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import gradio as gr
import numpy as np
import torch
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
from segment_anything import SamPredictor, sam_model_registry, SamAutomaticMaskGenerator
from diffusers import ControlNetModel
from diffusers import UniPCMultistepScheduler
from controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
import colorsys
sam_checkpoint = "weights/sam_vit_h_4b8939.pth"
model_type = "vit_h"
device = "cuda"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)
predictor = SamPredictor(sam)
mask_generator = SamAutomaticMaskGenerator(sam)
# pipe = StableDiffusionInpaintPipeline.from_pretrained(
# "stabilityai/stable-diffusion-2-inpainting",
# torch_dtype=torch.float16,
# )
# pipe = pipe.to("cuda")
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-seg",
torch_dtype=torch.float16,
)
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
controlnet=controlnet,
torch_dtype=torch.float16,
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.enable_xformers_memory_efficient_attention()
with gr.Blocks() as demo:
selected_pixels = gr.State([])
with gr.Row():
input_img = gr.Image(label="Input")
mask_img = gr.Image(label="Mask")
seg_img = gr.Image(label="Segmentation")
output_img = gr.Image(label="Output")
with gr.Row():
prompt_text = gr.Textbox(lines=1, label="Prompt")
negative_prompt_text = gr.Textbox(lines=1, label="Negative Prompt")
is_background = gr.Checkbox(label="Background")
with gr.Row():
submit = gr.Button("Submit")
clear = gr.Button("Clear")
def generate_mask(image, bg, sel_pix, evt: gr.SelectData):
sel_pix.append(evt.index)
predictor.set_image(image)
input_point = np.array(sel_pix)
input_label = np.ones(input_point.shape[0])
mask, _, _ = predictor.predict(
point_coords=input_point,
point_labels=input_label,
multimask_output=False,
)
if bg:
mask = np.logical_not(mask)
mask = Image.fromarray(mask[0, :, :])
segs = mask_generator.generate(image)
boolean_masks = [s["segmentation"] for s in segs]
finseg = np.zeros((boolean_masks[0].shape[0], boolean_masks[0].shape[1], 3), dtype=np.uint8)
# Loop over the boolean masks and assign a unique color to each class
for class_id, boolean_mask in enumerate(boolean_masks):
hue = class_id * 1.0 / len(boolean_masks)
rgb = tuple(int(i * 255) for i in colorsys.hsv_to_rgb(hue, 1, 1))
rgb_mask = np.zeros((boolean_mask.shape[0], boolean_mask.shape[1], 3), dtype=np.uint8)
rgb_mask[:, :, 0] = boolean_mask * rgb[0]
rgb_mask[:, :, 1] = boolean_mask * rgb[1]
rgb_mask[:, :, 2] = boolean_mask * rgb[2]
finseg += rgb_mask
return mask, finseg
def inpaint(image, mask, seg_img, prompt, negative_prompt):
image = Image.fromarray(image)
mask = Image.fromarray(mask)
seg_img = Image.fromarray(seg_img)
image = image.resize((512, 512))
mask = mask.resize((512, 512))
seg_img = seg_img.resize((512, 512))
output = pipe(prompt, image, mask, seg_img, negative_prompt=negative_prompt).images[0]
return output
def _clear(sel_pix, img, mask, seg, out, prompt, neg_prompt, bg):
sel_pix = []
img = None
mask = None
seg = None
out = None
prompt = ""
neg_prompt = ""
bg = False
return img, mask, seg, out, prompt, neg_prompt, bg
input_img.select(
generate_mask,
[input_img, is_background, selected_pixels],
[mask_img, seg_img],
)
submit.click(
inpaint,
inputs=[input_img, mask_img, seg_img, prompt_text, negative_prompt_text],
outputs=[output_img],
)
clear.click(
_clear,
inputs=[
selected_pixels,
input_img,
mask_img,
seg_img,
output_img,
prompt_text,
negative_prompt_text,
is_background,
],
outputs=[
input_img,
mask_img,
seg_img,
output_img,
prompt_text,
negative_prompt_text,
is_background,
],
)
if __name__ == "__main__":
demo.queue(concurrency_count=50).launch()
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