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from helpers import *
from diffusers import StableDiffusionInpaintPipeline, EulerAncestralDiscreteScheduler
from PIL import Image
import gradio as gr
import numpy as np
import torch
import os
import time
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
inpaint_model_list = [
"stabilityai/stable-diffusion-2-inpainting",
"runwayml/stable-diffusion-inpainting",
"parlance/dreamlike-diffusion-1.0-inpainting",
"ghunkins/stable-diffusion-liberty-inpainting",
"ImNoOne/f222-inpainting-diffusers"
]
default_prompt = "A psychedelic jungle with trees that have glowing, fractal-like patterns, Simon stalenhag poster 1920s style, street level view, hyper futuristic, 8k resolution, hyper realistic"
default_negative_prompt = "frames, borderline, text, charachter, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur"
def zoom(
model_id,
prompts_array,
negative_prompt,
num_outpainting_steps,
guidance_scale,
num_inference_steps,
custom_init_image
):
prompts = {}
for x in prompts_array:
try:
key = int(x[0])
value = str(x[1])
prompts[key] = value
except ValueError:
pass
pipe = StableDiffusionInpaintPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
pipe.scheduler.config)
pipe = pipe.to("cuda")
def no_check(images, **kwargs):
return images, False
pipe.safety_checker = no_check
pipe.enable_attention_slicing()
g_cuda = torch.Generator(device='cuda')
height = 512
width = height
current_image = Image.new(mode="RGBA", size=(height, width))
mask_image = np.array(current_image)[:, :, 3]
mask_image = Image.fromarray(255-mask_image).convert("RGB")
current_image = current_image.convert("RGB")
if (custom_init_image):
current_image = custom_init_image.resize(
(width, height), resample=Image.LANCZOS)
else:
init_images = pipe(prompt=prompts[min(k for k in prompts.keys() if k >= 0)],
negative_prompt=negative_prompt,
image=current_image,
guidance_scale=guidance_scale,
height=height,
width=width,
mask_image=mask_image,
num_inference_steps=num_inference_steps)[0]
current_image = init_images[0]
mask_width = 128
num_interpol_frames = 30
all_frames = []
all_frames.append(current_image)
for i in range(num_outpainting_steps):
print('Outpaint step: ' + str(i+1) +
' / ' + str(num_outpainting_steps))
prev_image_fix = current_image
prev_image = shrink_and_paste_on_blank(current_image, mask_width)
current_image = prev_image
# create mask (black image with white mask_width width edges)
mask_image = np.array(current_image)[:, :, 3]
mask_image = Image.fromarray(255-mask_image).convert("RGB")
# inpainting step
current_image = current_image.convert("RGB")
images = pipe(prompt=prompts[max(k for k in prompts.keys() if k <= i)],
negative_prompt=negative_prompt,
image=current_image,
guidance_scale=guidance_scale,
height=height,
width=width,
# generator = g_cuda.manual_seed(seed),
mask_image=mask_image,
num_inference_steps=num_inference_steps)[0]
current_image = images[0]
current_image.paste(prev_image, mask=prev_image)
# interpolation steps bewteen 2 inpainted images (=sequential zoom and crop)
for j in range(num_interpol_frames - 1):
interpol_image = current_image
interpol_width = round(
(1 - (1-2*mask_width/height)**(1-(j+1)/num_interpol_frames))*height/2
)
interpol_image = interpol_image.crop((interpol_width,
interpol_width,
width - interpol_width,
height - interpol_width))
interpol_image = interpol_image.resize((height, width))
# paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
interpol_width2 = round(
(1 - (height-2*mask_width) / (height-2*interpol_width)) / 2*height
)
prev_image_fix_crop = shrink_and_paste_on_blank(
prev_image_fix, interpol_width2)
interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
all_frames.append(interpol_image)
all_frames.append(current_image)
# interpol_image.show()
video_file_name = "infinite_zoom_" + str(time.time())
fps = 30
save_path = video_file_name + ".mp4"
start_frame_dupe_amount = 15
last_frame_dupe_amount = 15
write_video(save_path, all_frames, fps, False,
start_frame_dupe_amount, last_frame_dupe_amount)
return save_path
def zoom_app():
with gr.Blocks():
with gr.Row():
with gr.Column():
outpaint_prompts = gr.Dataframe(
type="array",
headers=["outpaint steps", "prompt"],
datatype=["number", "str"],
row_count=1,
col_count=(2, "fixed"),
value=[[0, default_prompt]],
wrap=True
)
outpaint_negative_prompt = gr.Textbox(
lines=1,
value=default_negative_prompt,
label='Negative Prompt'
)
outpaint_steps = gr.Slider(
minimum=5,
maximum=25,
step=1,
value=12,
label='Total Outpaint Steps'
)
with gr.Accordion("Advanced Options", open=False):
model_id = gr.Dropdown(
choices=inpaint_model_list,
value=inpaint_model_list[0],
label='Pre-trained Model ID'
)
guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7,
label='Guidance Scale'
)
sampling_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label='Sampling Steps for each outpaint'
)
init_image = gr.Image(type="pil")
generate_btn = gr.Button(value='Generate video')
with gr.Column():
output_image = gr.Video(label='Output', format="mp4").style(
width=512, height=512)
generate_btn.click(
fn=zoom,
inputs=[
model_id,
outpaint_prompts,
outpaint_negative_prompt,
outpaint_steps,
guidance_scale,
sampling_step,
init_image
],
outputs=output_image,
)
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