SDXL

Stable Diffusion XL (SDXL) is a latent-diffusion UNet model trained at 1024×1024, with two CLIP text encoders. It has a large ecosystem of community fine-tunes, LoRAs, ControlNets and IP-Adapters, and runs comfortably on mid-range GPUs.

SDXL was released together with a separate SDXL Refiner model — a second pass that polishes the last denoising steps of an SDXL image. InvokeAI treats the refiner as its own model family and supports it in the workflow editor (see SDXL Refiner).

InvokeAI identifies SDXL models automatically on install, including their variant (normal or inpainting).

License

Model	License	Commercial use
SDXL base and Refiner	CreativeML OpenRAIL++-M	Allowed, subject to the license’s use restrictions
Dreamshaper XL v2 Turbo, RealVisXL V5.0	OpenRAIL++	Allowed, subject to the license’s use restrictions
Juggernaut XL v9	CreativeML OpenRAIL-M	Allowed, but its card says it may not be served behind a paid API without a license from RunDiffusion

None of the starter downloads is gated.

This is a summary, not legal advice. Read the full license on the model’s HuggingFace page before using a model commercially.

Hardware

The System Requirements table lists 8 GB VRAM and 16 GB RAM as the minimum for SDXL at 1024×1024. On smaller cards, see Low-VRAM mode. For large SDXL checkpoints, FP8 Storage can reduce VRAM use further on NVIDIA GPUs.

Installing

The easiest path is the SDXL bundle in the Model Manager’s starter models. It installs:

• Juggernaut XL v9 — a photograph-focused main model
• sdxl-vae-fp16-fix — an SDXL VAE that works at fp16 precision (see VAE)
• Two IP-Adapters for reference images — Standard and Precise — plus their image encoder
• ControlNets: Hard Edge Detection (canny), Depth Map, Soft Edge Detection (softedge), Pose Detection (openpose), Contour Detection (scribble) and Tile
• SwinIR, a 4× upscaler used by the Upscale tool

Other SDXL starter models can be installed individually:

Starter model	Notes
Dreamshaper XL v2 Turbo	A turbo model — see Turbo models
Architecture (RealVisXL5)	Photorealistic, with architecture among its use cases
SDXL Refiner	The original Stability AI refiner (stabilityai/stable-diffusion-xl-refiner-1.0)
Alien Style, Noodles Style	Style LoRAs; trigger with alienzkin and noodlez respectively
Multi-Guidance Detection (Union Pro)	A single ControlNet that supports 10+ control types
QRCode Monster (SDXL)	ControlNet for scannable, stylized QR codes
T2I-Adapters: Hard Edge Detection (canny), Lineart, Sketch	Lighter-weight alternatives to ControlNet
PiD Decoder SDXL (2K to 4K)	4× super-resolution decoder — see PiD

Every SDXL main model in the starter list installs sdxl-vae-fp16-fix as a dependency.

SDXL main models are self-contained: the UNet, both text encoders and the VAE come in one install. Both formats are supported:

• Diffusers folders (for example a HuggingFace repo ID such as RunDiffusion/Juggernaut-XL-v9).
• Single-file checkpoints (.safetensors / .ckpt), the format most community fine-tunes are published in.

No HuggingFace token is needed for the starter models.

Generation settings

Selecting an SDXL model applies these defaults, which you can override globally or per model in the Model Manager’s Default Settings:

Setting	SDXL default
Width × Height	1024 × 1024
Steps	30
CFG Scale	7
Scheduler	Euler Ancestral

• Resolution — dimensions must be multiples of 8. An image size equivalent to 1024×1024 in pixel count is recommended; use the aspect-ratio control to change shape at roughly the same area.
• Negative prompt — fully supported.
• Schedulers — the full standard list (DDIM, DPM++ 2M Karras, DPM++ SDE Karras, Euler, Euler Ancestral, UniPC, and so on) is available.
• Prompt syntax — SDXL prompts support Compel weighting and prompt functions. The prompt is sent to both of SDXL’s text encoders.

The Advanced section of the Generate panel adds these controls for SDXL:

• VAE and VAE Precision — see VAE.
• Color Compensation — adjusts the input image to reduce color shifts during inpainting and image-to-image. This control is available only for SDXL.
• Seamless Tiling — generates images that tile without visible seams, on the X axis, Y axis or both.
• HiDiffusion — improves structure at higher resolutions (it is most noticeable at 1536px and above). See HiDiffusion.
• PiD — see PiD decoding.

CLIP Skip and CFG Rescale are not offered for SDXL.

Turbo models

Turbo fine-tunes need far fewer steps and a much lower CFG than the defaults. For Dreamshaper XL v2 Turbo, the starter model’s description recommends CFG Scale 2, 4–8 steps and the DPM++ SDE Karras scheduler. Set these as the model’s Default Settings in the Model Manager so they are applied whenever you select it.

VAE

The original SDXL VAE is prone to numeric overflow at fp16, which can produce black images. There are two remedies:

• Select sdxl-vae-fp16-fix in the VAE field (or set it as the model’s default VAE in the Model Manager). It is installed alongside every SDXL starter model.
• Or set VAE Precision to fp32, at the cost of more memory and time for decoding.

Inpainting models

SDXL models with an inpainting variant are detected automatically and can be used to fill masked areas and extend images on the Canvas. There is no SDXL inpainting model among the starter models.

LoRAs and embeddings

• LoRAs for SDXL, including LyCORIS variants, are supported. Add them in the LoRA section of the Generate panel. LoRAs made for a different model family are not applied.
• Textual-inversion embeddings for SDXL are supported in the positive and negative prompts. Type < in a prompt box to pick from the compatible embeddings you have installed.

Control layers

SDXL supports ControlNet and T2I-Adapter models as control layers on the Canvas. A control layer guides composition from an image — edges, depth, pose, line art and so on. The Multi-Guidance Detection (Union Pro) ControlNet handles many control types with one model. See Adding a gallery image to a layer for creating a control layer from an image.

Reference images (IP-Adapter)

SDXL supports up to five reference images, using IP-Adapter models:

• Standard Reference (IP Adapter ViT-H)
• Precise Reference (IP Adapter Plus ViT-H) — references the image with higher precision.

Each reference image has a Mode — Style, Composition or Both — and a Weight. Under Advanced, you can set the range of steps the reference is active for and, in Style mode, choose a Style variant (Standard, Strong or Precise).

The starter IP-Adapters carry their own image encoder. The CLIP Vision selector next to the model matters only for IP-Adapters installed from a single checkpoint file; if the chosen encoder is not installed, it is downloaded on first use.

Regional guidance

SDXL supports regional guidance on the Canvas: each region can have its own positive prompt, its own negative prompt, and its own IP-Adapter reference images.

PiD decoding

SDXL supports NVIDIA’s PiD decoder, which replaces the VAE decode with a 4× super-resolution decode. Install PiD Decoder SDXL (2K to 4K) from the starter models — only this 2K-to-4K preset exists for SDXL — and enable PiD in the Advanced section before generating. See PiD Decode for details and limitations. In the workflow editor, the corresponding node is Latents to Image - SDXL + PiD (4x SR).

Upscaling

The Upscale tool supports SDXL main models. It requires an SDXL Tile or Union ControlNet (the starter Tile ControlNet is part of the bundle), and the bundle’s SwinIR model can serve as the upscaler.

SDXL Refiner

The refiner is a separate model that runs as a second pass over an SDXL latent. It cannot generate an image on its own.

• Installing — install SDXL Refiner from the starter models, or any refiner checkpoint or Diffusers folder; InvokeAI recognizes refiners automatically and lists them under their own SDXL Refiner family.
• Using it — the refiner is not selectable in the Generate panel or on the Canvas. Use it in the workflow editor.

A refiner workflow runs two denoise passes over the same latents:

1. Load the base model with Main Model - SDXL and prompt it with Prompt - SDXL. In its Denoise - SD1.5, SDXL node, set Denoising End below 1 (for example 0.8) so the base model stops before the last steps.
2. Load the refiner with Refiner Model - SDXL and prompt it with Prompt - SDXL Refiner — one node for the positive and one for the negative conditioning. This node takes a Style prompt and an Aesthetic Score (default 6).
3. Feed the base pass’s latents into a second Denoise - SD1.5, SDXL node that uses the refiner’s UNet, with Denoising Start set to the value where the base pass stopped, then decode with Latents to Image - SD1.5, SDXL.

The refiner uses SDXL’s VAE.

Workflows

The workflow library includes Text to Image - SDXL and MultiDiffusion SDXL. Other nodes that work with SDXL include Apply LoRA - SDXL, Apply LoRA Collection - SDXL, ControlNet - SD1.5, SD2, SDXL, T2I-Adapter - SD1.5, SDXL, IP-Adapter - SD1.5, SDXL, Apply Seamless - SD1.5, SDXL and Tiled Multi-Diffusion Denoise - SD1.5, SDXL.