End of training

README.md

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+---
+base_model: black-forest-labs/FLUX.1-dev
+library_name: diffusers
+license: other
+instance_prompt: a photo of xjiminx
+widget: []
+tags:
+- text-to-image
+- diffusers-training
+- diffusers
+- lora
+- flux
+- flux-diffusers
+- template:sd-lora
+---
+
+<!-- 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. -->
+
+
+# Flux DreamBooth LoRA - xgemstarx/subset_8_step_2048_model
+
+<Gallery />
+
+## Model description
+
+These are xgemstarx/subset_8_step_2048_model DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
+
+The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
+
+Was LoRA for the text encoder enabled? False.
+
+## Trigger words
+
+You should use `a photo of xjiminx` to trigger the image generation.
+
+## Download model
+
+[Download the *.safetensors LoRA](xgemstarx/subset_8_step_2048_model/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("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
+pipeline.load_lora_weights('xgemstarx/subset_8_step_2048_model', weight_name='pytorch_lora_weights.safetensors')
+image = pipeline('a photo of xjiminx').images[0]
+```
+
+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/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
+
+
+## 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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+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
+cache_latents: false
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+center_crop: false
+checkpointing_steps: 500
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+gradient_accumulation_steps: 4
+gradient_checkpointing: false
+guidance_scale: 1.0
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+image_column: image
+instance_data_dir: subset_8
+instance_prompt: a photo of xjiminx
+learning_rate: 1.0
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+logging_dir: logs
+logit_mean: 0.0
+logit_std: 1.0
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+lr_warmup_steps: 0
+max_grad_norm: 1.0
+max_sequence_length: 512
+max_train_steps: 2048
+mixed_precision: bf16
+mode_scale: 1.29
+num_class_images: 100
+num_train_epochs: 1024
+num_validation_images: 4
+optimizer: prodigy
+output_dir: subset_8_step_2048_model
+pretrained_model_name_or_path: black-forest-labs/FLUX.1-dev
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+prior_loss_weight: 1.0
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+repeats: 1
+report_to: tensorboard
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+sample_batch_size: 4
+scale_lr: false
+seed: 0
+text_encoder_lr: 5.0e-06
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+use_8bit_adam: false
+validation_epochs: 50
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