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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) | |