Hugging Face's logo

linoyts
/
tarot_card_flux_v12_1000_no_captions

Text-to-Image
Diffusers
diffusers-training
lora
flux
flux-diffusers
template:sd-lora
Model card Files Community
tarot_card_flux_v12_1000_no_captions
File size: 2,850 Bytes 
f29315f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
 
---
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: trcrd
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/tarot_card_flux_v12_1000_no_captions

<Gallery />

## Model description

These are linoyts/tarot_card_flux_v12_1000_no_captions 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 `trtcrd` → use `<s0><s1>` in your prompt 

    

## Download model

[Download the *.safetensors LoRA](linoyts/tarot_card_flux_v12_1000_no_captions/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('linoyts/tarot_card_flux_v12_1000_no_captions', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='linoyts/tarot_card_flux_v12_1000_no_captions', filename='tarot_card_flux_v12_1000_no_captions_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('trcrd').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]