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#!/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 lcm_scheduler import LCMScheduler | |
from lcm_ov_pipeline import OVLatentConsistencyModelPipeline | |
from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel | |
import os | |
from tqdm import tqdm | |
from concurrent.futures import ThreadPoolExecutor | |
import uuid | |
DESCRIPTION = '''# Latent Consistency Model OpenVino CPU | |
Based on [Latency Consistency Model](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model) HF space | |
<p>Running on CPU 🥶.</p> | |
''' | |
MAX_SEED = np.iinfo(np.int32).max | |
CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1" | |
model_id = "Kano001/Dreamshaper_v7-Openvino" | |
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) | |
scheduler = LCMScheduler.from_pretrained(model_id, subfolder="scheduler") | |
pipe = OVLatentConsistencyModelPipeline.from_pretrained(model_id, scheduler = scheduler, compile = False, ov_config = {"CACHE_DIR":""}) | |
# Inject TAESD | |
taesd_dir = snapshot_download(repo_id="Kano001/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) | |
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 = 8.0, | |
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 = [ | |
"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", | |
"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k", | |
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece", | |
] | |
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=2, | |
maximum=14, | |
step=0.1, | |
value=8.0, | |
) | |
num_inference_steps = gr.Slider( | |
label="Number of inference steps for base", | |
minimum=1, | |
maximum=8, | |
step=1, | |
value=4, | |
) | |
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() | |