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import os |
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import gc |
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import gradio as gr |
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import numpy as np |
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import torch |
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import json |
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import spaces |
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import config |
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import utils |
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import logging |
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from PIL import Image, PngImagePlugin |
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from datetime import datetime |
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from diffusers.models import AutoencoderKL |
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from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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DESCRIPTION = "Juggernaut XL" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>" |
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IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1" |
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HF_TOKEN = os.getenv("HF_TOKEN") |
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1" |
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MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512")) |
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MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048")) |
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USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1" |
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ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1" |
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OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs") |
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MODEL = os.getenv( |
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"MODEL", |
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"https://huggingface.co/RunDiffusion/Juggernaut-XL-v9/blob/main/Juggernaut-XL_v9_RunDiffusionPhoto_v2.safetensors", |
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) |
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torch.backends.cudnn.deterministic = True |
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torch.backends.cudnn.benchmark = False |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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def load_pipeline(model_name): |
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vae = AutoencoderKL.from_pretrained( |
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"madebyollin/sdxl-vae-fp16-fix", |
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torch_dtype=torch.float16, |
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) |
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pipeline = ( |
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StableDiffusionXLPipeline.from_single_file |
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if MODEL.endswith(".safetensors") |
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else StableDiffusionXLPipeline.from_pretrained |
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) |
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pipe = pipeline( |
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model_name, |
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vae=vae, |
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torch_dtype=torch.float16, |
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custom_pipeline="lpw_stable_diffusion_xl", |
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use_safetensors=True, |
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add_watermarker=False, |
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use_auth_token=HF_TOKEN, |
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variant="fp16", |
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) |
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pipe.to(device) |
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return pipe |
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@spaces.GPU |
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def generate( |
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prompt: str, |
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negative_prompt: str = "", |
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seed: int = 0, |
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custom_width: int = 1024, |
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custom_height: int = 1024, |
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guidance_scale: float = 7.0, |
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num_inference_steps: int = 30, |
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sampler: str = "DPM++ 2M SDE Karras", |
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aspect_ratio_selector: str = "1024 x 1024", |
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use_upscaler: bool = False, |
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upscaler_strength: float = 0.55, |
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upscale_by: float = 1.5, |
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progress=gr.Progress(track_tqdm=True), |
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) -> Image: |
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generator = utils.seed_everything(seed) |
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width, height = utils.aspect_ratio_handler( |
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aspect_ratio_selector, |
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custom_width, |
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custom_height, |
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) |
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width, height = utils.preprocess_image_dimensions(width, height) |
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backup_scheduler = pipe.scheduler |
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pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler) |
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if use_upscaler: |
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upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components) |
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metadata = { |
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"prompt": prompt, |
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"negative_prompt": negative_prompt, |
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"resolution": f"{width} x {height}", |
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"guidance_scale": guidance_scale, |
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"num_inference_steps": num_inference_steps, |
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"seed": seed, |
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"sampler": sampler, |
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} |
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if use_upscaler: |
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new_width = int(width * upscale_by) |
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new_height = int(height * upscale_by) |
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metadata["use_upscaler"] = { |
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"upscale_method": "nearest-exact", |
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"upscaler_strength": upscaler_strength, |
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"upscale_by": upscale_by, |
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"new_resolution": f"{new_width} x {new_height}", |
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} |
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else: |
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metadata["use_upscaler"] = None |
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logger.info(json.dumps(metadata, indent=4)) |
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try: |
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if use_upscaler: |
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latents = pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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width=width, |
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height=height, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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generator=generator, |
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output_type="latent", |
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).images |
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upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by) |
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images = upscaler_pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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image=upscaled_latents, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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strength=upscaler_strength, |
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generator=generator, |
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output_type="pil", |
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).images |
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else: |
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images = pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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width=width, |
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height=height, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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generator=generator, |
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output_type="pil", |
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).images |
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if images and IS_COLAB: |
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for image in images: |
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filepath = utils.save_image(image, metadata, OUTPUT_DIR) |
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logger.info(f"Image saved as {filepath} with metadata") |
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return images, metadata |
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except Exception as e: |
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logger.exception(f"An error occurred: {e}") |
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raise |
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finally: |
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if use_upscaler: |
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del upscaler_pipe |
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pipe.scheduler = backup_scheduler |
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utils.free_memory() |
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if torch.cuda.is_available(): |
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pipe = load_pipeline(MODEL) |
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logger.info("Loaded on Device!") |
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else: |
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pipe = None |
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with gr.Blocks(css="style.css") as demo: |
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title = gr.HTML( |
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f"""<h1><span>{DESCRIPTION}</span></h1>""", |
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elem_id="title", |
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) |
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gr.Markdown( |
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f"""Gradio demo for [Juggernaut XL](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9)""", |
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elem_id="subtitle", |
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) |
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gr.DuplicateButton( |
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value="Duplicate Space for private use", |
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elem_id="duplicate-button", |
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visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", |
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) |
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with gr.Group(): |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=5, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button( |
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"Generate", |
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variant="primary", |
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scale=0 |
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) |
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result = gr.Gallery( |
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label="Result", |
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columns=1, |
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preview=True, |
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show_label=False |
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) |
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with gr.Accordion(label="Advanced Settings", open=False): |
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negative_prompt = gr.Text( |
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label="Negative Prompt", |
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max_lines=5, |
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placeholder="Enter a negative prompt", |
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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)" |
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) |
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aspect_ratio_selector = gr.Radio( |
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label="Aspect Ratio", |
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choices=config.aspect_ratios, |
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value="1024 x 1024", |
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container=True, |
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) |
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with gr.Group(visible=False) as custom_resolution: |
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with gr.Row(): |
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custom_width = gr.Slider( |
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label="Width", |
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minimum=MIN_IMAGE_SIZE, |
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maximum=MAX_IMAGE_SIZE, |
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step=8, |
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value=1024, |
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) |
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custom_height = gr.Slider( |
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label="Height", |
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minimum=MIN_IMAGE_SIZE, |
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maximum=MAX_IMAGE_SIZE, |
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step=8, |
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value=1024, |
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) |
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use_upscaler = gr.Checkbox(label="Use Upscaler", value=False) |
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with gr.Row() as upscaler_row: |
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upscaler_strength = gr.Slider( |
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label="Strength", |
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minimum=0, |
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maximum=1, |
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step=0.05, |
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value=0.55, |
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visible=False, |
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) |
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upscale_by = gr.Slider( |
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label="Upscale by", |
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minimum=1, |
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maximum=1.5, |
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step=0.1, |
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value=1.5, |
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visible=False, |
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) |
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sampler = gr.Dropdown( |
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label="Sampler", |
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choices=config.sampler_list, |
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interactive=True, |
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value="DPM++ 2M SDE Karras", |
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) |
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with gr.Row(): |
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seed = gr.Slider( |
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label="Seed", minimum=0, maximum=utils.MAX_SEED, step=1, value=0 |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Group(): |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="Guidance scale", |
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minimum=1, |
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maximum=12, |
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step=0.1, |
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value=7.0, |
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) |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps", |
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minimum=1, |
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maximum=50, |
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step=1, |
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value=28, |
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) |
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with gr.Accordion(label="Generation Parameters", open=False): |
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gr_metadata = gr.JSON(label="Metadata", show_label=False) |
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gr.Examples( |
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examples=config.examples, |
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inputs=prompt, |
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outputs=[result, gr_metadata], |
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fn=lambda *args, **kwargs: generate(*args, use_upscaler=True, **kwargs), |
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cache_examples=CACHE_EXAMPLES, |
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) |
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use_upscaler.change( |
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fn=lambda x: [gr.update(visible=x), gr.update(visible=x)], |
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inputs=use_upscaler, |
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outputs=[upscaler_strength, upscale_by], |
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queue=False, |
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api_name=False, |
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) |
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aspect_ratio_selector.change( |
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fn=lambda x: gr.update(visible=x == "Custom"), |
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inputs=aspect_ratio_selector, |
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outputs=custom_resolution, |
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queue=False, |
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api_name=False, |
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) |
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inputs = [ |
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prompt, |
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negative_prompt, |
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seed, |
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custom_width, |
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custom_height, |
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guidance_scale, |
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num_inference_steps, |
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sampler, |
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aspect_ratio_selector, |
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use_upscaler, |
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upscaler_strength, |
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upscale_by, |
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] |
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prompt.submit( |
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fn=utils.randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=generate, |
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inputs=inputs, |
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outputs=result, |
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api_name="run", |
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) |
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negative_prompt.submit( |
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fn=utils.randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=generate, |
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inputs=inputs, |
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outputs=result, |
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api_name=False, |
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) |
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run_button.click( |
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fn=utils.randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=generate, |
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inputs=inputs, |
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outputs=[result, gr_metadata], |
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api_name=False, |
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) |
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demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB) |
