| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| from utils import write_video, dummy, preprocess_image, preprocess_mask_image | |
| from PIL import Image | |
| import gradio as gr | |
| import torch | |
| import os | |
| os.environ["CUDA_VISIBLE_DEVICES"]="0" | |
| orig_prompt = "Ancient underground architectural ruins of Hong Kong in a flooded apocalypse landscape of dead skyscrapers" | |
| orig_negative_prompt = "lurry, bad art, blurred, text, watermark" | |
| model_list = ["stabilityai/stable-diffusion-2-inpainting", "runwayml/stable-diffusion-inpainting"] | |
| def stable_diffusion_zoom_out( | |
| repo_id, | |
| original_prompt, | |
| negative_prompt, | |
| step_size, | |
| num_frames, | |
| fps, | |
| num_inference_steps | |
| ): | |
| pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16) | |
| pipe.set_use_memory_efficient_attention_xformers(True) | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| pipe = pipe.to("cuda") | |
| pipe.safety_checker = dummy | |
| new_image = Image.new(mode="RGBA", size=(512,512)) | |
| current_image, mask_image = preprocess_mask_image(new_image) | |
| current_image = pipe(prompt=[original_prompt], negative_prompt=[negative_prompt], image=current_image, mask_image=mask_image, num_inference_steps=num_inference_steps).images[0] | |
| all_frames = [] | |
| all_frames.append(current_image) | |
| for i in range(num_frames): | |
| prev_image = preprocess_image(current_image, step_size, 512) | |
| current_image = prev_image | |
| current_image, mask_image = preprocess_mask_image(current_image) | |
| current_image = pipe(prompt=[original_prompt], negative_prompt=[negative_prompt], image=current_image, mask_image=mask_image, num_inference_steps=num_inference_steps).images[0] | |
| current_image.paste(prev_image, mask=prev_image) | |
| all_frames.append(current_image) | |
| save_path = "output.mp4" | |
| write_video(save_path, all_frames, fps=fps) | |
| return save_path | |
| inputs = [ | |
| gr.Dropdown(model_list, value=model_list[0], label="Model"), | |
| 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=120, default=25, step=5, label="Steps"), | |
| gr.inputs.Slider(minimum=1, maximum=100, default=10, step=5, label="Frames"), | |
| gr.inputs.Slider(minimum=1, maximum=100, default=16, step=1, label="FPS"), | |
| gr.inputs.Slider(minimum=1, maximum=100, default=15, step=1, label="Inference Steps") | |
| ] | |
| output = gr.outputs.Video() | |
| examples = [ | |
| ["stabilityai/stable-diffusion-2-inpainting", orig_prompt, orig_negative_prompt, 25, 10, 16, 15], | |
| ] | |
| 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, | |
| description=description, | |
| inputs=inputs, | |
| outputs=output, | |
| title=title, | |
| theme='huggingface', | |
| examples=examples, | |
| cache_examples=True | |
| ) | |
| demo_app.launch(debug=True, enable_queue=True) | |