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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import os | |
| import random | |
| import gradio as gr | |
| import numpy as np | |
| import PIL.Image | |
| import torch | |
| from lcm_pipeline import LatentConsistencyModelPipeline | |
| from lcm_scheduler import LCMScheduler | |
| from diffusers import AutoencoderKL, UNet2DConditionModel | |
| from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker | |
| from transformers import CLIPTokenizer, CLIPTextModel, CLIPImageProcessor | |
| import os | |
| import torch | |
| from tqdm import tqdm | |
| from safetensors.torch import load_file | |
| from huggingface_hub import hf_hub_download | |
| DESCRIPTION = "# Latent Consistency" | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
| 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", "512")) | |
| 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 | |
| model_id = "digiplay/DreamShaper_7" | |
| # Initalize Diffusers Model: | |
| vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae") | |
| text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder") | |
| tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer") | |
| config = UNet2DConditionModel.load_config(model_id, subfolder="unet") | |
| config["time_cond_proj_dim"] = 256 | |
| unet = UNet2DConditionModel.from_config(config) | |
| safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_id, subfolder="safety_checker") | |
| feature_extractor = CLIPImageProcessor.from_pretrained(model_id, subfolder="feature_extractor") | |
| # Initalize Scheduler: | |
| scheduler = LCMScheduler(beta_start=0.00085, beta_end=0.0120, beta_schedule="scaled_linear", prediction_type="epsilon") | |
| if torch.cuda.is_available(): | |
| # Replace the unet with LCM: | |
| lcm_unet_ckpt = hf_hub_download("SimianLuo/LCM_Dreamshaper_v7", filename="LCM_Dreamshaper_v7_4k.safetensors") | |
| ckpt = load_file(lcm_unet_ckpt) | |
| m, u = unet.load_state_dict(ckpt, strict=False) | |
| if len(m) > 0: | |
| print("missing keys:") | |
| print(m) | |
| if len(u) > 0: | |
| print("unexpected keys:") | |
| print(u) | |
| # LCM Pipeline: | |
| pipe = LatentConsistencyModelPipeline(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor) | |
| pipe = pipe.to(device="cuda", dtype=DTYPE) | |
| if USE_TORCH_COMPILE: | |
| pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| 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 = 4, | |
| ) -> PIL.Image.Image: | |
| torch.manual_seed(seed) | |
| return 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 | |
| examples = [ | |
| "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
| "An astronaut riding a green horse", | |
| ] | |
| 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_seed = gr.Checkbox(label="Randomize seed", 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=1, | |
| maximum=20, | |
| step=0.1, | |
| value=8.0, | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps for base", | |
| minimum=1, | |
| maximum=5, | |
| step=1, | |
| value=4, | |
| ) | |
| # with gr.Row(): | |
| # num_images = gr.Slider( | |
| # label="Number of images" | |
| # 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=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=generate, | |
| inputs=[ | |
| prompt, | |
| seed, | |
| width, | |
| height, | |
| guidance_scale, | |
| num_inference_steps, | |
| ], | |
| outputs=result, | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| # demo.queue(max_size=20).launch() | |
| demo.launch(share=True) | |