diffusion_webui/controlnet/controlnet_seg.py

from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
import torch
from diffusers import (StableDiffusionControlNetPipeline, 
                       ControlNetModel, UniPCMultistepScheduler)


from PIL import Image
import gradio as gr
import numpy as np
import torch

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 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]]


def controlnet_mlsd(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)
    controlnet = ControlNetModel.from_pretrained(
        "fusing/stable-diffusion-v1-5-controlnet-seg", torch_dtype=torch.float16
    )

    return controlnet, image


def stable_diffusion_controlnet_seg(
    image_path:str,
    model_path:str,
    prompt:str,
    negative_prompt:str,
    guidance_scale:int,
    num_inference_step:int,
    ):

    controlnet, image = controlnet_mlsd(image_path=image_path)

    pipe = StableDiffusionControlNetPipeline.from_pretrained(
        pretrained_model_name_or_path=model_path, 
        controlnet=controlnet, 
        safety_checker=None, 
        torch_dtype=torch.float16
    )

    pipe.to("cuda")
    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
    pipe.enable_xformers_memory_efficient_attention()

    output = pipe(
        prompt = prompt,
        image = image,
        negative_prompt = negative_prompt,
        num_inference_steps = num_inference_step,
        guidance_scale = guidance_scale,
    ).images

    return output[0]

def stable_diffusion_controlnet_seg_app():
    with gr.Tab('Segmentation'):
        controlnet_seg_image_file = gr.Image(
            type='filepath', 
            label='Image'
        )

        controlnet_seg_model_id = gr.Dropdown(
            choices=stable_model_list, 
            value=stable_model_list[0], 
            label='Stable Model Id'
        )

        controlnet_seg_prompt = gr.Textbox(
            lines=1, 
            value=stable_prompt_list[0], 
            label='Prompt'
        )

        controlnet_seg_negative_prompt = gr.Textbox(
            lines=1, 
            value=stable_negative_prompt_list[0], 
            label='Negative Prompt'
        )

        with gr.Accordion("Advanced Options", open=False):
            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'
            )

        controlnet_seg_predict = gr.Button(value='Generator')

    variables = {
        'image_path': controlnet_seg_image_file,
        'model_path': controlnet_seg_model_id,
        'prompt': controlnet_seg_prompt,
        'negative_prompt': controlnet_seg_negative_prompt,
        'guidance_scale': controlnet_seg_guidance_scale,
        'num_inference_step': controlnet_seg_num_inference_step,
        'predict': controlnet_seg_predict,
    }
    
    return variables