Stable Cascade

Stable-Casdade

Original repo: https://github.com/Stability-AI/StableCascade

Use

1. Set your compute precision in Settings -> Compute -> Precision
   to either BF16 (if supported) or FP32 (if not supported)
   Note: FP16 is not supported for this model
2. Enable model offloading in Settings -> Diffusers -> Model CPU offload
   without this, stable cascade will use >16GB of VRAM
3. Recommended: Set sampler to Default
4. Select model from Networks -> Models -> Reference
   you can select either Full or Lite variation of the model and it will automatically be downloaded on first use and loaded into SD.Next
   attempting to load a manually downloaded safetensors files is not supported as model requires special handling
   SD.Next automatically chooses BF16 variation when downloading from networs -> reference
   since its smaller and can be used with either BF16 or FP32 compute precision

UNet models:

1. Put the UNet safetensors in models/UNet folder and put the text encoder (if you use one) in there too. Text encoder name should be the UNet Name + _text_encoder
2. Load the Stable Cascade base (or a custom decoder) from Huggingface as the main model first, then load the UNet (prior) model as the UNet model from settings.

Example UNet name: sc_unet.safetensors
Example Text Encoder name: sc_unet_text_encoder.safetensors

Params

• Prompt & Negative prompt: as usual
• Width & Height: as usual
• CFG scale: used to condition the prior model, reference value is ~4
• Secondary CFG scale: used to condition decoder model, reference value is ~1
• Steps: used to control number of steps of the prior model
• Refiner steps: used to control number of steps of the decoder model
• Sampler: recommended to set to Default before loading a model
  Stable Cascade has its own sampler and results with standard samplers will look suboptimal
  Built-in sampler is DDIM/DDPM based, so if you want to experiment at least use similar sampler

Notes

• If model download fails, simply retry it, it will continue from where it left off
• Model consists out of 3 stages split into 2 pipelines which are exected as C -> B -> A:
• Full variation requires ~10GB VRAM and runs at ~3 it/s on RTX4090 at 1024px
• Lite variation requires ~4GB VRAM and runs at ~6 it/s on RTX4090 at 1024px

Note: performance numbers are for combined pipeline, both decoder and prior

Variations

Overview

Stable cascade is a 3-stage model split into two pipelines (so-called prior and decoder) and comes into two main variations: Full and Lite
You can select which one to use from Networks -> Models -> Reference

Additionally, each variation comes in 3 different precisions: FP32, BF16, and FP16
Note: FP16 is an unofficial version by @KohakuBlueleaf of the model fixed to work with FP16 and may result in slightly different output

Which precision is going to get loaded depends on:

• your user preference in Settings -> Compute -> Precision
• and GPU compatibility as not all GPUs support all precision types

Sizes

Stage A and auxiliary models sizes are fixed and noted above
Stage B and Stage C models are dependent on the variation and precision used

Variation	Precision	Stage B	Stage C
Full	FP32	6.2GB	14GB
Full	BF16	3.1GB	7GB
Full	FP16	N/A	7GB
Lite	FP32	2.8GB	4GB
Lite	BF16	1.4GB	2GB
Lite	FP16	N/A	N/A