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from diffusers import DiffusionPipeline, DDIMScheduler
from PIL import Image
import imageio
import torch
import gradio as gr
stable_model_list = [
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-2",
"stabilityai/stable-diffusion-2-base",
"stabilityai/stable-diffusion-2-1",
"stabilityai/stable-diffusion-2-1-base"
]
stable_inpiant_model_list = [
"stabilityai/stable-diffusion-2-inpainting",
"runwayml/stable-diffusion-inpainting"
]
stable_prompt_list = [
"a photo of a man.",
"a photo of a girl."
]
stable_negative_prompt_list = [
"bad, ugly",
"deformed"
]
def resize(height,img):
baseheight = height
img = Image.open(img)
hpercent = (baseheight/float(img.size[1]))
wsize = int((float(img.size[0])*float(hpercent)))
img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS)
return img
def img_preprocces(source_img, prompt, negative_prompt):
imageio.imwrite("data.png", source_img["image"])
imageio.imwrite("data_mask.png", source_img["mask"])
src = resize(512, "data.png")
src.save("src.png")
mask = resize(512, "data_mask.png")
mask.save("mask.png")
return src, mask
def stable_diffusion_inpaint(
image_path:str,
model_path:str,
prompt:str,
negative_prompt:str,
guidance_scale:int,
num_inference_step:int,
):
image, mask_image = img_preprocces(image_path, prompt, negative_prompt)
pipe = DiffusionPipeline.from_pretrained(
model_path,
revision="fp16",
torch_dtype=torch.float16,
)
pipe.to('cuda')
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
output = pipe(
prompt = prompt,
image = image,
mask_image=mask_image,
negative_prompt = negative_prompt,
num_inference_steps = num_inference_step,
guidance_scale = guidance_scale,
).images
return output[0]
def stable_diffusion_inpaint_app():
with gr.Tab('Inpaint'):
inpaint_image_file = gr.Image(
source="upload",
type="numpy",
tool="sketch",
elem_id="source_container"
)
inpaint_model_id = gr.Dropdown(
choices=stable_inpiant_model_list,
value=stable_inpiant_model_list[0],
label='Inpaint Model Id'
)
inpaint_prompt = gr.Textbox(
lines=1,
value=stable_prompt_list[0],
label='Prompt'
)
inpaint_negative_prompt = gr.Textbox(
lines=1,
value=stable_negative_prompt_list[0],
label='Negative Prompt'
)
with gr.Accordion("Advanced Options", open=False):
inpaint_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label='Guidance Scale'
)
inpaint_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label='Num Inference Step'
)
inpaint_predict = gr.Button(value='Generator')
variables = {
"image_path": inpaint_image_file,
"model_path": inpaint_model_id,
"prompt": inpaint_prompt,
"negative_prompt": inpaint_negative_prompt,
"guidance_scale": inpaint_guidance_scale,
"num_inference_step": inpaint_num_inference_step,
"predict": inpaint_predict
}
return variables
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