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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 | |
| 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." | |
| orig_negative_prompt = "lurry, bad art, blurred, text, watermark" | |
| def stable_diffusion_zoom_out( | |
| repo_id, | |
| original_prompt, | |
| negative_prompt, | |
| steps, | |
| num_frames, | |
| fps | |
| ): | |
| 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=fps) | |
| return save_path | |
| inputs = [ | |
| gr.inputs.Textbox(lines=1, default="stabilityai/stable-diffusion-2-inpainting", label="Model ID"), | |
| gr.inputs.Textbox(lines=5, default=orig_prompt, label="Prompt"), | |
| gr.inputs.Textbox(lines=1, default=orig_negative_prompt, label="Negative Prompt"), | |
| gr.inputs.Slider(minimum=1, maximum=64, default=32, label="Steps"), | |
| gr.inputs.Slider(minimum=1, maximum=500, default=10, step=10, label="Frames"), | |
| gr.inputs.Slider(minimum=1, maximum=100, default=16, step=1, label="FPS") | |
| ] | |
| output = gr.outputs.Video() | |
| examples = [ | |
| ["stabilityai/stable-diffusion-2-inpainting", orig_prompt, orig_negative_prompt, 32, 50, 16] | |
| ] | |
| title = "Stable Diffusion Infinite Zoom Out" | |
| description = """ | |
| <p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. | |
| <br/> | |
| <a href="https://huggingface.co/spaces/kadirnar/stable-diffusion-2-infinite-zoom-out?duplicate=true"> | |
| <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> | |
| <p/>""" | |
| demo_app = gr.Interface( | |
| fn=stable_diffusion_zoom_out, | |
| inputs=inputs, | |
| outputs=output, | |
| title=title, | |
| description=description, | |
| theme='huggingface', | |
| examples=examples, | |
| cache_examples=True | |
| ) | |
| demo_app.launch(debug=True, enable_queue=True) | |