Instructions to use VHKE/3d-bever-lr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VHKE/3d-bever-lr with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("VHKE/3d-bever-lr") prompt = "3D bever lr walking in a forest -- d 45" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Upload folder using huggingface_hub
Browse files- 3d-bever-lr-000004.safetensors +3 -0
- 3d-bever-lr-000008.safetensors +3 -0
- 3d-bever-lr-000012.safetensors +3 -0
- 3d-bever-lr.safetensors +3 -0
- README.md +33 -0
- dataset.toml +14 -0
- sample/3d-bever-lr_000500_00_20250112185133.png +0 -0
- sample/3d-bever-lr_001000_00_20250112185948.png +0 -0
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- sample/3d-bever-lr_009500_00_20250112212327.png +0 -0
- sample/3d-bever-lr_010000_00_20250112213202.png +0 -0
- sample_prompts.txt +1 -0
- train.sh +38 -0
3d-bever-lr-000004.safetensors
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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/3d-bever-lr_010000_00_20250112213202.png
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text: 3D bever lr walking in a forest -- d 45
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base_model: black-forest-labs/FLUX.1-dev
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instance_prompt: 3D bever lr
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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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# 3D bever lr
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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 `3D bever lr` 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 = '/app/fluxgym/datasets/3d-bever-lr'
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class_tokens = '3D bever lr'
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num_repeats = 10
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sample_prompts.txt
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3D bever lr walking in a forest -- d 45
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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 "/app/fluxgym/models/unet/flux1-dev.sft" \
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--clip_l "/app/fluxgym/models/clip/clip_l.safetensors" \
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--t5xxl "/app/fluxgym/models/clip/t5xxl_fp16.safetensors" \
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--ae "/app/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 64 \
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--optimizer_type adamw8bit \--sample_prompts="/app/fluxgym/outputs/3d-bever-lr/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 16 \
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--save_every_n_epochs 4 \
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--dataset_config "/app/fluxgym/outputs/3d-bever-lr/dataset.toml" \
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--output_dir "/app/fluxgym/outputs/3d-bever-lr" \
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--output_name 3d-bever-lr \
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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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--multires_noise_discount 0.1 \
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--multires_noise_iterations 10 \
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--network_alpha 32 \
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--noise_offset 0.03
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