112 lines
5.6 KiB
Markdown
112 lines
5.6 KiB
Markdown
# SD-Latent-Interposer
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A small neural network to provide interoperability between the latents generated by the different Stable Diffusion models.
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I wanted to see if it was possible to pass latents generated by the new SDXL model directly into SDv1.5 models without decoding and re-encoding them using a VAE first.
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## Installation
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To install it, simply clone this repo to your custom_nodes folder using the following command:
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```
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git clone https://github.com/city96/SD-Latent-Interposer custom_nodes/SD-Latent-Interposer
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```
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Alternatively, you can download the [comfy_latent_interposer.py](https://github.com/city96/SD-Latent-Interposer/raw/main/comfy_latent_interposer.py) file to your `ComfyUI/custom_nodes` folder as well. You may need to install hfhub using the command `pip install huggingface-hub` inside your venv.
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If you need the model weights for something else, they are [hosted on HF](https://huggingface.co/city96/SD-Latent-Interposer/tree/main) under the same Apache2 license as the rest of the repo. The current files are in the **"v4.0"** subfolder.
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## Usage
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Simply place it where you would normally place a VAE decode followed by a VAE encode. Set the denoise as appropirate to hide any artifacts while keeping the composition. See image below.
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Without the interposer, the two latent spaces are incompatible:
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### Local models
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The node pulls the required files from huggingface hub by default. You can create a `models` folder and place the models there if you have a flaky connection or prefer to use it completely offline. The custom node will prefer local files over HF when available. The path should be: `ComfyUI/custom_nodes/SD-Latent-Interposer/models`
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Alternatively, just clone the entire HF repo to it:
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```
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git clone https://huggingface.co/city96/SD-Latent-Interposer custom_nodes/SD-Latent-Interposer/models
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```
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### Supported Models
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Model names:
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| code | name |
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| ---- | -------------------------- |
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| `v1` | SDXL |
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| `xl` | Stable Diffusion v1.x |
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| `ca` | Stable Cascade (Stage A/B) |
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Available models:
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| From | to `v1` | to `xl` | to `ca` |
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|:----:|:-------:|:-------:|:-------:|
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| `v1` | - | v4.0 | No |
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| `xl` | v4.0 | - | No |
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| `ca` | v4.0 | v4.0 | - |
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## Training
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The training code initializes most training parameters from the provided config file. The dataset should be a single .bin file saved with `torch.save` for each latent version. The format should be [batch, channels, height, width] with the "batch" being as large as the dataset, ie 88000.
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### Interposer v4.0
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The training code currently initializes two copies of the model, one in the target direction and one in the opposite. The losses are defined based on this.
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- `p_loss` is the main criterion for the primary model.
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- `b_loss` is the main criterion for the secondary one.
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- `r_loss` is the output of the primary model back through the secondary model and checked against the source latent (basically a round trip through the two models).
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- `h_loss` is the same as `r_loss` but for the secondary model.
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All models were trained for 50000 steps with either batch size 128 (xl/v1) or 48 (cascade).
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The training was done locally on an RTX 3080 and a Tesla V100S.
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### Older versions
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<details><summary>Interposer v3.1</summary>
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### Interposer v3.1
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This is basically a complete rewrite. Replaced the mediocre bunch of conv2d layers with something that looks more like a proper neural network. No VGG loss because I still don't have a better GPU.
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Training was done on combined Flickr2K + DIV2K, with each image being processed into 6 1024x1024 segments. Padded with some of my random images for a total of 22,000 source images in the dataset.
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I think I got rid of most of the XL artifacts, but the color/hue/saturation shift issues are still there. I actually saved the optimizer state this time so I might be able to do 100K steps with visual loss on my P40s. Hopefully they won't burn up.
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v3.0 was 500k steps at a constant LR of 1e-4, v3.1 was 1M steps using a CosineAnnealingLR to drop the learning rate towards the end. Both used AdamW.
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</details>
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<details><summary>Interposer v1.1</summary>
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### Interposer v1.1
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This is the second release using the "spaceship" architecture. It was trained on the Flickr2K dataset and was continued from the v1.0 checkpoint.
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Overall, it seems to perform a lot better, especially for real life photos. I also investigated the odd v1->xl artifacts but in the end it seems [inherent to the VAE decoder stage.](https://github.com/comfyanonymous/ComfyUI/issues/1116)
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</details>
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<details><summary>Interposer v1.0</summary>
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### Interposer v1.0
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Not sure why the training loss is so different, it might be due to the """highly curated""" dataset of 1000 random images from my Downloads folder that I used to train it.
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I probably should've just grabbed LAION.
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I also trained a v1-to-v2 mode, before realizing v1 and v2 shared the same latent space. Oh well.
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</details>
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</details>
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