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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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import torch |
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from diffusers import DiffusionPipeline |
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import torch |
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import os |
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import torch |
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from tqdm import tqdm |
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from safetensors.torch import load_file |
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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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import cv2 |
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DESCRIPTION = '''# Latent Consistency Model |
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Distilled from [Dreamshaper v7](https://huggingface.co/Lykon/dreamshaper-7) fine-tune of [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with only 4,000 training iterations (~32 A100 GPU Hours). [Project page](https://latent-consistency-models.github.io) |
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''' |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" |
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MAX_SEED = np.iinfo(np.int32).max |
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1" |
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MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768")) |
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USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1" |
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DTYPE = torch.float32 |
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pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7") |
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pipe.to(torch_device="cuda", torch_dtype=DTYPE) |
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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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width: int = 512, |
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height: int = 512, |
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guidance_scale: float = 8.0, |
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num_inference_steps: int = 4, |
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num_images: 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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seed = randomize_seed_fn(seed, randomize_seed) |
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torch.manual_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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lcm_origin_steps=50, |
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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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"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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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=512, |
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) |
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height = gr.Slider( |
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label="Height", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=512, |
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) |
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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.Row(): |
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num_images = gr.Slider( |
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label="Number of images", |
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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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visible=False, |
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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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width, |
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height, |
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guidance_scale, |
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num_inference_steps, |
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num_images, |
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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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