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from __future__ import annotations |
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import requests |
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import re |
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import threading |
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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 lcm_scheduler import LCMScheduler |
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from lcm_ov_pipeline import OVLatentConsistencyModelPipeline |
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from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel |
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import os |
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from tqdm import tqdm |
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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](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model) HF space |
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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 = "Kano001/Dreamshaper_v7-Openvino" |
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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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scheduler = LCMScheduler.from_pretrained(model_id, subfolder="scheduler") |
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pipe = OVLatentConsistencyModelPipeline.from_pretrained(model_id, scheduler = scheduler, compile = False, ov_config = {"CACHE_DIR":""}) |
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taesd_dir = snapshot_download(repo_id="Kano001/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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api_url = None |
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def make_api_request(): |
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global api_url |
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response = requests.get("https://genielamp-image2.hf.space/") |
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api_url = response.text |
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match = re.search(r'"root"\s*:\s*"([^"]+)"', response.text) |
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api_url = match.group(1) + "/file=" |
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print(api_url) |
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def delayed_api_request(): |
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threading.Timer(10, make_api_request).start() |
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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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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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url: str, |
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seed: int = 0, |
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guidance_scale: float = 8.0, |
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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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url = api_url |
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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, url |
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examples = [ |
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"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography", |
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"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k", |
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", |
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"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece", |
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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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url = gr.Text( |
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label="url", |
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value="Null", |
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show_label=False, |
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placeholder="Null", |
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max_lines=1, |
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container=False, |
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interactive=False, |
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) |
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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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url, |
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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, url], |
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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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delayed_api_request() |
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demo.launch() |
