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Running
on
Zero
Running
on
Zero
| import spaces | |
| import argparse | |
| import os | |
| import time | |
| from os import path | |
| from safetensors.torch import load_file | |
| from huggingface_hub import hf_hub_download | |
| cache_path = path.join(path.dirname(path.abspath(__file__)), "models") | |
| os.environ["TRANSFORMERS_CACHE"] = cache_path | |
| os.environ["HF_HUB_CACHE"] = cache_path | |
| os.environ["HF_HOME"] = cache_path | |
| import gradio as gr | |
| import torch | |
| from diffusers import StableDiffusionXLPipeline, LCMScheduler | |
| # from scheduling_tcd import TCDScheduler | |
| torch.backends.cuda.matmul.allow_tf32 = True | |
| class timer: | |
| def __init__(self, method_name="timed process"): | |
| self.method = method_name | |
| def __enter__(self): | |
| self.start = time.time() | |
| print(f"{self.method} starts") | |
| def __exit__(self, exc_type, exc_val, exc_tb): | |
| end = time.time() | |
| print(f"{self.method} took {str(round(end - self.start, 2))}s") | |
| if not path.exists(cache_path): | |
| os.makedirs(cache_path, exist_ok=True) | |
| pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16) | |
| pipe.to(device="cuda", dtype=torch.bfloat16) | |
| unet_state = load_file(hf_hub_download("ByteDance/Hyper-SD", "Hyper-SDXL-1step-Unet.safetensors"), device="cuda") | |
| pipe.unet.load_state_dict(unet_state) | |
| pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing ="trailing") | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| num_images = gr.Slider(label="Number of Images", minimum=1, maximum=8, step=1, value=4, interactive=True) | |
| height = gr.Number(label="Image Height", value=1024, interactive=True) | |
| width = gr.Number(label="Image Width", value=1024, interactive=True) | |
| # steps = gr.Slider(label="Inference Steps", minimum=1, maximum=8, step=1, value=1, interactive=True) | |
| # eta = gr.Number(label="Eta (Corresponds to parameter eta (η) in the DDIM paper, i.e. 0.0 eqauls DDIM, 1.0 equals LCM)", value=1., interactive=True) | |
| prompt = gr.Text(label="Prompt", value="a photo of a cat", interactive=True) | |
| seed = gr.Number(label="Seed", value=3413, interactive=True) | |
| btn = gr.Button(value="run") | |
| with gr.Column(): | |
| output = gr.Gallery(height=1024) | |
| def process_image(num_images, height, width, prompt, seed): | |
| global pipe | |
| with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"): | |
| return pipe( | |
| prompt=[prompt]*num_images, | |
| generator=torch.Generator().manual_seed(int(seed)), | |
| num_inference_steps=1, | |
| guidance_scale=0., | |
| height=int(height), | |
| width=int(width), | |
| timesteps=[800] | |
| ).images | |
| reactive_controls = [num_images, height, width, prompt, seed] | |
| # for control in reactive_controls: | |
| # control.change(fn=process_image, inputs=reactive_controls, outputs=[output]) | |
| btn.click(process_image, inputs=reactive_controls, outputs=[output]) | |
| if __name__ == "__main__": | |
| demo.launch() |