pwntus / stable-diffusion-depth2img

Create variations of an image while preserving shape and depth.

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Input

Output

Run time and cost

This model runs on Nvidia A100 (40GB) GPU hardware. Predictions typically complete within 8 seconds.

Readme

This stable-diffusion-depth2img model is resumed from stable-diffusion-2-base (512-base-ema.ckpt) and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by MiDaS (dpt_hybrid) which is used as an additional conditioning.

Model description

Intended use

See stabilityai/stable-diffusion-2-depth for direct use, misuse, malicious use, out-of-scope use, limitations, and bias.

Training

Training Data The model developers used the following dataset for training the model:

  • LAION-5B and subsets (details below). The training data is further filtered using LAION’s NSFW detector, with a “p_unsafe” score of 0.1 (conservative). For more details, please refer to LAION-5B’s NeurIPS 2022 paper and reviewer discussions on the topic.

Training Procedure Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,

  • Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
  • Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.
  • The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
  • The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called v-objective, see https://arxiv.org/abs/2202.00512.