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FLUX.1 LoRA supports CLIP-L

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Kohya S
2024-08-27 19:59:40 +09:00
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@@ -9,6 +9,14 @@ __Please update PyTorch to 2.4.0. We have tested with `torch==2.4.0` and `torchv
The command to install PyTorch is as follows:
`pip3 install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu124`
Aug 27, 2024:
- FLUX.1 LoRA training now supports CLIP-L LoRA. Please remove `--network_train_unet_only`. T5XXL is not trained. The output of T5XXL is still cached, so `--cache_text_encoder_outputs` or `--cache_text_encoder_outputs_to_disk` is still required. The trained LoRA can be used with ComfyUI.
- `flux_extract_lora.py` and `convert_flux_lora.py` do not support CLIP-L LoRA.
- `--sigmoid_scale` is now effective even when `--timestep_sampling shift` is specified. Normally, leave it at 1.0. Larger values make the value before shift closer to a uniform distribution.
- __Experimental__ `--fp8_base_unet` option is added to `flux_train_network.py`. Flux can be trained with fp8, and CLIP-L can be trained with bf16/fp16. When specifying this option, the `--fp8_base` option is not required (Flux is fp8, and CLIP-L is bf16/fp16, regardless of the `--fp8_base` option).
Aug 25, 2024:
Added `shift` option to `--timestep_sampling` in FLUX.1 fine-tuning and LoRA training. Shifts timesteps according to the value of `--discrete_flow_shift` (shifts the value of sigmoid of normal distribution random number). It may be good to start with a value of 3.1582 (=e^1.15) for `--discrete_flow_shift`.
Sample command: `--timestep_sampling shift --discrete_flow_shift 3.1582 --model_prediction_type raw --guidance_scale 1.0`