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import gradio as gr
import numpy as np
import torch
from diffusers import UniPCMultistepScheduler
from PIL import Image
from diffusion_webui.controlnet_inpaint.canny_inpaint import controlnet_canny
from diffusion_webui.controlnet_inpaint.pipeline_stable_diffusion_controlnet_inpaint import (
StableDiffusionControlNetInpaintPipeline,
)
stable_inpaint_model_list = [
"runwayml/stable-diffusion-inpainting",
"stabilityai/stable-diffusion-2-inpainting",
]
controlnet_model_list = [
"lllyasviel/sd-controlnet-canny",
]
prompt_list = [
"a red panda sitting on a bench",
]
negative_prompt_list = [
"bad, ugly",
]
def load_img(image_path: str):
image = Image.open(image_path)
image = np.array(image)
image = Image.fromarray(image)
return image
def stable_diffusion_inpiant_controlnet_canny(
dict_image: str,
stable_model_path: str,
controlnet_model_path: str,
prompt: str,
negative_prompt: str,
controlnet_conditioning_scale: str,
guidance_scale: int,
num_inference_steps: int,
):
normal_image = dict_image["image"].convert("RGB").resize((512, 512))
mask_image = dict_image["mask"].convert("RGB").resize((512, 512))
controlnet, control_image = controlnet_canny(
dict_image=dict_image,
controlnet_model_path=controlnet_model_path,
)
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
pretrained_model_name_or_path=stable_model_path,
controlnet=controlnet,
torch_dtype=torch.float16,
)
pipe.to("cuda")
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
generator = torch.manual_seed(0)
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
generator=generator,
image=normal_image,
control_image=control_image,
controlnet_conditioning_scale=controlnet_conditioning_scale,
guidance_scale=guidance_scale,
mask_image=mask_image,
).images
return output[0]
def stable_diffusion_inpiant_controlnet_canny_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",
)
inpaint_model_id = gr.Dropdown(
choices=stable_inpaint_model_list,
value=stable_inpaint_model_list[0],
label="Inpaint Model Id",
)
inpaint_controlnet_model_id = gr.Dropdown(
choices=controlnet_model_list,
value=controlnet_model_list[0],
label="ControlNet Model Id",
)
inpaint_prompt = gr.Textbox(
lines=1, value=prompt_list[0], label="Prompt"
)
inpaint_negative_prompt = gr.Textbox(
lines=1,
value=negative_prompt_list[0],
label="Negative Prompt",
)
with gr.Accordion("Advanced Options", open=False):
controlnet_conditioning_scale = gr.Slider(
minimum=0.1,
maximum=1,
step=0.1,
value=0.5,
label="ControlNet Conditioning Scale",
)
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.Image(label="Outputs")
inpaint_predict.click(
fn=stable_diffusion_inpiant_controlnet_canny,
inputs=[
inpaint_image_file,
inpaint_model_id,
inpaint_controlnet_model_id,
inpaint_prompt,
inpaint_negative_prompt,
controlnet_conditioning_scale,
inpaint_guidance_scale,
inpaint_num_inference_step,
],
outputs=output_image,
)
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