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import spaces |
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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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from PIL import ImageOps |
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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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import gradio as gr |
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import torch |
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel |
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from scheduling_tcd import TCDScheduler |
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torch.backends.cuda.matmul.allow_tf32 = True |
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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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def __enter__(self): |
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self.start = time.time() |
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print(f"{self.method} starts") |
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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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if not path.exists(cache_path): |
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os.makedirs(cache_path, exist_ok=True) |
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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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variant="fp16") |
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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.to("cuda") |
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pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config, timestep_spacing ="trailing") |
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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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scribble = gr.ImageEditor(type="pil", image_mode="L", crop_size=(512, 512), sources=(), brush=gr.Brush(color_mode="fixed", colors=["#FFFFFF"])) |
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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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prompt = gr.Text(label="Prompt", value="a photo of a cat", 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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seed = gr.Number(label="Seed", value=3413, interactive=True) |
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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, format="png") |
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@spaces.GPU |
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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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if scribble: |
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with torch.inference_mode(), torch.autocast("cuda", dtype=torch.float16), timer("inference"): |
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result = pipe( |
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prompt=[prompt]*num_images, |
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image=[ImageOps.invert(scribble['composite'])]*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=float(controlnet_scale), |
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).images |
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return result |
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else: |
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return None |
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reactive_controls = [steps, prompt, controlnet_scale, eta, seed, scribble, num_images] |
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for control in reactive_controls: |
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if reactive_controls[-2] is not None: |
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control.change(fn=process_image, inputs=reactive_controls, outputs=[output, ]) |
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btn.click(process_image, inputs=reactive_controls, outputs=[output, ]) |
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if __name__ == "__main__": |
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demo.launch() |