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| 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 = "Animagine XL 3.1" | |
| 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 | |
| ) | |
| img_pipeline = ( | |
| StableDiffusionXLImg2ImgPipeline.from_single_file | |
| if MODEL.endswith(".safetensors") | |
| else StableDiffusionXLImg2ImgPipeline.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, | |
| ) | |
| img_pipe = img_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) | |
| img_pipe.to(device) | |
| return pipe, img_pipe | |
| def load_img(resize_width,img: str): | |
| img = Image.open(img) | |
| width, height = img.size | |
| scale = resize_width / width | |
| resize_height = int(height * scale) | |
| img = img.resize((resize_width, resize_height), Image.Resampling.LANCZOS) | |
| return img, resize_width, resize_height | |
| def example_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 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, | |
| isImg2Img: bool = True, | |
| img_path: str= "", | |
| img2img_strength: float=0.65, | |
| 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) | |
| img_backup_scheduler = img_pipe.scheduler | |
| img_pipe.scheduler = utils.get_scheduler(img_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, | |
| "isImg2Img": isImg2Img, | |
| "img_path": img_path, | |
| "img2img_strength": img2img_strength | |
| } | |
| 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: | |
| if isImg2Img: | |
| print("Img2Img") | |
| img, img_width, img_height = load_img(1024, img_path) | |
| latents = img_pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=img_width, | |
| height=img_height, | |
| image=img, | |
| strength=img2img_strength, | |
| 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: | |
| 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: | |
| if isImg2Img: | |
| print("Img2Img") | |
| img, img_width, img_height = load_img(512, img_path) | |
| images = img_pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=img_width, | |
| height=img_height, | |
| image=img, | |
| strength=img2img_strength, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| 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 | |
| if isImg2Img: | |
| img_pipe.scheduler = img_backup_scheduler | |
| else: | |
| pipe.scheduler = backup_scheduler | |
| utils.free_memory() | |
| def fake_generate(*args,use_upscaler=False,**kwargs): | |
| args = ",".join(args) | |
| #result, metadata = generate(args, use_upscaler=use_upscaler) | |
| return None, None | |
| if torch.cuda.is_available(): | |
| pipe, img_pipe = load_pipeline(MODEL) | |
| logger.info("Loaded on Device!") | |
| else: | |
| pipe, img_pipe = None, 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", | |
| ) | |
| gr.Markdown( | |
| f"""Gradio demo for [cagliostrolab/animagine-xl-3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1)""", | |
| 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("Input"): | |
| 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="Img2Img", open=False): | |
| isImg2Img = gr.Checkbox( | |
| label="Enable Img2Img", value=False | |
| ) | |
| image = gr.Image( | |
| sources=["upload", "webcam", "clipboard"], | |
| type="filepath", | |
| #visible=False, | |
| label="Choose Image" | |
| ) | |
| img2img_strength = gr.Slider( | |
| minimum=0.05, | |
| maximum=1, | |
| step=0.05, | |
| value=0.65, | |
| label="Strength", | |
| #visible=False | |
| ) | |
| 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("ControlNet"): | |
| with gr.Group(): | |
| gr.Label(label="ControlNet is not available now") | |
| use_controlnet = gr.Checkbox( | |
| label="Use ControlNet", | |
| value=False | |
| ) | |
| controlnet_type = gr.Radio( | |
| label="ControlNet", | |
| choices=["Canny", "Depth", "OpenPose"] | |
| ) | |
| 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: example_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, | |
| ) | |
| #isImg2Img.change( | |
| # fn=lambda x: [gr.update(visible=x), gr.update(visible=x)], | |
| # inputs=isImg2Img, | |
| # outputs=[image, img2img_strength], | |
| # 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, | |
| isImg2Img, | |
| image, | |
| img2img_strength | |
| ], | |
| outputs=[result, gr_metadata], | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB) | |