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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 stable_diffusion_inpaint(
dict:str,
model_path:str,
prompt:str,
negative_prompt:str,
guidance_scale:int,
num_inference_step:int,
):
image = dict["image"].convert("RGB").resize((512, 512))
mask_image = dict["mask"].convert("RGB").resize((512, 512))
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.Blocks():
with gr.Row():
with gr.Column():
inpaint_image_file = gr.Image(
source='upload',
tool='sketch',
elem_id="image_upload",
type="pil",
label="Upload"
).style(height=400)
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')
with gr.Column():
output_image = gr.Gallery(label="Outputs")
inpaint_predict.click(
fn=stable_diffusion_inpaint,
inputs=[
inpaint_image_file,
inpaint_model_id,
inpaint_prompt,
inpaint_negative_prompt,
inpaint_guidance_scale,
inpaint_num_inference_step,
],
outputs=output_image
)
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