renyuxi commited on
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2000056
1 Parent(s): c53774d

Create app.py

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  1. app.py +80 -0
app.py ADDED
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+ import argparse
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+ import os
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+ import time
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+ from os import path
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+
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+ cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
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+ os.environ["TRANSFORMERS_CACHE"] = cache_path
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+ os.environ["HF_HUB_CACHE"] = cache_path
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+ os.environ["HF_HOME"] = cache_path
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+
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+ import gradio as gr
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+ import torch
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+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
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+
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+ from scheduling_tcd import TCDScheduler
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+
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+ torch.backends.cuda.matmul.allow_tf32 = True
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+
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+ class timer:
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+ def __init__(self, method_name="timed process"):
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+ self.method = method_name
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+
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+ def __enter__(self):
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+ self.start = time.time()
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+ print(f"{self.method} starts")
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+
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+ def __exit__(self, exc_type, exc_val, exc_tb):
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+ end = time.time()
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+ print(f"{self.method} took {str(round(end - self.start, 2))}s")
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+
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+ if not path.exists(cache_path):
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+ os.makedirs(cache_path, exist_ok=True)
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+
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+ controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble", torch_dtype=torch.float16, use_safetensors=True)
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+ pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, safety_checker=None)
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+ pipe.to(device="cuda", dtype=torch.float16)
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+ pipe.load_lora_weights("ByteDance/Hyper-SD", weight_name="Hyper-SD15-1step-lora.safetensors", adapter_name="default")
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+ pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config, timestep_spacing ="trailing")
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+
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+ with gr.Blocks() as demo:
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+ with gr.Column():
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+ with gr.Row():
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+ with gr.Column():
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+ num_images = gr.Slider(label="Number of Images", minimum=1, maximum=8, step=1, value=4, interactive=True)
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+ steps = gr.Slider(label="Inference Steps", minimum=1, maximum=8, step=1, value=1, interactive=True)
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+ 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)
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+ controlnet_scale = gr.Number(label="ControlNet Conditioning Scale", value=1.0, interactive=True)
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+ prompt = gr.Text(label="Prompt", value="a photo of a cat", interactive=True)
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+ seed = gr.Number(label="Seed", value=3413, interactive=True)
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+ scribble = gr.Image(source="canvas", tool="color-sketch", shape=(512, 512), height=768, width=768, type="pil")
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+ btn = gr.Button(value="run")
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+ with gr.Column():
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+ output = gr.Gallery(height=768)
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+
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+ def process_image(steps, prompt, controlnet_scale, eta, seed, scribble, num_images):
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+ global pipe
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+ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.float16), timer("inference"):
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+ return pipe(
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+ prompt=[prompt]*num_images,
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+ image=[scribble]*num_images,
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+ generator=torch.Generator().manual_seed(int(seed)),
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+ num_inference_steps=steps,
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+ guidance_scale=0.,
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+ eta=eta,
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+ controlnet_conditioning_scale=controlnet_scale
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+ ).images
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+
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+ reactive_controls = [steps, prompt, controlnet_scale, eta, seed, scribble, num_images]
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+
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+ for control in reactive_controls:
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+ control.change(fn=process_image, inputs=reactive_controls, outputs=[output])
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+
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+ btn.click(process_image, inputs=reactive_controls, outputs=[output])
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+
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+ if __name__ == "__main__":
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+ # parser = argparse.ArgumentParser()
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+ # parser.add_argument("--port", default=7891, type=int)
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+ # args = parser.parse_args()
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+ # demo.launch(server_name="0.0.0.0", server_port=args.port)
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+ demo.launch()