| | --- |
| | base_model: black-forest-labs/FLUX.1-dev |
| | library_name: diffusers |
| | license: other |
| | instance_prompt: urc |
| | 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 - Wunderlife/urctest |
| |
|
| | <Gallery /> |
| |
|
| | ## Model description |
| |
|
| | These are Wunderlife/urctest 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. |
| |
|
| | Pivotal tuning was enabled: True. |
| |
|
| | ## Trigger words |
| |
|
| | To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: |
| |
|
| | to trigger concept `TOK` → use `<s0><s1>` in your prompt |
| | |
| | |
| | |
| | ## Download model |
| |
|
| | [Download the *.safetensors LoRA](Wunderlife/urctest/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 |
| | from huggingface_hub import hf_hub_download |
| | from safetensors.torch import load_file |
| | |
| | pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') |
| | pipeline.load_lora_weights('Wunderlife/urctest', weight_name='pytorch_lora_weights.safetensors') |
| | embedding_path = hf_hub_download(repo_id='Wunderlife/urctest', filename='urctest_emb.safetensors', repo_type="model") |
| | state_dict = load_file(embedding_path) |
| | pipeline.load_textual_inversion(state_dict["clip_l"], token=[], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) |
| | |
| | image = pipeline('urc').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] |
