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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
best_model_list = [
"stabilityai/stable-diffusion-2-inpainting",
"runwayml/stable-diffusion-v1-5",
"CompVis/stable-diffusion-v1-4",
"prompthero/openjourney",
"dreamlike-art/dreamlike-photoreal-2.0",
"dreamlike-art/dreamlike-diffusion-1.0",
]
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=32,
num_frames=10,
image_size=512,
fps=16
):
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=(image_size,image_size))
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((image_size-2*steps,image_size-2*steps))
prev_image = prev_image.convert("RGBA")
prev_image = np.array(prev_image)
next_image[:, :, 3] = 1
next_image[steps:image_size-steps,steps:image_size-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.Dropdown(choices=best_model_list,default="stabilityai/stable-diffusion-2-inpainting", label="Model"),
gr.inputs.Textbox(lines=1, 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=128, maximum=1024, default=512, step=256, label="Image Size"),
gr.inputs.Slider(minimum=1, maximum=100, default=16, step=1, label="FPS")
]
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)
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