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Browse files- README.md +34 -0
- dataset.toml +14 -0
- gucci-sweater-000004.safetensors +3 -0
- gucci-sweater-000008.safetensors +3 -0
- gucci-sweater-000012.safetensors +3 -0
- gucci-sweater.safetensors +3 -0
- sample/gucci-sweater_000100_00_20241127230037.png +0 -0
- sample/gucci-sweater_000200_00_20241127230227.png +0 -0
- sample/gucci-sweater_000300_00_20241127230416.png +0 -0
- sample/gucci-sweater_000400_00_20241127230606.png +0 -0
- sample/gucci-sweater_000500_00_20241127230757.png +0 -0
- sample/gucci-sweater_000600_00_20241127230947.png +0 -0
- sample_prompts.txt +1 -0
- train.sh +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/gucci-sweater_000600_00_20241127230947.png
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text: A white man standing in new york wearing a gucci sweater with black jeans.
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The background features a vibrant and iconic view of New York City
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base_model: black-forest-labs/FLUX.1-dev
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instance_prompt: gucci
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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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# Gucci Sweater
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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 `gucci` 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 = '/home/ubuntu/fluxgym/datasets/gucci-sweater'
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class_tokens = 'gucci'
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num_repeats = 10
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gucci-sweater-000004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:b6ae86f6d0db62bd390b76e1f69984689534c2ea2bc191585ae1f4193e424eb7
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size 39757768
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gucci-sweater-000008.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:2ac978db8d2b76aa49c41bea844daa9e870136d6077ce106f91a1b9960426176
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size 39757768
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gucci-sweater-000012.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:14d3bbaa7d597171be672dc97c9b0c91f70f024eefdcda281511a3d930be5477
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size 39757768
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gucci-sweater.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1d3517f8c0633c8956acfd71f564b758ad4afd49583c7bb91636e253ab52a807
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size 39757768
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sample/gucci-sweater_000100_00_20241127230037.png
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sample/gucci-sweater_000200_00_20241127230227.png
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sample/gucci-sweater_000300_00_20241127230416.png
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sample/gucci-sweater_000400_00_20241127230606.png
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sample/gucci-sweater_000500_00_20241127230757.png
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sample/gucci-sweater_000600_00_20241127230947.png
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sample_prompts.txt
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A white man standing in new york wearing a gucci sweater with black jeans. The background features a vibrant and iconic view of New York City
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train.sh
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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 "/home/ubuntu/fluxgym/models/unet/flux1-dev.sft" \
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--clip_l "/home/ubuntu/fluxgym/models/clip/clip_l.safetensors" \
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--t5xxl "/home/ubuntu/fluxgym/models/clip/t5xxl_fp16.safetensors" \
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--ae "/home/ubuntu/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="/home/ubuntu/fluxgym/outputs/gucci-sweater/sample_prompts.txt" --sample_every_n_steps="100" \
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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 16 \
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--save_every_n_epochs 4 \
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--dataset_config "/home/ubuntu/fluxgym/outputs/gucci-sweater/dataset.toml" \
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--output_dir "/home/ubuntu/fluxgym/outputs/gucci-sweater" \
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--output_name gucci-sweater \
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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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