End of training

README.md

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+---
+base_model: black-forest-labs/FLUX.2-klein-9B
+library_name: diffusers
+license: other
+instance_prompt: a photo of AndyYang
+widget: []
+tags:
+- text-to-image
+- diffusers-training
+- diffusers
+- lora
+- flux2-klein
+- flux2-klein-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.2 [Klein] DreamBooth LoRA - mm9all/klein9b-lora-klein9b-lora-j9n8xs-9b-test-3-1770645277
+
+<Gallery />
+
+## Model description
+
+These are mm9all/klein9b-lora-klein9b-lora-j9n8xs-9b-test-3-1770645277 DreamBooth LoRA weights for black-forest-labs/FLUX.2-klein-9B.
+
+The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux2 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux2.md).
+
+Quant training? None
+
+## Trigger words
+
+You should use `a photo of AndyYang` to trigger the image generation.
+
+## Download model
+
+[Download the *.safetensors LoRA](mm9all/klein9b-lora-klein9b-lora-j9n8xs-9b-test-3-1770645277/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.2", torch_dtype=torch.bfloat16).to('cuda')
+pipeline.load_lora_weights('mm9all/klein9b-lora-klein9b-lora-j9n8xs-9b-test-3-1770645277', weight_name='pytorch_lora_weights.safetensors')
+image = pipeline('a photo of AndyYang').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.2/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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training_progress.json

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+{"current_step": 1000, "total_steps": 1000, "resume_from_step": 0, "current_loss": 0.627, "best_loss": 0.343, "best_checkpoint_step": 890, "current_lr": 0.0001, "step_time": 2.6753219604492187, "eta_seconds": 0, "progress": 100, "status": "training", "model_version": "klein_9b"}