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from __future__ import annotations |
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import os |
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import random |
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import time |
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import gradio as gr |
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import numpy as np |
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import PIL.Image |
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from huggingface_hub import snapshot_download |
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from diffusers import DiffusionPipeline |
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from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel, OVStableDiffusionPipeline |
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import os |
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from tqdm import tqdm |
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import gradio_user_history as gr_user_history |
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from concurrent.futures import ThreadPoolExecutor |
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import uuid |
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DESCRIPTION = '''# Latent Consistency Model OpenVino CPU |
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Based on [Latency Consistency Model OpenVINO CPU](deinferno/Latent_Consistency_Model_OpenVino_CPU) HF space |
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Converted from [SoteMix](https://huggingface.co/Disty0/SoteMix) [Project page](https://latent-consistency-models.github.io) |
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<p>Running on CPU 🥶.</p> |
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''' |
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MAX_SEED = np.iinfo(np.int32).max |
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CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1" |
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model_id = "Disty0/LCM_SoteMix" |
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batch_size = 1 |
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width = int(os.getenv("IMAGE_WIDTH", "512")) |
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height = int(os.getenv("IMAGE_HEIGHT", "512")) |
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num_images = int(os.getenv("NUM_IMAGES", "1")) |
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class CustomOVModelVaeDecoder(OVModelVaeDecoder): |
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def __init__( |
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self, model: openvino.runtime.Model, parent_model: OVBaseModel, ov_config: Optional[Dict[str, str]] = None, model_dir: str = None, |
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): |
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super(OVModelVaeDecoder, self).__init__(model, parent_model, ov_config, "vae_decoder", model_dir) |
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pipe = OVStableDiffusionPipeline.from_pretrained(model_id, scheduler = scheduler, compile = False, ov_config = {"CACHE_DIR":""}) |
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taesd_dir = snapshot_download(repo_id="deinferno/taesd-openvino") |
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pipe.vae_decoder = CustomOVModelVaeDecoder(model = OVBaseModel.load_model(f"{taesd_dir}/vae_decoder/openvino_model.xml"), parent_model = pipe, model_dir = taesd_dir) |
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pipe.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images) |
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pipe.compile() |
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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return seed |
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def save_image(img, profile: gr.OAuthProfile | None, metadata: dict): |
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unique_name = str(uuid.uuid4()) + '.png' |
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img.save(unique_name) |
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gr_user_history.save_image(label=metadata["prompt"], image=img, profile=profile, metadata=metadata) |
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return unique_name |
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def save_images(image_array, profile: gr.OAuthProfile | None, metadata: dict): |
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paths = [] |
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with ThreadPoolExecutor() as executor: |
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paths = list(executor.map(save_image, image_array, [profile]*len(image_array), [metadata]*len(image_array))) |
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return paths |
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def generate( |
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prompt: str, |
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seed: int = 0, |
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guidance_scale: float = 1.0, |
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num_inference_steps: int = 4, |
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randomize_seed: bool = False, |
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progress = gr.Progress(track_tqdm=True), |
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profile: gr.OAuthProfile | None = None, |
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) -> PIL.Image.Image: |
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global batch_size |
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global width |
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global height |
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global num_images |
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seed = randomize_seed_fn(seed, randomize_seed) |
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np.random.seed(seed) |
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start_time = time.time() |
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result = pipe( |
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prompt=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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num_images_per_prompt=num_images, |
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output_type="pil", |
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).images |
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paths = save_images(result, profile, metadata={"prompt": prompt, "seed": seed, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps}) |
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print(time.time() - start_time) |
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return paths, seed |
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examples = [ |
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo", |
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, dark red hair, blue eyes, long hair, straight hair, medium breasts, mature female, sweater", |
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, pov, scenery, flowers, shrine, dark red hair, blue eyes, long hair, straight hair, medium breasts, mature female, sweater", |
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"(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, abstract, abstract background, (bloom, swirling lights, light particles), floating, romanticized, blush, emotional, cat ears, fire, galaxy, dark red hair, blue eyes, long hair, straight hair, medium breasts, mature female, sweater", |
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] |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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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.Group(): |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button("Run", scale=0) |
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result = gr.Gallery( |
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label="Generated images", show_label=False, elem_id="gallery", grid=[2] |
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) |
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with gr.Accordion("Advanced options", open=False): |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0, |
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randomize=True |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed across runs", value=True) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="Guidance scale for base", |
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minimum=2, |
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maximum=14, |
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step=0.1, |
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value=8.0, |
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) |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps for base", |
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minimum=1, |
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maximum=8, |
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step=1, |
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value=4, |
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) |
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with gr.Accordion("Past generations", open=False): |
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gr_user_history.render() |
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gr.Examples( |
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examples=examples, |
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inputs=prompt, |
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outputs=result, |
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fn=generate, |
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cache_examples=CACHE_EXAMPLES, |
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) |
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gr.on( |
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triggers=[ |
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prompt.submit, |
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run_button.click, |
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], |
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fn=generate, |
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inputs=[ |
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prompt, |
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seed, |
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guidance_scale, |
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num_inference_steps, |
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randomize_seed |
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], |
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outputs=[result, seed], |
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api_name="run", |
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) |
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if __name__ == "__main__": |
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demo.queue(api_open=False) |
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demo.launch() |
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