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linoyts
/
huggy_flux_3000_ti_1_pure_ti_w_t5

Text-to-Image
Diffusers
diffusers-training
lora
flux
flux-diffusers
template:sd-lora
Model card Files Community
huggy_flux_3000_ti_1_pure_ti_w_t5
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---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
instance_prompt: a <s0><s1> hugging face emoji
widget: []
---

<!-- 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 - linoyts/huggy_flux_3000_ti_1_pure_ti_w_t5

<Gallery />

## Model description

These are linoyts/huggy_flux_3000_ti_1_pure_ti_w_t5 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](linoyts/huggy_flux_3000_ti_1_pure_ti_w_t5/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')

embedding_path = hf_hub_download(repo_id='linoyts/huggy_flux_3000_ti_1_pure_ti_w_t5', filename='huggy_flux_3000_ti_1_pure_ti_w_t5_emb.safetensors', repo_type="model")
    state_dict = load_file(embedding_path)
    pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
    pipeline.load_textual_inversion(state_dict["t5"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
            
image = pipeline('a <s0><s1> hugging face emoji').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]