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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler |
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from utils import write_video, dummy |
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from PIL import Image |
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import numpy as np |
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import os |
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os.environ["CUDA_VISIBLE_DEVICES"]="0" |
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import torch |
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import gradio as gr |
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best_model_list = [ |
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"stabilityai/stable-diffusion-2-inpainting", |
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"runwayml/stable-diffusion-v1-5", |
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"CompVis/stable-diffusion-v1-4", |
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"prompthero/openjourney", |
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"dreamlike-art/dreamlike-photoreal-2.0", |
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"dreamlike-art/dreamlike-diffusion-1.0", |
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] |
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orig_prompt = "Create a relaxing atmosphere with the use of plants and other natural elements. Such as a hanging terrarium or a wall-mounted planter. Include plenty of storage options to keep the space organized and clutter-free. Consider adding a vanity with double sinks and plenty of drawers and cabinets. As well as a wall mounted medicine and towel storage." |
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orig_negative_prompt = "lurry, bad art, blurred, text, watermark" |
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def stable_diffusion_zoom_out( |
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repo_id, |
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original_prompt, |
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negative_prompt, |
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steps=32, |
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num_frames=10, |
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image_size=512, |
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fps=16 |
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): |
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pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16") |
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pipe.set_use_memory_efficient_attention_xformers(True) |
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) |
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pipe = pipe.to("cuda") |
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pipe.safety_checker = dummy |
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current_image = Image.new(mode="RGBA", size=(image_size,image_size)) |
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mask_image = np.array(current_image)[:,:,3] |
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mask_image = Image.fromarray(255-mask_image).convert("RGB") |
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current_image = current_image.convert("RGB") |
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num_images = 1 |
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prompt = [original_prompt] * num_images |
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negative_prompt = [negative_prompt] * num_images |
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images = pipe(prompt=prompt, negative_prompt=negative_prompt, image=current_image, mask_image=mask_image, num_inference_steps=25)[0] |
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current_image = images[0] |
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all_frames = [] |
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all_frames.append(current_image) |
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for i in range(num_frames): |
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next_image = np.array(current_image.convert("RGBA"))*0 |
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prev_image = current_image.resize((image_size-2*steps,image_size-2*steps)) |
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prev_image = prev_image.convert("RGBA") |
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prev_image = np.array(prev_image) |
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next_image[:, :, 3] = 1 |
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next_image[steps:image_size-steps,steps:image_size-steps,:] = prev_image |
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prev_image = Image.fromarray(next_image) |
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current_image = prev_image |
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mask_image = np.array(current_image)[:,:,3] |
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mask_image = Image.fromarray(255-mask_image).convert("RGB") |
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current_image = current_image.convert("RGB") |
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images = pipe(prompt=prompt, negative_prompt=negative_prompt, image=current_image, mask_image=mask_image, num_inference_steps=25)[0] |
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current_image = images[0] |
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current_image.paste(prev_image, mask=prev_image) |
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all_frames.append(current_image) |
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save_path = "infinite_zoom_out.mp4" |
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write_video(save_path, all_frames, fps=fps) |
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return save_path |
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inputs = [ |
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gr.Dropdown(choices=best_model_list,default="stabilityai/stable-diffusion-2-inpainting", label="Model"), |
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gr.inputs.Textbox(lines=1, default=orig_prompt, label="Prompt"), |
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gr.inputs.Textbox(lines=1, default=orig_negative_prompt, label="Negative Prompt"), |
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gr.inputs.Slider(minimum=1, maximum=64, default=32, label="Steps"), |
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gr.inputs.Slider(minimum=1, maximum=500, default=10, step=10, label="Frames"), |
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gr.inputs.Slider(minimum=128, maximum=1024, default=512, step=256, label="Image Size"), |
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gr.inputs.Slider(minimum=1, maximum=100, default=16, step=1, label="FPS") |
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] |
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output = gr.outputs.Video() |
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title = "Stable Diffusion Infinite Zoom Out" |
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demo_app = gr.Interface( |
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fn=stable_diffusion_zoom_out, |
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inputs=inputs, |
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outputs=output, |
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title=title, |
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theme='huggingface', |
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) |
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demo_app.launch(debug=True, enable_queue=True) |
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