paconaranjo
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Browse files- README.md +33 -0
- dataset.toml +14 -0
- inah-000004.safetensors +3 -0
- inah-000008.safetensors +3 -0
- inah.safetensors +3 -0
- sample/inah_000500_00_20241123035320.png +0 -0
- sample/inah_000500_00_20241123044239.png +0 -0
- sample/inah_001000_00_20241123045700.png +0 -0
- sample/inah_001500_00_20241123051120.png +0 -0
- sample_prompts.txt +1 -0
- train.bat +34 -0
README.md
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---
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tags:
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- text-to-image
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- flux
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- lora
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- diffusers
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- template:sd-lora
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- fluxgym
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widget:
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- output:
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url: sample/inah_001500_00_20241123051120.png
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text: inah
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base_model: black-forest-labs/FLUX.1-dev
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instance_prompt: inah
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license: other
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license_name: flux-1-dev-non-commercial-license
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license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
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---
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# inah
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A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
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<Gallery />
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## Trigger words
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You should use `inah` to trigger the image generation.
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## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
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Weights for this model are available in Safetensors format.
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dataset.toml
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[general]
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shuffle_caption = false
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caption_extension = '.txt'
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keep_tokens = 1
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[[datasets]]
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resolution = 512
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batch_size = 1
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keep_tokens = 1
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[[datasets.subsets]]
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image_dir = 'C:\Pinokio\api\Fluxgym\datasets\inah'
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class_tokens = 'inah'
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num_repeats = 5
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inah-000004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e3c4fbda6f81b822a433b9002b114ce46705245ac465a234e9d61f72de6550cc
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size 39757096
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inah-000008.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:39519382f75662ecd3c579be5cb0e61f7e94e7b34d791035f6b8a8127bd9886c
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size 39757096
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inah.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d3aeb70e8968571a16e15002d82b307e63195675a295155af629f0cf07eb7154
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size 39757096
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sample/inah_000500_00_20241123035320.png
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sample/inah_000500_00_20241123044239.png
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sample/inah_001000_00_20241123045700.png
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sample/inah_001500_00_20241123051120.png
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sample_prompts.txt
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inah
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train.bat
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accelerate launch ^
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--mixed_precision bf16 ^
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--num_cpu_threads_per_process 1 ^
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sd-scripts/flux_train_network.py ^
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--pretrained_model_name_or_path "C:\Pinokio\api\Fluxgym\models\unet\flux1-dev.sft" ^
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--clip_l "C:\Pinokio\api\Fluxgym\models\clip\clip_l.safetensors" ^
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--t5xxl "C:\Pinokio\api\Fluxgym\models\clip\t5xxl_fp16.safetensors" ^
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--ae "C:\Pinokio\api\Fluxgym\models\vae\ae.sft" ^
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--cache_latents_to_disk ^
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--save_model_as safetensors ^
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--sdpa --persistent_data_loader_workers ^
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--max_data_loader_n_workers 2 ^
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--seed 42 ^
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--gradient_checkpointing ^
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--mixed_precision bf16 ^
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--save_precision bf16 ^
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--network_module networks.lora_flux ^
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--network_dim 4 ^
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--optimizer_type adamw8bit ^--sample_prompts="C:\Pinokio\api\Fluxgym\outputs\inah\sample_prompts.txt" --sample_every_n_steps="500" ^
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--learning_rate 8e-4 ^
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--cache_text_encoder_outputs ^
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--cache_text_encoder_outputs_to_disk ^
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--fp8_base ^
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--highvram ^
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--max_train_epochs 10 ^
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--save_every_n_epochs 4 ^
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--dataset_config "C:\Pinokio\api\Fluxgym\outputs\inah\dataset.toml" ^
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--output_dir "C:\Pinokio\api\Fluxgym\outputs\inah" ^
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--output_name inah ^
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--timestep_sampling shift ^
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--discrete_flow_shift 3.1582 ^
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--model_prediction_type raw ^
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--guidance_scale 1 ^
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--loss_type l2 ^
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