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---
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.
## Naming convention (mx for 'mixed')
[original_model_name]_mxN_N.gguf
where N_N is the average number of bits per parameter.
## Good choices to start with
```
- 9_2 is a good choice for 16 GB cards
- 6_9 just fits on a 12 GB card
- 5_9 is comfortable on 12 GB cards
```
## Speed?
On an A40 (plenty of VRAM), everything except the model identical, the time taken to generate an image (30 steps, deis sampler) was:
- 5_1 => 40.1s
- 5_9 => 55.4s
- 6_9 => 52.1s
- 7_4 => 49.7s
- 7_6 => 43.6s
- 8_4 => 46.8s
- 9_2 => 42.8s
- 9_6 => 48.2s
for comparison:
- bfloat16 (default) =>
- fp8_e4m3fn =>
- fp8_e5m2 =>
## 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 quantization:
- 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 - how much different it is to the full model
- 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
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