train code about controlnet ?

I am very interested in training a new controlnet model. After studying the kandinsky-2-2-controlnet-depth model uploaded in HuggingFace, I found that its architecture seems to be different from the controlnet model of the traditional stable diffusion model.

In my understanding, the structure of the unet model corresponding to kandinsky-2-2-controlnet-depth has been modified compared to the unet model of kandinsky-2-2-decoder. The "in_channels" parameter of conv_in has been changed to 8, and an additional module called "input_hint_block" has been added.

In terms of parameters, the weights and biases are also completely different from the unet model of kandinsky-2-2-decoder.

My training approach is as follows: First, download the unet models corresponding to kandinsky-2-2-controlnet-depth and kandinsky-2-2-decoder. Then, copy the overall parameters of the unet in kandinsky-2-2-decoder to the corresponding unet parameters in kandinsky-2-2-controlnet-depth (except for the parts with different structures).

Afterward, train the new unet model based on the fill50k dataset.

I wonder if there are any issues with this approach? I would greatly appreciate any help or suggestions you can provide.

Additionally, I seem to have not found the training code specifically for kandinsky-2-2-controlnet-depth. I would greatly appreciate it if you could provide information on where to find it.

@Blucknote

The issue names no repository file, test, or training entry point. Start by locating the Kandinsky-2-2-controlnet-depth and Kandinsky-2-2-decoder model definitions and the fill50k training setup; done would require a documented, validated training path or a clear pointer to existing code.

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