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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 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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import tags |
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|
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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|
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DESCRIPTION = "Animagine XL 3.1" |
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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 = False |
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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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|
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MODEL = os.getenv( |
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"MODEL", |
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"https://huggingface.co/cagliostrolab/animagine-xl-3.1/blob/main/animagine-xl-3.1.safetensors", |
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) |
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VAE_MODEL = os.getenv( |
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"VAE_MODEL", |
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"madebyollin/sdxl-vae-fp16-fix", |
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) |
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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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|
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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|
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def load_pipeline(model_name, vae_model): |
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vae = AutoencoderKL.from_pretrained( |
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vae_model, |
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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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|
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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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) |
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|
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pipe.to(device) |
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return pipe |
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|
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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 = 28, |
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sampler: str = "Euler a", |
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aspect_ratio_selector: str = "896 x 1152", |
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style_selector: str = "(None)", |
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quality_selector: str = "Standard v3.1", |
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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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add_quality_tags: bool = True, |
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add_danbooru_tags: bool = False, |
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rating_tags: str = "general", |
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copyright_tags_list: list[str] = [], |
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character_tags_list: list[str] = [], |
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general_tags: str = "", |
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ban_tags: str = "", |
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do_cfg: bool = False, |
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cfg_scale: float = 1.5, |
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negative_tags: str = "", |
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total_token_length: str = "long", |
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max_new_tokens: int = 128, |
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min_new_tokens: int = 0, |
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temperature: float = 1.0, |
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top_p: float = 1.0, |
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top_k: int = 20, |
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num_beams: int = 1, |
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|
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progress=gr.Progress(track_tqdm=True), |
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): |
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generator = utils.seed_everything(seed) |
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|
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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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|
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prompt = utils.add_wildcard(prompt, wildcard_files) |
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|
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generated_tags_animagine = "" |
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if add_danbooru_tags: |
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generated_tags_animagine = tags.add_tags(prompt, rating_tags, copyright_tags_list, character_tags_list, general_tags, ban_tags, do_cfg, cfg_scale, negative_tags, total_token_length, max_new_tokens, min_new_tokens, temperature, top_p, top_k, num_beams) |
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prompt = generated_tags_animagine.strip() |
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|
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prompt, negative_prompt = utils.preprocess_prompt( |
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quality_prompt, quality_selector, prompt, negative_prompt, add_quality_tags |
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) |
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prompt, negative_prompt = utils.preprocess_prompt( |
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styles, style_selector, prompt, negative_prompt |
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) |
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|
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width, height = utils.preprocess_image_dimensions(width, height) |
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|
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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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|
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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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"sdxl_style": style_selector, |
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"add_quality_tags": add_quality_tags, |
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"quality_tags": quality_selector, |
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} |
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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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metadata["Model"] = { |
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"Model": DESCRIPTION, |
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"Model hash": "e3c47aedb0", |
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} |
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|
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logger.info(json.dumps(metadata, indent=4)) |
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|
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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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|
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if images: |
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image_paths = [ |
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utils.save_image(image, metadata, OUTPUT_DIR, IS_COLAB) |
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for image in images |
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] |
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|
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for image_path in image_paths: |
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logger.info(f"Image saved as {image_path} with metadata") |
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return image_paths, metadata, generated_tags_animagine |
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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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|
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if torch.cuda.is_available(): |
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pipe = load_pipeline(MODEL, VAE_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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|
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styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.style_list} |
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quality_prompt = { |
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k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.quality_prompt_list |
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} |
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wildcard_files = utils.load_wildcard_files("wildcard") |
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|
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COPY_ACTION_JS = """\ |
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(inputs, _outputs) => { |
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// inputs is the string value of the input_text |
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if (inputs.trim() !== "") { |
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navigator.clipboard.writeText(inputs); |
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} |
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}""" |
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|
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with gr.Blocks(css="style.css", theme="NoCrypt/miku@1.2.1") 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 [cagliostrolab/animagine-xl-3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1)""", |
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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.Row(): |
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with gr.Column(scale=2): |
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with gr.Tab("Txt2img"): |
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with gr.Group(): |
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prompt = gr.Text( |
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label="Prompt", |
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max_lines=5, |
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placeholder="Enter your prompt", |
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) |
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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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) |
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with gr.Accordion(label="Quality Tags", open=True): |
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add_quality_tags = gr.Checkbox( |
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label="Add Quality Tags", value=True |
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) |
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quality_selector = gr.Dropdown( |
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label="Quality Tags Presets", |
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interactive=True, |
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choices=list(quality_prompt.keys()), |
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value="Standard v3.1", |
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) |
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add_danbooru_tags = gr.Checkbox( |
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label="Add Generated Tags", value=False |
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) |
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with gr.Tab("Advanced Settings"): |
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with gr.Group(): |
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style_selector = gr.Radio( |
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label="Style Preset", |
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container=True, |
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interactive=True, |
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choices=list(styles.keys()), |
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value="(None)", |
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) |
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with gr.Group(): |
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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="896 x 1152", |
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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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with gr.Group(): |
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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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with gr.Group(): |
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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="Euler a", |
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) |
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with gr.Group(): |
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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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|
|
|
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with gr.Tab("tags"): |
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with gr.Row(): |
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with gr.Column(): |
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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with gr.Group(): |
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rating_dropdown = gr.Dropdown( |
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label="Rating", |
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choices=[ |
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"general", |
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"sensitive", |
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"questionable", |
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"explicit", |
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], |
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value="general", |
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) |
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|
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with gr.Group(): |
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copyright_tags_mode_dropdown = gr.Dropdown( |
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label="Copyright tags mode", |
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choices=[ |
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"None", |
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"Original", |
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|
|
|
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"Custom", |
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], |
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value="None", |
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interactive=True, |
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) |
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copyright_tags_dropdown = gr.Dropdown( |
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label="Copyright tags", |
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choices=tags.get_copyright_tags_list(), |
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value=[], |
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multiselect=True, |
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visible=False, |
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) |
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|
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def on_change_copyright_tags_dropdouwn(mode: str): |
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kwargs: dict = {"visible": mode == "Custom"} |
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if mode == "Original": |
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kwargs["value"] = ["original"] |
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elif mode == "None": |
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kwargs["value"] = [] |
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|
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return gr.update(**kwargs) |
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|
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with gr.Group(): |
|
character_tags_mode_dropdown = gr.Dropdown( |
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label="Character tags mode", |
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choices=[ |
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"None", |
|
|
|
|
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"Custom", |
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], |
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value="None", |
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interactive=True, |
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) |
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character_tags_dropdown = gr.Dropdown( |
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label="Character tags", |
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choices=tags.get_character_tags_list(), |
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value=[], |
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multiselect=True, |
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visible=False, |
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) |
|
|
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def on_change_character_tags_dropdouwn(mode: str): |
|
kwargs: dict = {"visible": mode == "Custom"} |
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if mode == "None": |
|
kwargs["value"] = [] |
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|
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return gr.update(**kwargs) |
|
|
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with gr.Group(): |
|
general_tags_textbox = gr.Textbox( |
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label="General tags (the condition to generate tags)", |
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value="", |
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placeholder="1girl, ...", |
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lines=4, |
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) |
|
|
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ban_tags_textbox = gr.Textbox( |
|
label="Ban tags (tags in this field never appear in generation)", |
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value="", |
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placeholder="official alternate cosutme, english text,...", |
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lines=2, |
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) |
|
|
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generate_btn = gr.Button("Generate", variant="primary") |
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|
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with gr.Accordion(label="Generation config (advanced)", open=False): |
|
with gr.Group(): |
|
do_cfg_check = gr.Checkbox( |
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label="Do CFG (Classifier Free Guidance)", |
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value=False, |
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) |
|
cfg_scale_slider = gr.Slider( |
|
label="CFG scale", |
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maximum=3.0, |
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minimum=0.1, |
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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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negative_tags_textbox = gr.Textbox( |
|
label="Negative prompt", |
|
placeholder="simple background, ...", |
|
value="", |
|
lines=2, |
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visible=False, |
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) |
|
|
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def on_change_do_cfg_check(do_cfg: bool): |
|
kwargs: dict = {"visible": do_cfg} |
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return gr.update(**kwargs), gr.update(**kwargs) |
|
|
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do_cfg_check.change( |
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on_change_do_cfg_check, |
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inputs=[do_cfg_check], |
|
outputs=[cfg_scale_slider, negative_tags_textbox], |
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) |
|
|
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with gr.Group(): |
|
total_token_length_radio = gr.Radio( |
|
label="Total token length", |
|
choices=list(tags.get_length_tags().keys()), |
|
value="long", |
|
) |
|
|
|
with gr.Group(): |
|
max_new_tokens_slider = gr.Slider( |
|
label="Max new tokens", |
|
maximum=256, |
|
minimum=1, |
|
step=1, |
|
value=128, |
|
) |
|
min_new_tokens_slider = gr.Slider( |
|
label="Min new tokens", |
|
maximum=255, |
|
minimum=0, |
|
step=1, |
|
value=0, |
|
) |
|
temperature_slider = gr.Slider( |
|
label="Temperature (larger is more random)", |
|
maximum=1.0, |
|
minimum=0.0, |
|
step=0.1, |
|
value=1.0, |
|
) |
|
top_p_slider = gr.Slider( |
|
label="Top p (larger is more random)", |
|
maximum=1.0, |
|
minimum=0.0, |
|
step=0.1, |
|
value=1.0, |
|
) |
|
top_k_slider = gr.Slider( |
|
label="Top k (larger is more random)", |
|
maximum=500, |
|
minimum=1, |
|
step=1, |
|
value=100, |
|
) |
|
num_beams_slider = gr.Slider( |
|
label="Number of beams (smaller is more random)", |
|
maximum=10, |
|
minimum=1, |
|
step=1, |
|
value=1, |
|
) |
|
|
|
with gr.Column(): |
|
with gr.Group(): |
|
output_tags_natural = gr.Textbox( |
|
label="Generation result", |
|
|
|
interactive=False, |
|
) |
|
output_tags_natural_copy_btn = gr.Button("Copy", visible=False) |
|
output_tags_natural_copy_btn.click( |
|
fn=tags.copy_text, |
|
inputs=[output_tags_natural], |
|
js=COPY_ACTION_JS, |
|
) |
|
|
|
with gr.Group(): |
|
output_tags_general_only = gr.Textbox( |
|
label="General tags only (sorted)", |
|
interactive=False, |
|
) |
|
output_tags_general_only_copy_btn = gr.Button("Copy", visible=False) |
|
output_tags_general_only_copy_btn.click( |
|
fn=tags.copy_text, |
|
inputs=[output_tags_general_only], |
|
js=COPY_ACTION_JS, |
|
) |
|
|
|
with gr.Group(): |
|
output_tags_animagine = gr.Textbox( |
|
label="Output tags (AnimagineXL v3 style order)", |
|
|
|
interactive=False, |
|
) |
|
output_tags_animagine_copy_btn = gr.Button("Copy", visible=False) |
|
output_tags_animagine_copy_btn.click( |
|
fn=tags.copy_text, |
|
inputs=[output_tags_animagine], |
|
js=COPY_ACTION_JS, |
|
) |
|
|
|
with gr.Accordion(label="Metadata", open=False): |
|
_model_backend_md = gr.Markdown( |
|
f"Model backend: {tags.get_model_backend()}", |
|
) |
|
input_prompt_raw = gr.Textbox( |
|
label="Input prompt (raw)", |
|
interactive=False, |
|
lines=4, |
|
) |
|
|
|
output_tags_raw = gr.Textbox( |
|
label="Output tags (raw)", |
|
interactive=False, |
|
lines=4, |
|
) |
|
|
|
elapsed_time_md = gr.Markdown(value="Waiting to generate...") |
|
|
|
copyright_tags_mode_dropdown.change( |
|
on_change_copyright_tags_dropdouwn, |
|
inputs=[copyright_tags_mode_dropdown], |
|
outputs=[copyright_tags_dropdown], |
|
) |
|
character_tags_mode_dropdown.change( |
|
on_change_character_tags_dropdouwn, |
|
inputs=[character_tags_mode_dropdown], |
|
outputs=[character_tags_dropdown], |
|
) |
|
|
|
generate_btn.click( |
|
tags.handle_inputs, |
|
inputs=[ |
|
rating_dropdown, |
|
copyright_tags_dropdown, |
|
character_tags_dropdown, |
|
general_tags_textbox, |
|
ban_tags_textbox, |
|
do_cfg_check, |
|
cfg_scale_slider, |
|
negative_tags_textbox, |
|
total_token_length_radio, |
|
max_new_tokens_slider, |
|
min_new_tokens_slider, |
|
temperature_slider, |
|
top_p_slider, |
|
top_k_slider, |
|
num_beams_slider, |
|
|
|
], |
|
outputs=[ |
|
output_tags_natural, |
|
output_tags_general_only, |
|
output_tags_animagine, |
|
input_prompt_raw, |
|
output_tags_raw, |
|
elapsed_time_md, |
|
output_tags_natural_copy_btn, |
|
output_tags_general_only_copy_btn, |
|
output_tags_animagine_copy_btn, |
|
], |
|
) |
|
|
|
gr.Examples( |
|
examples=[ |
|
["1girl, solo, from side", ""], |
|
["1girl, solo, abstract, from above", ""], |
|
["2girls, yuri", "1boy"], |
|
["no humans, scenery, summer, day", ""], |
|
], |
|
inputs=[ |
|
general_tags_textbox, |
|
ban_tags_textbox, |
|
], |
|
) |
|
|
|
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 |
|
) |
|
generated_tags_animagine = gr.Textbox( |
|
label="Generated tags (AnimagineXL v3 style order)", |
|
|
|
interactive=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=generate, |
|
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, |
|
add_danbooru_tags, |
|
rating_dropdown, |
|
copyright_tags_dropdown, |
|
character_tags_dropdown, |
|
general_tags_textbox, |
|
ban_tags_textbox, |
|
do_cfg_check, |
|
cfg_scale_slider, |
|
negative_tags_textbox, |
|
total_token_length_radio, |
|
max_new_tokens_slider, |
|
min_new_tokens_slider, |
|
temperature_slider, |
|
top_p_slider, |
|
top_k_slider, |
|
num_beams_slider, |
|
|
|
], |
|
outputs=[result, gr_metadata, generated_tags_animagine], |
|
api_name="run", |
|
) |
|
|
|
if __name__ == "__main__": |
|
demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB) |
|
|
