End of training

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+image_0.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,81 @@
+---
+base_model: stabilityai/stable-diffusion-3-medium-diffusers
+library_name: diffusers
+license: openrail++
+tags:
+- text-to-image
+- diffusers-training
+- diffusers
+- lora
+- sd3
+- sd3-diffusers
+- template:sd-lora
+instance_prompt: a photo of Nongshim Squid Snack 83g
+widget:
+- text: A photo of Nongshim Squid Snack 83g in the refrigerator
+  output:
+    url: image_0.png
+---
+
+<!-- This model card has been generated automatically according to the information the training script had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+# SD3 DreamBooth LoRA - BangDoon/lora-Nongshim_Squid_Snack_83g-SD3
+
+<Gallery />
+
+## Model description
+
+These are BangDoon/lora-Nongshim_Squid_Snack_83g-SD3 DreamBooth LoRA weights for stabilityai/stable-diffusion-3-medium-diffusers.
+
+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).
+
+Was LoRA for the text encoder enabled? False.
+
+## Trigger words
+
+You should use `a photo of Nongshim Squid Snack 83g` to trigger the image generation.
+
+## Download model
+
+[Download the *.safetensors LoRA](BangDoon/lora-Nongshim_Squid_Snack_83g-SD3/tree/main) in the Files & versions tab.
+
+## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
+
+```py
+from diffusers import AutoPipelineForText2Image
+import torch
+pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-3-medium-diffusers', torch_dtype=torch.float16).to('cuda')
+pipeline.load_lora_weights('BangDoon/lora-Nongshim_Squid_Snack_83g-SD3', weight_name='pytorch_lora_weights.safetensors')
+image = pipeline('A photo of Nongshim Squid Snack 83g in the refrigerator').images[0]
+```
+
+### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
+
+- **LoRA**: download **[`diffusers_lora_weights.safetensors` here 💾](/BangDoon/lora-Nongshim_Squid_Snack_83g-SD3/blob/main/diffusers_lora_weights.safetensors)**.
+    - Rename it and place it on your `models/Lora` folder.
+    - 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/).
+
+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)
+
+## License
+
+Please adhere to the licensing terms as described [here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE).
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# TODO: add an example code snippet for running this diffusion pipeline
+```
+
+#### Limitations and bias
+
+[TODO: provide examples of latent issues and potential remediations]
+
+## Training details
+
+[TODO: describe the data used to train the model]

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logs/dreambooth-sd3-lora/1725519989.76198/hparams.yml

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+adam_beta1: 0.9
+adam_beta2: 0.999
+adam_epsilon: 1.0e-08
+adam_weight_decay: 0.0001
+adam_weight_decay_text_encoder: 0.001
+allow_tf32: false
+cache_dir: null
+caption_column: null
+center_crop: true
+checkpointing_steps: 500
+checkpoints_total_limit: null
+class_data_dir: null
+class_prompt: null
+dataloader_num_workers: 0
+dataset_config_name: null
+dataset_name: null
+gradient_accumulation_steps: 2
+gradient_checkpointing: true
+hub_model_id: null
+hub_token: null
+image_column: image
+instance_data_dir: "./\uC6D0\uCC9C\uB370\uC774\uD130/10092_\uB18D\uC2EC\uC624\uC9D5\
+  \uC5B4\uC9D183G/"
+instance_prompt: a photo of Nongshim Squid Snack 83g
+learning_rate: 0.0001
+local_rank: 0
+logging_dir: logs
+logit_mean: 0.0
+logit_std: 1.0
+lr_num_cycles: 1
+lr_power: 1.0
+lr_scheduler: constant
+lr_warmup_steps: 0
+max_grad_norm: 1.0
+max_sequence_length: 77
+max_train_steps: 500
+mixed_precision: fp16
+mode_scale: 1.29
+num_class_images: 100
+num_train_epochs: 100
+num_validation_images: 1
+optimizer: AdamW
+output_dir: lora-Nongshim_Squid_Snack_83g-SD3
+precondition_outputs: 1
+pretrained_model_name_or_path: stabilityai/stable-diffusion-3-medium-diffusers
+prior_generation_precision: null
+prior_loss_weight: 1.0
+prodigy_beta3: null
+prodigy_decouple: true
+prodigy_safeguard_warmup: true
+prodigy_use_bias_correction: true
+push_to_hub: true
+random_flip: false
+rank: 4
+repeats: 1
+report_to: tensorboard
+resolution: 1024
+resume_from_checkpoint: null
+revision: null
+sample_batch_size: 4
+scale_lr: false
+seed: 0
+text_encoder_lr: 5.0e-06
+train_batch_size: 2
+train_text_encoder: false
+use_8bit_adam: true
+validation_epochs: 300
+validation_prompt: A photo of Nongshim Squid Snack 83g in the refrigerator
+variant: null
+weighting_scheme: logit_normal
+with_prior_preservation: false

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