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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from inpaint_zoom.zoom_out_utils import preprocess_image, preprocess_mask_image, write_video, dummy
from PIL import Image
import gradio as gr
import torch
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
stable_paint_model_list = [
"stabilityai/stable-diffusion-2-inpainting",
"runwayml/stable-diffusion-inpainting"
]
stable_paint_prompt_list = [
"Ancient underground architectural ruins of Hong Kong in a flooded apocalypse landscape of dead skyscrapers",
"A beautiful landscape of a mountain range with a lake in the foreground",
]
stable_paint_negative_prompt_list = [
"lurry, bad art, blurred, text, watermark",
]
def stable_diffusion_zoom_out(
model_id,
original_prompt,
negative_prompt,
guidance_scale,
num_inference_steps,
step_size,
num_frames,
fps,
):
pipe = DiffusionPipeline.from_pretrained(model_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,
guidance_scale=guidance_scale
).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
def stable_diffusion_text2img_app():
with gr.Blocks():
with gr.Row():
with gr.Column():
text2image_out_model_path = gr.Dropdown(
choices=stable_paint_model_list,
value=stable_paint_model_list[0],
label='Text-Image Model Id'
)
text2image_out_prompt = gr.Textbox(
lines=1,
value=stable_paint_prompt_list[0],
label='Prompt'
)
text2image_out_negative_prompt = gr.Textbox(
lines=1,
value=stable_paint_negative_prompt_list[0],
label='Negative Prompt'
)
with gr.Accordion("Advanced Options", open=False):
text2image_out_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label='Guidance Scale'
)
text2image_out_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label='Num Inference Step'
)
text2image_out_step_size = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=10,
label='Step Size'
)
text2image_out_num_frames = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=10,
label='Frames'
)
text2image_out_fps = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=30,
label='FPS'
)
text2image_out_predict = gr.Button(value='Generator')
with gr.Column():
output_image = gr.Video(label='Output')
text2image_out_predict.click(
fn=stable_diffusion_zoom_out,
inputs=[
text2image_out_model_path,
text2image_out_prompt,
text2image_out_negative_prompt,
text2image_out_guidance_scale,
text2image_out_num_inference_step,
text2image_out_step_size,
text2image_out_num_frames,
text2image_out_fps
],
outputs=output_image
)
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