Use externally generated Depth map?

[bruibbruib]

With img2img, is it possible to use my own, better quality depth map? I load my depth map into ControlNet, but can't understand how to utilise it.
Thanks for any help!!

[kiriri]

Replace gradio_depth2image.py with the following code

from share import *
import config

import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random

from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.midas import MidasDetector
from cldm.model import create_model, load_state_dict
from ldm.models.diffusion.ddim import DDIMSampler

apply_midas = MidasDetector()

model = create_model('./models/cldm_v15.yaml').cpu()
model.load_state_dict(load_state_dict('./models/control_sd15_depth.pth', location='cuda'))
model = model.cuda()
ddim_sampler = DDIMSampler(model)


def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, scale, seed, eta, calculate_depth):
    with torch.no_grad():
        input_image = HWC3(input_image)
        img = resize_image(input_image, image_resolution)
        H, W, C = img.shape
        if calculate_depth:
            detected_map, _ = apply_midas(resize_image(input_image, detect_resolution))
            detected_map = HWC3(detected_map)
            detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
        else:
            detected_map = img

        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
        control = torch.stack([control for _ in range(num_samples)], dim=0)
        control = einops.rearrange(control, 'b h w c -> b c h w').clone()

        if seed == -1:
            seed = random.randint(0, 65535)
        seed_everything(seed)

        if config.save_memory:
            model.low_vram_shift(is_diffusing=False)

        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
        un_cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
        shape = (4, H // 8, W // 8)

        if config.save_memory:
            model.low_vram_shift(is_diffusing=True)

        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
                                                     shape, cond, verbose=False, eta=eta,
                                                     unconditional_guidance_scale=scale,
                                                     unconditional_conditioning=un_cond)

        if config.save_memory:
            model.low_vram_shift(is_diffusing=False)

        x_samples = model.decode_first_stage(samples)
        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)

        results = [x_samples[i] for i in range(num_samples)]
    return [detected_map] + results


block = gr.Blocks().queue()
with block:
    with gr.Row():
        gr.Markdown("## Control Stable Diffusion with Depth Maps")
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(source='upload', type="numpy")
            prompt = gr.Textbox(label="Prompt")
            calculate_depth = gr.Checkbox(label="Calculate Depth")
            run_button = gr.Button(label="Run")
            with gr.Accordion("Advanced options", open=False):
                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=256)
                detect_resolution = gr.Slider(label="Depth Resolution", minimum=128, maximum=1024, value=384, step=1)
                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=-1)
                eta = gr.Number(label="eta (DDIM)", value=0.0)
                a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
                n_prompt = gr.Textbox(label="Negative Prompt",
                                      value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
        with gr.Column():
            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
    ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, scale, seed, eta,calculate_depth]
    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])


block.launch(server_name='0.0.0.0')

Open it in a text editor and highlight all the parts containing "calculate_depth" to see what I changed. By default your desired functionality was not available.

Restart gradio and you should have a checkbox that lets you toggle between generating a depth map from the image, and using the image itself as the depth map.

[bruibbruib]

Holy Smokes... That is amazing. TY.
I will attempt it and get back with results.

[bruibbruib]

I'm sorry, I'm probably being a fool, but I cannot find
gradio_depth2image.py
Or anything close...

[kiriri]

gradio_depth2image.py
Right in the root folder.

[bruibbruib]

Ah Ha! I was right. I AM a fool.
I have just realised I was on the wrong Git. I meant to ask this question on ContraolNet extension for AUTOMATIC1111 page.
Sorry for that. I will show myself out....

[BiuliaGonelli]

Hi! Sorry, I just tried to use your code but for some reason the toggle doesn't show up, sorry, I'm a noob, do you perhaps know why that is happening?