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import argparse
import os
import time
from os import path
from safetensors.torch import load_file
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, safety_checker=None)
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(width=1024, height=768)
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() |