hgh

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-penguin.png filter=lfs diff=lfs merge=lfs -text
-shrine.png filter=lfs diff=lfs merge=lfs -text

README.md

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-## flux-hasui-lora-d4-sigmoid-raw-gs1.0.safetensors
-Experimental LoRA for FLUX.1 dev.
-
-Trained with `sd-scripts` (Aug. 11) `sd3` branch. __NOTE:__ This settings requires > 26GB VRAM. Please add `--fp8_base` to enable fp8 training to reduce VRAM usage.
-
-```
-accelerate launch  --mixed_precision bf16 --num_cpu_threads_per_process 1 flux_train_network.py --pretrained_model_name_or_path flux1/flux1-dev.sft --clip_l sd3/clip_l.safetensors --t5xxl sd3/t5xxl_fp16.safetensors --ae flux1/ae_dev.sft --cache_latents_to_disk --save_model_as safetensors --sdpa --persistent_data_loader_workers --max_data_loader_n_workers 2 --seed 42 --gradient_checkpointing --mixed_precision bf16 --save_precision bf16 --network_module networks.lora_flux --network_dim 4 --optimizer_type adamw8bit --learning_rate 1e-3 --network_train_unet_only --cache_text_encoder_outputs --cache_text_encoder_outputs_to_disk  --highvram --max_train_epochs 4 --save_every_n_epochs 1 --dataset_config hasui_1024_bs1.toml --output_dir flux/lora --output_name lora-name --timestep_sampling sigmoid --model_prediction_type raw --guidance_scale 1.0
-```
-
-.toml is below.
-```.toml
-[general]
-flip_aug = true
-color_aug = false
-
-[[datasets]]
-enable_bucket = true
-resolution = [1024,1024]
-bucket_reso_steps = 64
-max_bucket_reso = 2048
-min_bucket_reso = 128
-bucket_no_upscale = false
-batch_size = 1
-random_crop = false
-shuffle_caption = false
-
-  [[datasets.subsets]]
-  image_dir = "path/to/train/images"
-  num_repeats = 1
-  caption_extension = ".txt"
-```
-
-
-## sdxl-negprompt8-v1m.safetensors
-Negative embeddings for sdxl. Num vectors per token = 8
- 
-## stable-cascade-c-lora-hasui-v02.safetensors
-Sample of LoRA for Stable Cascade Stage C.
-
-Feb 22, 2024 Update: Fixed a bug that LoRA is not applied to some modules (to_q/k/v and to_out) in Attention.
-
-__This is an experimental model, so the format of the weights may change in the future.__
-
-- a painting of an anthropomorphic penguin sitting in a cafe reading a book and having a coffee --w 1024 --h 1024 --d 1
-![sample1](penguin.png)
-
-- a painting of japanese shrine in winter with snowfall --w 832 --h 1152 --d 1234
-![sample2](shrine.png)
-
-This model is trained with 169 images with captions. U-Net only, dim=4, conv_dim=4, alpha=1, lr=1e-3, 4 epochs, mixed precision bf16, 8bit AdamW, batch size 8, resolution 1024x1024 with aspect ratio bucketing. VRAM usage is approximately 22 GB. 

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