diffusion_webui/controlnet/controlnet_depth.py

from diffusers import ( StableDiffusionControlNetPipeline, 
                       ControlNetModel, UniPCMultistepScheduler )

from transformers import pipeline
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 controlnet_depth(image_path:str):
    depth_estimator = pipeline('depth-estimation')

    image = Image.open(image_path)
    image = depth_estimator(image)['depth']
    image = np.array(image)
    image = image[:, :, None]
    image = np.concatenate([image, image, image], axis=2)
    image = Image.fromarray(image)

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

    return controlnet, image

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

    controlnet, image = controlnet_depth(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_depth_app():
    with gr.Tab('Depth'):
        controlnet_depth_image_file = gr.Image(
            type='filepath', 
            label='Image'
        )

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

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

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

        with gr.Accordion("Advanced Options", open=False):
            controlnet_depth_guidance_scale = gr.Slider(
                minimum=0.1, 
                maximum=15, 
                step=0.1, 
                value=7.5, 
                label='Guidance Scale'
            )

            controlnet_depth_num_inference_step = gr.Slider(
                minimum=1, 
                maximum=100, 
                step=1, 
                value=50, 
                label='Num Inference Step'
            )

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

    variables = {
        'image_path': controlnet_depth_image_file,
        'model_path': controlnet_depth_model_id,
        'prompt': controlnet_depth_prompt,
        'negative_prompt': controlnet_depth_negative_prompt,
        'guidance_scale': controlnet_depth_guidance_scale,
        'num_inference_step': controlnet_depth_num_inference_step,
        'predict': controlnet_depth_predict
    }
    
    return variables