End of training
Browse files- README.md +78 -0
- checkpoint-500/optimizer.bin +3 -0
- checkpoint-500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
- checkpoint-500/scaler.pt +3 -0
- checkpoint-500/scheduler.bin +3 -0
- logs/dreambooth-sd3-lora/1740215385.5205522/events.out.tfevents.1740215385.daintlabH.284300.1 +3 -0
- logs/dreambooth-sd3-lora/1740215385.5293095/hparams.yml +74 -0
- logs/dreambooth-sd3-lora/events.out.tfevents.1740215385.daintlabH.284300.0 +3 -0
- pytorch_lora_weights.safetensors +3 -0
README.md
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---
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base_model: stabilityai/stable-diffusion-3-medium-diffusers
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library_name: diffusers
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license: other
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instance_prompt: a photo of sks boot
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widget: []
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tags:
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- text-to-image
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- diffusers-training
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- diffusers
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- lora
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- template:sd-lora
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- sd3
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- sd3-diffusers
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# SD3 DreamBooth LoRA - BJEon121/trained-sd3_fancy_boot
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<Gallery />
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## Model description
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These are BJEon121/trained-sd3_fancy_boot DreamBooth LoRA weights for stabilityai/stable-diffusion-3-medium-diffusers.
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The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md).
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Was LoRA for the text encoder enabled? False.
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## Trigger words
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You should use `a photo of sks boot` to trigger the image generation.
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## Download model
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[Download the *.safetensors LoRA](BJEon121/trained-sd3_fancy_boot/tree/main) in the Files & versions tab.
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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```py
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from diffusers import AutoPipelineForText2Image
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import torch
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pipeline = AutoPipelineForText2Image.from_pretrained(stabilityai/stable-diffusion-3-medium-diffusers, torch_dtype=torch.float16).to('cuda')
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pipeline.load_lora_weights('BJEon121/trained-sd3_fancy_boot', weight_name='pytorch_lora_weights.safetensors')
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image = pipeline('a photo of sks boot').images[0]
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```
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### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
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- **LoRA**: download **[`diffusers_lora_weights.safetensors` here 💾](/BJEon121/trained-sd3_fancy_boot/blob/main/diffusers_lora_weights.safetensors)**.
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- Rename it and place it on your `models/Lora` folder.
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- On AUTOMATIC1111, load the LoRA by adding `<lora:your_new_name:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
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For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
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## License
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Please adhere to the licensing terms as described [here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE.md).
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## Intended uses & limitations
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#### How to use
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```python
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# TODO: add an example code snippet for running this diffusion pipeline
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```
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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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checkpoint-500/optimizer.bin
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checkpoint-500/pytorch_lora_weights.safetensors
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checkpoint-500/random_states_0.pkl
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checkpoint-500/scaler.pt
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checkpoint-500/scheduler.bin
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logs/dreambooth-sd3-lora/1740215385.5205522/events.out.tfevents.1740215385.daintlabH.284300.1
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logs/dreambooth-sd3-lora/1740215385.5293095/hparams.yml
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adam_beta1: 0.9
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adam_beta2: 0.999
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adam_epsilon: 1.0e-08
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adam_weight_decay: 0.0001
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adam_weight_decay_text_encoder: 0.001
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allow_tf32: false
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cache_dir: null
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cache_latents: false
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caption_column: null
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center_crop: false
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checkpointing_steps: 500
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checkpoints_total_limit: null
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class_data_dir: null
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class_prompt: null
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dataloader_num_workers: 0
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dataset_config_name: null
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dataset_name: null
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gradient_accumulation_steps: 4
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gradient_checkpointing: false
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hub_model_id: null
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hub_token: null
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image_column: image
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instance_data_dir: /nas/home/jj275/Personalization_ICCV_2025/data/fancy_boot
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instance_prompt: a photo of sks boot
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learning_rate: 0.0004
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local_rank: 0
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logging_dir: logs
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logit_mean: 0.0
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logit_std: 1.0
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lora_blocks: null
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lora_layers: null
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lr_num_cycles: 1
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lr_power: 1.0
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lr_scheduler: constant
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lr_warmup_steps: 0
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max_grad_norm: 1.0
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max_sequence_length: 77
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max_train_steps: 500
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mixed_precision: fp16
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mode_scale: 1.29
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num_class_images: 100
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num_train_epochs: 500
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num_validation_images: 4
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optimizer: AdamW
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output_dir: trained-sd3_fancy_boot
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precondition_outputs: 1
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pretrained_model_name_or_path: stabilityai/stable-diffusion-3-medium-diffusers
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prior_generation_precision: null
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prior_loss_weight: 1.0
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prodigy_beta3: null
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prodigy_decouple: true
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prodigy_safeguard_warmup: true
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prodigy_use_bias_correction: true
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push_to_hub: true
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random_flip: false
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rank: 4
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repeats: 1
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report_to: tensorboard
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resolution: 512
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resume_from_checkpoint: null
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revision: null
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sample_batch_size: 4
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scale_lr: false
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seed: 0
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text_encoder_lr: 5.0e-06
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train_batch_size: 1
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train_text_encoder: false
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upcast_before_saving: false
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use_8bit_adam: false
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validation_epochs: 50
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validation_prompt: null
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variant: null
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weighting_scheme: logit_normal
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with_prior_preservation: false
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logs/dreambooth-sd3-lora/events.out.tfevents.1740215385.daintlabH.284300.0
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pytorch_lora_weights.safetensors
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