import spaces import os import gc import gradio as gr import gradio_client as grcl import numpy as np import torch import json 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 GRAD_CLIENT = grcl.Client("https://yoinked-da-nsfw-checker.hf.space/") logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) DESCRIPTION = "Illustrious XL v0.1" 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") == "0" 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", "OnomaAIResearch/Illustrious-xl-early-release-v0", ) torch.backends.cudnn.deterministic = True # maybe disable this? seems 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, ) 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 = 28, sampler: str = "Euler a", aspect_ratio_selector: str = "896 x 1152", style_selector: str = "(None)", quality_selector: str = "Standard v3.1", use_upscaler: bool = False, upscaler_strength: float = 0.55, upscale_by: float = 1.5, add_quality_tags: bool = True, progress=gr.Progress(track_tqdm=True), ): generator = utils.seed_everything(seed) width, height = utils.aspect_ratio_handler( aspect_ratio_selector, custom_width, custom_height, ) prompt = utils.add_wildcard(prompt, wildcard_files) prompt, negative_prompt = utils.preprocess_prompt( quality_prompt, quality_selector, prompt, negative_prompt, add_quality_tags ) prompt, negative_prompt = utils.preprocess_prompt( styles, style_selector, prompt, negative_prompt ) 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, "sdxl_style": style_selector, "add_quality_tags": add_quality_tags, "quality_tags": quality_selector, } 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 metadata["Model"] = { "Model": DESCRIPTION, "Model hash": "e3c47aedb0", } 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: image_paths = [ utils.save_image(image, metadata, OUTPUT_DIR, IS_COLAB) for image in images ] for image_path in image_paths: logger.info(f"Image saved as {image_path} with metadata") return image_paths, 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() def genwrap(*args, **kwargs): ipth, mtd = generate(*args, **kwargs) r = GRAD_CLIENT.predict(grcl.file(ipth), "chen-evangelion", 0.4, False, False, api_name="/classify") ratings = val[0] rating = rating['confidences'] highestval, classtype = -1, "aa" for o in rating: if o['confidence'] > highestval: highestval = o['confidence'] classtype = o['label'] if classtype not in ["general", "sensitive"]: #add "questionable" and "explicit" to enable nsfw, or just delete this func return "https://upload.wikimedia.org/wikipedia/commons/b/bf/Bucephala-albeola-010.jpg", mtd return ipth, mtd if torch.cuda.is_available(): pipe = load_pipeline(MODEL) logger.info("Loaded on Device!") else: pipe = None styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.style_list} quality_prompt = { k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.quality_prompt_list } wildcard_files = utils.load_wildcard_files("wildcard") with gr.Blocks(css="style.css", theme="NoCrypt/miku@1.2.1") as demo: title = gr.HTML( f"""

{DESCRIPTION}

""", elem_id="title", ) gr.Markdown( f"""Gradio demo for [OnomaAIResearch/Illustrious-xl-v0.1](2024-9/30 RELEASE) 2024-9/30 RELEASE""", elem_id="subtitle", ) gr.DuplicateButton( value="Duplicate Space for private use", elem_id="duplicate-button", visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", ) with gr.Row(): with gr.Column(scale=2): with gr.Tab("Txt2img"): with gr.Group(): prompt = gr.Text( label="Prompt", max_lines=5, placeholder="Enter your prompt", ) negative_prompt = gr.Text( label="Negative Prompt", max_lines=5, placeholder="Enter a negative prompt", ) with gr.Accordion(label="Quality Tags", open=True): add_quality_tags = gr.Checkbox( label="Add Quality Tags", value=True ) quality_selector = gr.Dropdown( label="Quality Tags Presets", interactive=True, choices=list(quality_prompt.keys()), value="Standard v3.1", ) with gr.Tab("Advanced Settings"): with gr.Group(): style_selector = gr.Radio( label="Style Preset", container=True, interactive=True, choices=list(styles.keys()), value="(None)", ) with gr.Group(): aspect_ratio_selector = gr.Radio( label="Aspect Ratio", choices=config.aspect_ratios, value="896 x 1152", 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, ) with gr.Group(): 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, ) with gr.Group(): sampler = gr.Dropdown( label="Sampler", choices=config.sampler_list, interactive=True, value="Euler a", ) with gr.Group(): 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.Column(scale=3): with gr.Blocks(): run_button = gr.Button("Generate", variant="primary") result = gr.Gallery( label="Result", columns=1, height='100%', preview=True, show_label=False ) 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, ) gr.on( triggers=[ prompt.submit, negative_prompt.submit, run_button.click, ], fn=utils.randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False, ).then( fn=genwrap, inputs=[ prompt, negative_prompt, seed, custom_width, custom_height, guidance_scale, num_inference_steps, sampler, aspect_ratio_selector, style_selector, quality_selector, use_upscaler, upscaler_strength, upscale_by, add_quality_tags, ], outputs=[result, gr_metadata], api_name="run", ) if __name__ == "__main__": demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)