End of training
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README.md
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---
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base_model: black-forest-labs/FLUX.1-dev
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library_name: diffusers
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license: other
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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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- flux
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- flux-diffusers
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- template:sd-lora
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instance_prompt: a photo of sks niels
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widget: []
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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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# Flux DreamBooth LoRA - nielsr/trained-flux-lora
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<Gallery />
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## Model description
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These are nielsr/trained-flux-lora DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
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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).
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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 niels` to trigger the image generation.
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## Download model
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[Download the *.safetensors LoRA](nielsr/trained-flux-lora/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("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
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pipeline.load_lora_weights('nielsr/trained-flux-lora', weight_name='pytorch_lora_weights.safetensors')
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image = pipeline('a photo of sks niels').images[0]
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```
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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/black-forest-labs/FLUX.1-dev/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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version https://git-lfs.github.com/spec/v1
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oid sha256:a8da14e812446bee96a0e1b4cd4500b8118e3e2801c5a5ab77a1c5217326fd5c
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size 18996742
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checkpoint-500/pytorch_lora_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f46c463613d3bb0b5227ec95c1c679ab4dccd658d6a41d208d436ae98633dda0
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size 9388696
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checkpoint-500/random_states_0.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:8fccf15e36748d9d5b2cb6cf739fe43c291ba680232a27070a02ff6b400e385b
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size 14344
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checkpoint-500/scheduler.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:68d7b85c46d195e3cd6e587d94878d77f315e0a073acfab811251f39fbe891fc
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size 1000
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pytorch_lora_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:046fafb6e11b8c14580511de2f892faf388ff68b490b857ccb8bcb1404227dc3
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size 18727592
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