import gradio as gr
import numpy as np
import torch
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
from PIL import Image
from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
from diffusion_webui.utils.model_list import stable_model_list
from diffusion_webui.utils.scheduler_list import (
SCHEDULER_LIST,
get_scheduler_list,
)
def ade_palette():
"""ADE20K palette that maps each class to RGB values."""
return [
[120, 120, 120],
[180, 120, 120],
[6, 230, 230],
[80, 50, 50],
[4, 200, 3],
[120, 120, 80],
[140, 140, 140],
[204, 5, 255],
[230, 230, 230],
[4, 250, 7],
[224, 5, 255],
[235, 255, 7],
[150, 5, 61],
[120, 120, 70],
[8, 255, 51],
[255, 6, 82],
[143, 255, 140],
[204, 255, 4],
[255, 51, 7],
[204, 70, 3],
[0, 102, 200],
[61, 230, 250],
[255, 6, 51],
[11, 102, 255],
[255, 7, 71],
[255, 9, 224],
[9, 7, 230],
[220, 220, 220],
[255, 9, 92],
[112, 9, 255],
[8, 255, 214],
[7, 255, 224],
[255, 184, 6],
[10, 255, 71],
[255, 41, 10],
[7, 255, 255],
[224, 255, 8],
[102, 8, 255],
[255, 61, 6],
[255, 194, 7],
[255, 122, 8],
[0, 255, 20],
[255, 8, 41],
[255, 5, 153],
[6, 51, 255],
[235, 12, 255],
[160, 150, 20],
[0, 163, 255],
[140, 140, 140],
[250, 10, 15],
[20, 255, 0],
[31, 255, 0],
[255, 31, 0],
[255, 224, 0],
[153, 255, 0],
[0, 0, 255],
[255, 71, 0],
[0, 235, 255],
[0, 173, 255],
[31, 0, 255],
[11, 200, 200],
[255, 82, 0],
[0, 255, 245],
[0, 61, 255],
[0, 255, 112],
[0, 255, 133],
[255, 0, 0],
[255, 163, 0],
[255, 102, 0],
[194, 255, 0],
[0, 143, 255],
[51, 255, 0],
[0, 82, 255],
[0, 255, 41],
[0, 255, 173],
[10, 0, 255],
[173, 255, 0],
[0, 255, 153],
[255, 92, 0],
[255, 0, 255],
[255, 0, 245],
[255, 0, 102],
[255, 173, 0],
[255, 0, 20],
[255, 184, 184],
[0, 31, 255],
[0, 255, 61],
[0, 71, 255],
[255, 0, 204],
[0, 255, 194],
[0, 255, 82],
[0, 10, 255],
[0, 112, 255],
[51, 0, 255],
[0, 194, 255],
[0, 122, 255],
[0, 255, 163],
[255, 153, 0],
[0, 255, 10],
[255, 112, 0],
[143, 255, 0],
[82, 0, 255],
[163, 255, 0],
[255, 235, 0],
[8, 184, 170],
[133, 0, 255],
[0, 255, 92],
[184, 0, 255],
[255, 0, 31],
[0, 184, 255],
[0, 214, 255],
[255, 0, 112],
[92, 255, 0],
[0, 224, 255],
[112, 224, 255],
[70, 184, 160],
[163, 0, 255],
[153, 0, 255],
[71, 255, 0],
[255, 0, 163],
[255, 204, 0],
[255, 0, 143],
[0, 255, 235],
[133, 255, 0],
[255, 0, 235],
[245, 0, 255],
[255, 0, 122],
[255, 245, 0],
[10, 190, 212],
[214, 255, 0],
[0, 204, 255],
[20, 0, 255],
[255, 255, 0],
[0, 153, 255],
[0, 41, 255],
[0, 255, 204],
[41, 0, 255],
[41, 255, 0],
[173, 0, 255],
[0, 245, 255],
[71, 0, 255],
[122, 0, 255],
[0, 255, 184],
[0, 92, 255],
[184, 255, 0],
[0, 133, 255],
[255, 214, 0],
[25, 194, 194],
[102, 255, 0],
[92, 0, 255],
]
class StableDiffusionControlNetSegGenerator:
def __init__(self):
self.pipe = None
def load_model(
self,
stable_model_path,
scheduler,
):
if self.pipe is None:
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16
)
self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
pretrained_model_name_or_path=stable_model_path,
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16,
)
self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
self.pipe.to("cuda")
self.pipe.enable_xformers_memory_efficient_attention()
return self.pipe
def controlnet_seg(self, image_path: str):
image_processor = AutoImageProcessor.from_pretrained(
"openmmlab/upernet-convnext-small"
)
image_segmentor = UperNetForSemanticSegmentation.from_pretrained(
"openmmlab/upernet-convnext-small"
)
image = Image.open(image_path).convert("RGB")
pixel_values = image_processor(image, return_tensors="pt").pixel_values
with torch.no_grad():
outputs = image_segmentor(pixel_values)
seg = image_processor.post_process_semantic_segmentation(
outputs, target_sizes=[image.size[::-1]]
)[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
palette = np.array(ade_palette())
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
color_seg = color_seg.astype(np.uint8)
image = Image.fromarray(color_seg)
return image
def generate_image(
self,
image_path: str,
model_path: str,
prompt: str,
negative_prompt: str,
num_images_per_prompt: int,
guidance_scale: int,
num_inference_step: int,
scheduler: str,
seed_generator: int,
):
image = self.controlnet_seg(image_path=image_path)
pipe = self.load_model(
stable_model_path=model_path,
scheduler=scheduler,
)
if seed_generator == 0:
random_seed = torch.randint(0, 1000000, (1,))
generator = torch.manual_seed(random_seed)
else:
generator = torch.manual_seed(seed_generator)
output = pipe(
prompt=prompt,
image=image,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=num_inference_step,
guidance_scale=guidance_scale,
generator=generator,
).images
return output
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
controlnet_seg_image_file = gr.Image(
type="filepath", label="Image"
)
controlnet_seg_prompt = gr.Textbox(
lines=1,
show_label=False,
placeholder="Prompt",
)
controlnet_seg_negative_prompt = gr.Textbox(
lines=1,
show_label=False,
placeholder="Negative Prompt",
)
with gr.Row():
with gr.Column():
controlnet_seg_model_id = gr.Dropdown(
choices=stable_model_list,
value=stable_model_list[0],
label="Stable Model Id",
)
controlnet_seg_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label="Guidance Scale",
)
controlnet_seg_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label="Num Inference Step",
)
with gr.Row():
with gr.Column():
controlnet_seg_scheduler = gr.Dropdown(
choices=SCHEDULER_LIST,
value=SCHEDULER_LIST[0],
label="Scheduler",
)
controlnet_seg_num_images_per_prompt = (
gr.Slider(
minimum=1,
maximum=10,
step=1,
value=1,
label="Number Of Images",
)
)
controlnet_seg_seed_generator = gr.Slider(
minimum=0,
maximum=1000000,
step=1,
value=0,
label="Seed Generator",
)
controlnet_seg_predict = gr.Button(value="Generator")
with gr.Column():
output_image = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="gallery",
).style(grid=(1, 2))
controlnet_seg_predict.click(
fn=StableDiffusionControlNetSegGenerator().generate_image,
inputs=[
controlnet_seg_image_file,
controlnet_seg_model_id,
controlnet_seg_prompt,
controlnet_seg_negative_prompt,
controlnet_seg_num_images_per_prompt,
controlnet_seg_guidance_scale,
controlnet_seg_num_inference_step,
controlnet_seg_scheduler,
controlnet_seg_seed_generator,
],
outputs=[output_image],
)