---
base_model: black-forest-labs/FLUX.1-dev
---

*Note that all these models are derivatives of black-forest-labs/FLUX.1-dev and therefore covered by the 
[FLUX.1 [dev] Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md) license.*

*Some models are derivatives of finetunes, and are included with the permission of the finetuner*

# Optimised Flux GGUF models

A collection of GGUF models using mixed quantization (different layers quantized to different precision to optimise fidelity v. memory).

They were created using the [convert.py script](https://github.com/chrisgoringe/mixed-gguf-converter).

They can be loaded in ComfyUI using the [ComfyUI GGUF Nodes](https://github.com/city96/ComfyUI-GGUF). Just put the gguf files in your
models/unet directory.

## Bigger numbers in the name = smaller model!

## Naming convention (mx for 'mixed')

[original_model_name]_mxNN_N.gguf

where NN_N is the approximate *reduction* in VRAM usage compared the full 16 bit version.
```
-  9_0 might just fit on a 16GB card
- 10_6 is a good balance for 16GB cards,
- 12_0 is roughly the size of an 8 bit model,
- 14_1 should work for 12 GB cards
- 15_2 is fully quantised to Q4_1 
```
## How is this optimised?

The process for optimisation is as follows:

- 240 prompts used for flux images popular at civit.ai were run through the full Flux.1-dev model with randomised resolution and step count.
- For a randomly selected step in the inference, the hidden states before and after the layer stack were captured.
- For each layer in turn, and for each of the Q8_0, Q5_1 and Q4_1 quantizations:
  - A single layer was quantized
  - The initial hidden states were  processed by the modified layer stack
  - The error (MSE) in the final hidden state was calculated
- This gives a 'cost' for each possible layer quantization
- An optimised quantization is one that gives the desired reduction in size for the smallest total cost
  - A series of recipies for optimization have been created from the calculated costs
- the various 'in' blocks, the final layer blocks, and all normalization scale parameters are stored in float32

## Also note

- Tests on using bitsandbytes quantizations showed they did not perform as well as the equivalent sized GGUF quants
- Different quantizations of different parts of a layer gave significantly worse results
- Leaving bias in 16 bit made no relevant difference
- Costs were evaluated for the original Flux.1-dev model. They are assumed to be essentially the same for finetunes

## Details

The optimisation recipes are as follows (layers 0-18 are the double_block_layers, 19-56 are the single_block_layers)

```python

CONFIGURATIONS = {
    "9_0" : { 
        'casts': [
            {'layers': '0-10',             'castto': 'BF16'},
            {'layers': '11-14, 54',        'castto': 'Q8_0'},
            {'layers': '15-36, 39-53, 55', 'castto': 'Q5_1'},
            {'layers': '37-38, 56',        'castto': 'Q4_1'},
        ]
    },
    "10_6" : { 
        'casts': [
            {'layers': '0-4, 10',      'castto': 'BF16'},
            {'layers': '5-9, 11-14',   'castto': 'Q8_0'},
            {'layers': '15-35, 41-55', 'castto': 'Q5_1'},
            {'layers': '36-40, 56',    'castto': 'Q4_1'},
        ]
    },
    "12_0" : {
        'casts': [
            {'layers': '0-2',                  'castto': 'BF16'},
            {'layers': '5, 7-12',              'castto': 'Q8_0'},
            {'layers': '3-4, 6, 13-33, 42-55', 'castto': 'Q5_1'},
            {'layers': '34-41, 56',            'castto': 'Q4_1'},
        ]
    },
    "14_1" : {
        'casts': [
            {'layers': '0-25, 27-28, 44-54', 'castto': 'Q5_1'},
            {'layers': '26, 29-43, 55-56',   'castto': 'Q4_1'},
        ]
    },
    "15_2" : {
        'casts': [
            {'layers': '0-56', 'castto': 'Q4_1'},
        ]
    },
}
```