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| import spaces | |
| import argparse | |
| import os | |
| import time | |
| from os import path | |
| from PIL import ImageOps | |
| 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 StableDiffusionControlNetPipeline, ControlNetModel | |
| from scheduling_tcd import TCDScheduler | |
| torch.backends.cuda.matmul.allow_tf32 = True | |
| js_func = """ | |
| function refresh() { | |
| const url = new URL(window.location); | |
| if (url.searchParams.get('__theme') !== 'dark') { | |
| url.searchParams.set('__theme', 'dark'); | |
| window.location.href = url.href; | |
| } | |
| } | |
| """ | |
| 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) | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble", torch_dtype=torch.float16, use_safetensors=True) | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16") | |
| pipe.load_lora_weights("ByteDance/Hyper-SD", weight_name="Hyper-SD15-1step-lora.safetensors", adapter_name="default") | |
| pipe.to("cuda") | |
| pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config, timestep_spacing ="trailing") | |
| with gr.Blocks(js=js_func) as demo: | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| # scribble = gr.Image(source="canvas", tool="color-sketch", shape=(512, 512), height=768, width=768, type="pil") | |
| scribble = gr.ImageEditor(type="pil", image_mode="L", crop_size=(512, 512), sources=(), brush=gr.Brush(color_mode="fixed", colors=["#FFFFFF"]), canvas_size=(1024, 1024)) | |
| # scribble_out = gr.Image(height=384, width=384) | |
| num_images = gr.Slider(label="Number of Images", minimum=1, maximum=8, step=1, value=4, interactive=True) | |
| steps = gr.Slider(label="Inference Steps", minimum=1, maximum=8, step=1, value=1, interactive=True) | |
| prompt = gr.Text(label="Prompt", value="a photo of a cat", 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) | |
| controlnet_scale = gr.Number(label="ControlNet Conditioning Scale", value=1.0, interactive=True) | |
| seed = gr.Number(label="Seed", value=3413, interactive=True) | |
| btn = gr.Button(value="run") | |
| with gr.Column(): | |
| output = gr.Gallery(height=768, format="png") | |
| # output = gr.Image() | |
| def process_image(steps, prompt, controlnet_scale, eta, seed, scribble, num_images): | |
| global pipe | |
| if scribble: | |
| with torch.inference_mode(), torch.autocast("cuda", dtype=torch.float16), timer("inference"): | |
| result = pipe( | |
| prompt=[prompt]*num_images, | |
| image=[ImageOps.invert(scribble['composite'])]*num_images, | |
| # image=[scribble['composite']]*num_images, | |
| generator=torch.Generator().manual_seed(int(seed)), | |
| num_inference_steps=steps, | |
| guidance_scale=0., | |
| eta=eta, | |
| controlnet_conditioning_scale=float(controlnet_scale), | |
| ).images | |
| # result[0].save("test.jpg") | |
| # print(result[0]) | |
| return result | |
| else: | |
| return None | |
| reactive_controls = [steps, prompt, controlnet_scale, eta, seed, scribble, num_images] | |
| for control in reactive_controls: | |
| if reactive_controls[-2] is not None: | |
| control.change(fn=process_image, inputs=reactive_controls, outputs=[output, ]) | |
| btn.click(process_image, inputs=reactive_controls, outputs=[output, ]) | |
| if __name__ == "__main__": | |
| demo.launch() |