Using FLUX Models on Invoke

Supported Flux.1 base models

1. Pro - Not supported
   1. Accessed via API only
2. Dev - Open-weight, guidance-distilled model requiring license for commercial use
   1. Professional Edition: Supported for users with a commercial license
   2. Community Edition: Supported for all users
3. Schnell - Fasted Flux model designed for local development and personal use
   1. Supported in both Professional and Community Editions

Accessing Flux models

Professional Edition:

• Flux Dev - We are working to allow users to self-upgrade directly in the Invoke application but currently users can request commercial access to the dev model through this form
• Flux Schnell- Users can use the ‘FLUX Schnell’ model by using the ‘Add account models to project’ dropdown on the Model Management tab within Project Settings or upload their own version of the Schnell model for use
  • Note: Enterprise users may need to have an Account Admin add Flux models to the Enterprise account

Community Edition:

• Flux Dev - Users can use the ‘FLUX Dev (Quantized)’ model found on the starter models tab or upload a version dev model for use
• Flux Schnell - Users can use the ‘FLUX Schnell’ or ‘FLUX Schnell (Quantized)’ models found on the ‘Starter Models’ tab within Model Manger or upload their own version of the dev model for use

Troubleshooting errors when uploading Flux models

Right now, there’s a wide range of different formats being used by model trainers and fine-tuners across the ecosystem, and unfortunately, there isn’t any clear standardization. Model trainers are often not specifying the formats they use, which can cause issues when uploading models.

We’ve chosen to support the most commonly used format variances, but those are not well labeled on the sites that host Flux LoRAs, so it’s hard to give guidance on which ones work and which don’t yet. In general, you can understand current model support through the following rules:

• Models with full (non-quantized) model weights (float8, float16, bfloat16, float32) should work
• bitsandbytes NF4 quantized models should work
• GGUF quantized models will be supported soon
• Most LoRA models trained with diffusers or kohya should work. Please report variants to [email protected] and we'll work on adding support

We’re working on driving that standardization through the Open Model Initiative, but for now, we’re focused on optimizing for the most widely adopted formats. If your model isn’t working, it could be due to the format it’s been trained in. Feel free to reach out if you need more help!

Troubleshooting slow generation speeds for Flux

A common cause of slowness is unnecessary offloads of large models from VRAM / RAM. To avoid unnecessary model offloads, make sure that your ram and vram config settings are properly configured in ${INVOKEAI_ROOT}/invokeai.yaml.

Example configuration:

# In ${INVOKEAI_ROOT}/invokeai.yaml

# ...

# ram is the number of GBs of RAM used to keep models warm in memory.

# Set ram to a value slightly below you system RAM capacity. Make sure to leave room for other processes and non-model

# Invoke memory. 24GB could be a reasonable starting point on a system with 32GB of RAM.

# If you hit RAM out-of-memory errors or find that your system RAM is full resulting in slowness, then adjust this value

# downward.

ram: 24

# vram is the number of GBs of VRAM used to keep models warm on the GPU.

# Set VRAM to a value slightly below your system VRAM capacity. Leave room for non-model VRAM memory overhead.

# 20GB is a reasonable starting point on a 24GB GPU.

# If you hit VRAM out-of-memory errors, then adjust this value downward.

vram: 20

Flux model support

Transformer Models

T5 Text Encoder Models

CLIP Text Encoder Models

LoRA Models