#!/usr/bin/env python from __future__ import annotations import os import random import time import gradio as gr import numpy as np import PIL.Image from huggingface_hub import snapshot_download from diffusers import DiffusionPipeline from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel, OVStableDiffusionPipeline import os from tqdm import tqdm import gradio_user_history as gr_user_history from concurrent.futures import ThreadPoolExecutor import uuid DESCRIPTION = '''# Latent Consistency Model OpenVino CPU Based on [Latency Consistency Model OpenVINO CPU](https://huggingface.co/spaces/deinferno/Latent_Consistency_Model_OpenVino_CPU) HF space Converted from [SoteMix](https://huggingface.co/Disty0/SoteMix) to [LCM_SoteMix](https://huggingface.co/Disty0/LCM_SoteMix) and then to OpenVINO This model is for Anime art style. [LCM Project page](https://latent-consistency-models.github.io)

Running on CPU with OpenVINO Acceleration

''' MAX_SEED = np.iinfo(np.int32).max CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1" model_id = "Disty0/LCM_SoteMix" batch_size = -1 width = int(os.getenv("IMAGE_WIDTH", "512")) height = int(os.getenv("IMAGE_HEIGHT", "512")) num_images = int(os.getenv("NUM_IMAGES", "1")) class CustomOVModelVaeDecoder(OVModelVaeDecoder): def __init__( self, model: openvino.runtime.Model, parent_model: OVBaseModel, ov_config: Optional[Dict[str, str]] = None, model_dir: str = None, ): super(OVModelVaeDecoder, self).__init__(model, parent_model, ov_config, "vae_decoder", model_dir) pipe = OVStableDiffusionPipeline.from_pretrained(model_id, compile = False, ov_config = {"CACHE_DIR":""}) # Inject TAESD taesd_dir = snapshot_download(repo_id="deinferno/taesd-openvino") pipe.vae_decoder = CustomOVModelVaeDecoder(model = OVBaseModel.load_model(f"{taesd_dir}/vae_decoder/openvino_model.xml"), parent_model = pipe, model_dir = taesd_dir) pipe.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images) pipe.compile() def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed def save_image(img, profile: gr.OAuthProfile | None, metadata: dict): unique_name = str(uuid.uuid4()) + '.png' img.save(unique_name) gr_user_history.save_image(label=metadata["prompt"], image=img, profile=profile, metadata=metadata) return unique_name def save_images(image_array, profile: gr.OAuthProfile | None, metadata: dict): paths = [] with ThreadPoolExecutor() as executor: paths = list(executor.map(save_image, image_array, [profile]*len(image_array), [metadata]*len(image_array))) return paths def generate( prompt: str, seed: int = 0, guidance_scale: float = 1.0, num_inference_steps: int = 4, randomize_seed: bool = False, progress = gr.Progress(track_tqdm=True), profile: gr.OAuthProfile | None = None, ) -> PIL.Image.Image: global batch_size global width global height global num_images seed = randomize_seed_fn(seed, randomize_seed) np.random.seed(seed) start_time = time.time() result = pipe( prompt=prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, num_images_per_prompt=num_images, output_type="pil", ).images paths = save_images(result, profile, metadata={"prompt": prompt, "seed": seed, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps}) print(time.time() - start_time) return paths, seed examples = [ "(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo", "(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, dark red hair, long hair, blue eyes, straight hair, cat ears, medium breasts, mature female, white sweater", "(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, scenery, wind, petals, rim lighting, shrine, lens flare, light scatter, depth of field, lens refraction, dark red hair, long hair, blue eyes, straight hair, cat ears, medium breasts, mature female, white sweater", "(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, supernova, abstract, abstract background, (bloom, swirling lights, light particles), fire, galaxy, floating, romanticized, blush, looking at viewer, dark red hair, long hair, blue eyes, straight hair, cat ears, medium breasts, mature female, white sweater", ] with gr.Blocks(css="style.css") as demo: gr.Markdown(DESCRIPTION) gr.DuplicateButton( value="Duplicate Space for private use", elem_id="duplicate-button", visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", ) with gr.Group(): with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) run_button = gr.Button("Run", scale=0) result = gr.Gallery( label="Generated images", show_label=False, elem_id="gallery", grid=[2] ) with gr.Accordion("Advanced options", open=False): seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True ) randomize_seed = gr.Checkbox(label="Randomize seed across runs", value=True) with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale for base", minimum=1, maximum=8, step=0.1, value=1.0, ) num_inference_steps = gr.Slider( label="Number of inference steps for base", minimum=1, maximum=8, step=1, value=4, ) with gr.Accordion("Past generations", open=False): gr_user_history.render() gr.Examples( examples=examples, inputs=prompt, outputs=result, fn=generate, cache_examples=CACHE_EXAMPLES, ) gr.on( triggers=[ prompt.submit, run_button.click, ], fn=generate, inputs=[ prompt, seed, guidance_scale, num_inference_steps, randomize_seed ], outputs=[result, seed], api_name="run", ) if __name__ == "__main__": demo.queue(api_open=False) # demo.queue(max_size=20).launch() demo.launch()