| | 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 = "AI Image Generator" |
| | if not torch.cuda.is_available(): |
| | DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>" |
| | 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/cagliostrolab/animagine-xl-3.1/blob/main/animagine-xl-3.1.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, |
| | ) |
| |
|
| | 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() |
| |
|
| |
|
| | 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"""<h1><span>{DESCRIPTION}</span></h1>""", |
| | elem_id="title", |
| | ) |
| | |
| | |
| | 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=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, |
| | ], |
| | outputs=[result, gr_metadata], |
| | api_name="run", |
| | ) |
| |
|
| | if __name__ == "__main__": |
| | demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB) |
| |
|
