accelerate launch "./sd-scripts/flux_train_network.py" ^
--pretrained_model_name_or_path "D:\kohya_ss\flux\unet\flux1DevFp8_v10.safetensors" ^
--clip_l "D:/stable-diffusion-webui-forge-main/models/text_encoder/flux/clip_l.safetensors" ^
--t5xxl "D:/stable-diffusion-webui-forge-main/models/text_encoder/flux/flux1T5TextEncoder_stock.safetensors" ^
--ae "D:/stable-diffusion-webui-forge-main/models/VAE/flux/flux1DevVAE_stock.safetensors" ^
--train_data_dir "D:\kohya_ss\flux\img" ^
--reg_data_dir "D:\kohya_ss\flux\regularization\img" ^
--output_dir "D:/kohya_ss/flux/models/from_deepweb_concept_lora_flux" ^
--output_name "from_deepweb_concept_lora_flux" ^
--save_model_as safetensors ^
--sdpa ^
--sample_sampler "euler" ^
--sample_prompts "D:\kohya_ss\lucignolo_grimace_prompts.txt" ^
--sample_every_n_epochs 1 ^
--max_data_loader_n_workers 4 ^
--seed 777 ^
--gradient_checkpointing ^
--mixed_precision bf16 ^
--save_precision bf16 ^
--network_module networks.lora_flux ^
--network_dim 16 ^
--network_alpha 32 ^
--network_args train_blocks=single ^
--optimizer_type Prodigy ^
--lr_scheduler constant ^
--lr_scheduler_num_cycles 1 ^
--lr_scheduler_power 1 ^
--learning_rate 1 ^
--network_train_unet_only ^
--timestep_sampling sigmoid ^
--max_grad_norm 1 ^
--model_prediction_type raw ^
--apply_t5_attn_mask ^
--guidance_scale 1.0 ^
--min_bucket_reso 256 ^
--max_bucket_reso 2048 ^
--bucket_reso_steps 64 ^
--resolution 512,512 ^
--discrete_flow_shift 3 ^
--cache_text_encoder_outputs ^
--cache_text_encoder_outputs_to_disk ^
--cache_latents ^
--fp8_base ^
--enable_bucket ^
--split_mode ^
--caption_extension .txt ^
--keep_tokens 0 ^
--min_snr_gamma 7 ^
--save_every_n_epochs 1 ^
--train_batch_size "3" ^
--noise_offset 0.05 ^
--max_train_epochs 30 ^
--max_timestep 1000 ^
--adaptive_noise_scale 0