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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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instance_prompt: a photo of a car |
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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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- flux |
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- flux-diffusers |
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- template:sd-lora |
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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 - sachi1/3d-icon-Flux-LoRA_car1 |
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<Gallery /> |
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## Model description |
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These are sachi1/3d-icon-Flux-LoRA_car1 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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Pivotal tuning was enabled: True. |
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## Trigger words |
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To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: |
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to trigger concept `TOK` → use `<s0><s1>` in your prompt |
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## Download model |
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[Download the *.safetensors LoRA](sachi1/3d-icon-Flux-LoRA_car1/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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from huggingface_hub import hf_hub_download |
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from safetensors.torch import load_file |
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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('sachi1/3d-icon-Flux-LoRA_car1', weight_name='pytorch_lora_weights.safetensors') |
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embedding_path = hf_hub_download(repo_id='sachi1/3d-icon-Flux-LoRA_car1', filename='3d-icon-Flux-LoRA_car1_emb.safetensors', repo_type="model") |
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state_dict = load_file(embedding_path) |
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pipeline.load_textual_inversion(state_dict["clip_l"], token=[], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) |
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pipeline.load_textual_inversion(state_dict["t5"], token=[], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) |
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image = pipeline('a photo of a car').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] |
