Model Loading

Loading Models

Note

This guide is intended for ALL model types other than legacy SD1.5/SD2.1/SD-XL models that are loaded directly from a single file

Base Models

Important

For all base models, start with the Reference Models listed in the Networks panel on the right sidebar
SD.Next will automatically download and cache the required model files for you

Variants

A single base model may have multiple available variants. In the Networks panel, variants are grouped by category

• Base - the original model
  This is the reference implementation for the family and included without any modifications
  Base variants typically require higher step counts and guidance scale to achieve the best quality
• Distilled - a smaller or faster version of the base model
  Distilled variants often trade some quality for faster performance
  Example: Turbo variants are distilled for speed so they can run with lower step count and/or without guidance scale
• Quantized - a model version pre-quantized using SDNQ-Quantization
  These versions are much smaller and may run faster on limited memory
  Example: typical SDNQ prequantized models are ~4× smaller than the original model with near-zero quality loss
• Community - community-created variants
  These can include fine-tuning, special-purpose training, or other custom modifications
• Nunchaku - optimized variants for NVIDIA GPUs
  These may include specialized kernels or execution optimizations

Tip

When choosing a large model, prefer a Quantized variant first, unless you specifically need a Base model

Downloaded Cache

SD.Next separates large shared model components from smaller model definition files:

• models/huggingface/<model_name>/: Large model components
  These are typically the heavy weights such as the transformer, and text encoder
• models/Diffusers/<model_name>/: Model definitions, configs
  Also includes smaller supporting modules such as VAE

This separation allows multiple models to share the same large components when possible
For example, different models may reuse the same text encoder, so the large encoder weights are downloaded once and reused

Finetunes & Custom Models

Downloading Safetensors

When downloading safetensors from external sources such as CivitAI, put the files in the correct model folder so SD.Next can detect them properly

• SD15 / SD21 / SD-XL → models/Stable-diffusion/
• All other diffusion models → models/UNET/

This includes models such as Flux.1, Flux.2, Chroma, Z-Image, Anima, Krea.2, Qwen, and similar families

Other model component types belong in these folders:

• VAE → models/VAE/
• LoRA → models/Lora/
• Text Encoder → models/Text-encoder/
• Embedding → models/Embeddings/
• ControlNet → models/control/
• Detailer → models/yolo/
• Upscaler → models/chainner/

Tip

The root models folder is the base location for all model files
You can change this path in Settings → Paths

Loading finetunes

Important

Always load the reference base model first, then load the finetune on top of it

A finetune is usually NOT a complete standalone model
It modifies a single component of the base model (typically, the transformer component), so the full base model and its standard components (for example, the VAE and text encoder) must be loaded first

To load a finetune:

1. Load the reference base model from the Networks → Model panel
2. Select the finetune model in Networks → UNET panel

Loading via Settings

You can also configure base and finetune models in Settings → Model Loading.

• Base model: usually found in the Quicksettings section
• UNET model: can also be added to Quicksettings for easier access

Common pitfalls

• Custom models downloaded from external sources should be placed in the correct folder and may require restarting SD.Next or refreshing the model list
• If a finetune is loaded before its reference model, the model may fail to initialize
• If the file is in the wrong models/ subfolder, SD.Next may not detect it automatically
• When a model uses separate encoder or VAE files, those files must be placed in the correct folder and loaded by the matching Settings option