#!/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 import torch #from diffusers import DiffusionPipeline from diffusers import StableDiffusionPipeline from tqdm import tqdm from safetensors.torch import load_file from concurrent.futures import ThreadPoolExecutor import uuid #import cv2 model_id = "Lykon/dreamshaper-7" #"openskyml/lcm-lora-sdxl-turbo" #"SimianLuo/LCM_Dreamshaper_v7" DESCRIPTION = '''# Fast Stable Diffusion CPU with Latent Consistency Model 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) ''' if not torch.cuda.is_available(): DESCRIPTION += "\n

Running on CPU 🤩 This demo works on CPU 👌.

" MAX_SEED = np.iinfo(np.int32).max CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1" MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768")) USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1" DTYPE = torch.float32 # torch.float16 works as well, but pictures seem to be a bit worse #pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main") #"SimianLuo/LCM_Dreamshaper_v7" ''' pipe = DiffusionPipeline.from_pretrained( model_id , custom_pipeline=model_id, custom_revision="main", low_cpu_mem_usage=True, safety_checker= None, use_safetensors=True ) #pipe.to(torch_device="cpu",torch_dtype="float16", torch_dtype=DTYPE) pipe.to(torch_dtype="float32" ) pipe.to("cpu") ''' #from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained(model_id, safety_checker= None) prompt = "A futuristic cityscape at sunset" image = pipe(prompt).images[0] image.show() 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, width: int = 512, height: int = 512, guidance_scale: float = 8.0, num_inference_steps: int = 4, num_images: int = 1, randomize_seed: bool = False, progress = gr.Progress(track_tqdm=True), profile: gr.OAuthProfile | None = None, ) -> PIL.Image.Image: seed = randomize_seed_fn(seed, randomize_seed) torch.manual_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, lcm_origin_steps=50, 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(): width = gr.Slider( label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512, ) height = gr.Slider( label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512, ) 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, ) with gr.Row(): num_images = gr.Slider( label="Number of images", minimum=1, maximum=8, step=1, value=1, visible=True, ) with gr.Accordion("Past generations", open=False): tr = gr.Textbox(value="ol") 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, width, height, guidance_scale, num_inference_steps, num_images, randomize_seed ], outputs=[result, seed], api_name="run", ) if __name__ == "__main__": demo.queue(api_open=False) # demo.queue(max_size=20).launch() demo.launch()