.getenv("HF_TOKEN") CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1" MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512")) MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048")) USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1" ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1" OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs") MODEL = os.getenv( "MODEL", "https://huggingface.co/RunDiffusion/Juggernaut-XL-v9/blob/main/Juggernaut-XL_v9_RunDiffusionPhoto_v2.safetensors", ) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") def load_pipeline(model_name): vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, ) pipeline = ( StableDiffusionXLPipeline.from_single_file if MODEL.endswith(".safetensors") else StableDiffusionXLPipeline.from_pretrained ) pipe = pipeline( model_name, vae=vae, torch_dtype=torch.float16, custom_pipeline="lpw_stable_diffusion_xl", use_safetensors=True, add_watermarker=False, use_auth_token=HF_TOKEN, variant="fp16", ) pipe.to(device) return pipe @spaces.GPU def generate( prompt: str, negative_prompt: str = "", seed: int = 0, custom_width: int = 1024, custom_height: int = 1024, guidance_scale: float = 7.0, num_inference_steps: int = 30, sampler: str = "DPM++ 2M SDE Karras", aspect_ratio_selector: str = "1024 x 1024", use_upscaler: bool = False, upscaler_strength: float = 0.55, upscale_by: float = 1.5, progress=gr.Progress(track_tqdm=True), ) -> Image: generator = utils.seed_everything(seed) width, height = utils.aspect_ratio_handler( aspect_ratio_selector, custom_width, custom_height, ) width, height = utils.preprocess_image_dimensions(width, height) backup_scheduler = pipe.scheduler pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler) if use_upscaler: upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components) metadata = { "prompt": prompt, "negative_prompt": negative_prompt, "resolution": f"{width} x {height}", "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps, "seed": seed, "sampler": sampler, } if use_upscaler: new_width = int(width * upscale_by) new_height = int(height * upscale_by) metadata["use_upscaler"] = { "upscale_method": "nearest-exact", "upscaler_strength": upscaler_strength, "upscale_by": upscale_by, "new_resolution": f"{new_width} x {new_height}", } else: metadata["use_upscaler"] = None logger.info(json.dumps(metadata, indent=4)) try: if use_upscaler: latents = pipe( prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator, output_type="latent", ).images upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by) images = upscaler_pipe( prompt=prompt, negative_prompt=negative_prompt, image=upscaled_latents, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, strength=upscaler_strength, generator=generator, output_type="pil", ).images else: images = pipe( prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator, output_type="pil", ).images if images and IS_COLAB: for image in images: filepath = utils.save_image(image, metadata, OUTPUT_DIR) logger.info(f"Image saved as {filepath} with metadata") return images, metadata except Exception as e: logger.exception(f"An error occurred: {e}") raise finally: if use_upscaler: del upscaler_pipe pipe.scheduler = backup_scheduler utils.free_memory() if torch.cuda.is_available(): pipe = load_pipeline(MODEL) logger.info("Loaded on Device!") else: pipe = None with gr.Blocks(css="style.css") as demo: title = gr.HTML( f"""

{DESCRIPTION}

""", elem_id="title", ) gr.Markdown( f"""Gradio demo for [Juggernaut XL](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9)""", elem_id="subtitle", ) 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=5, placeholder="Enter your prompt", container=False, ) run_button = gr.Button( "Generate", variant="primary", scale=0 ) result = gr.Gallery( label="Result", columns=1, preview=True, show_label=False ) with gr.Accordion(label="Advanced Settings", open=False): negative_prompt = gr.Text( label="Negative Prompt", max_lines=5, placeholder="Enter a negative prompt", value="(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)" ) aspect_ratio_selector = gr.Radio( label="Aspect Ratio", choices=config.aspect_ratios, value="1024 x 1024", container=True, ) with gr.Group(visible=False) as custom_resolution: with gr.Row(): custom_width = gr.Slider( label="Width", minimum=MIN_IMAGE_SIZE, maximum=MAX_IMAGE_SIZE, step=8, value=1024, ) custom_height = gr.Slider( label="Height", minimum=MIN_IMAGE_SIZE, maximum=MAX_IMAGE_SIZE, step=8, value=1024, ) use_upscaler = gr.Checkbox(label="Use Upscaler", value=False) with gr.Row() as upscaler_row: upscaler_strength = gr.Slider( label="Strength", minimum=0, maximum=1, step=0.05, value=0.55, visible=False, ) upscale_by = gr.Slider( label="Upscale by", minimum=1, maximum=1.5, step=0.1, value=1.5, visible=False, ) sampler = gr.Dropdown( label="Sampler", choices=config.sampler_list, interactive=True, value="DPM++ 2M SDE Karras", ) with gr.Row(): seed = gr.Slider( label="Seed", minimum=0, maximum=utils.MAX_SEED, step=1, value=0 ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Group(): with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale", minimum=1, maximum=12, step=0.1, value=7.0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=50, step=1, value=28, ) with gr.Accordion(label="Generation Parameters", open=False): gr_metadata = gr.JSON(label="Metadata", show_label=False) gr.Examples( examples=config.examples, inputs=prompt, outputs=[result, gr_metadata], fn=lambda *args, **kwargs: generate(*args, use_upscaler=True, **kwargs), cache_examples=CACHE_EXAMPLES, ) use_upscaler.change( fn=lambda x: [gr.update(visible=x), gr.update(visible=x)], inputs=use_upscaler, outputs=[upscaler_strength, upscale_by], queue=False, api_name=False, ) aspect_ratio_selector.change( fn=lambda x: gr.update(visible=x == "Custom"), inputs=aspect_ratio_selector, outputs=custom_resolution, queue=False, api_name=False, ) inputs = [ prompt, negative_prompt, seed, custom_width, custom_height, guidance_scale, num_inference_steps, sampler, aspect_ratio_selector, use_upscaler, upscaler_strength, upscale_by, ] prompt.submit( fn=utils.randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False, ).then( fn=generate, inputs=inputs, outputs=result, api_name="run", ) negative_prompt.submit( fn=utils.randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False, ).then( fn=generate, inputs=inputs, outputs=result, api_name=False, ) run_button.click( fn=utils.randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False, ).then( fn=generate, inputs=inputs, outputs=[result, gr_metadata], api_name=False, ) demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)