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| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| from utils import write_video, dummy | |
| from PIL import Image | |
| import numpy as np | |
| import os | |
| os.environ["CUDA_VISIBLE_DEVICES"]="0" | |
| import torch | |
| import gradio as gr | |
| def stable_diffusion_zoom_out( | |
| repo_id="stabilityai/stable-diffusion-2-inpainting", | |
| original_prompt="a dog", | |
| negative_prompt="a cat", | |
| steps=32, | |
| num_frames=10, | |
| ): | |
| pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16") | |
| pipe.set_use_memory_efficient_attention_xformers(True) | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| pipe = pipe.to("cuda") | |
| pipe.safety_checker = dummy | |
| current_image = Image.new(mode="RGBA", size=(512, 512)) | |
| mask_image = np.array(current_image)[:,:,3] # assume image has alpha mask (use .mode to check for "RGBA") | |
| mask_image = Image.fromarray(255-mask_image).convert("RGB") | |
| current_image = current_image.convert("RGB") | |
| num_images = 1 | |
| prompt = [original_prompt] * num_images | |
| negative_prompt = [negative_prompt] * num_images | |
| images = pipe(prompt=prompt, negative_prompt=negative_prompt, image=current_image, mask_image=mask_image, num_inference_steps=25)[0] | |
| current_image = images[0] | |
| all_frames = [] | |
| all_frames.append(current_image) | |
| for i in range(num_frames): | |
| next_image = np.array(current_image.convert("RGBA"))*0 | |
| prev_image = current_image.resize((512-2*steps,512-2*steps)) | |
| prev_image = prev_image.convert("RGBA") | |
| prev_image = np.array(prev_image) | |
| next_image[:, :, 3] = 1 | |
| next_image[steps:512-steps,steps:512-steps,:] = prev_image | |
| prev_image = Image.fromarray(next_image) | |
| current_image = prev_image | |
| mask_image = np.array(current_image)[:,:,3] # assume image has alpha mask (use .mode to check for "RGBA") | |
| mask_image = Image.fromarray(255-mask_image).convert("RGB") | |
| current_image = current_image.convert("RGB") | |
| images = pipe(prompt=prompt, negative_prompt=negative_prompt, image=current_image, mask_image=mask_image, num_inference_steps=25)[0] | |
| current_image = images[0] | |
| current_image.paste(prev_image, mask=prev_image) | |
| all_frames.append(current_image) | |
| save_path = "infinite_zoom_out.mp4" | |
| write_video(save_path, all_frames, fps=16) | |
| return save_path | |
| inputs = [ | |
| gr.Dropdown(["stabilityai/stable-diffusion-2-inpainting"], label="Model"), | |
| gr.inputs.Textbox(lines=1, default="a dog", label="Prompt"), | |
| gr.inputs.Textbox(lines=1, default="a cat", label="Negative Prompt"), | |
| gr.inputs.Slider(minimum=1, maximum=64, default=32, label="Steps"), | |
| gr.inputs.Slider(minimum=1, maximum=100, default=10, label="Frames"), | |
| ] | |
| output = gr.outputs.Video() | |
| title = "Stable Diffusion Infinite Zoom Out" | |
| demo_app = gr.Interface( | |
| fn=stable_diffusion_zoom_out, | |
| inputs=inputs, | |
| outputs=output, | |
| title=title, | |
| theme='huggingface', | |
| ) | |
| demo_app.launch(debug=True, enable_queue=True) | |