README.md

---
license: other
base_model: "black-forest-labs/FLUX.1-dev"
tags:
  - flux
  - flux-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - safe-for-work
  - lora
  - template:sd-lora
  - standard
inference: true
widget:
- text: 'unconditional (blank prompt)'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_0_0.png
- text: 'A girl in light blue sits at the bar counter, holding an ice-cold wine glass and drinking alone on top of the Eiffel Tower, with a night view outside the window.. It features a close-up shot of her sitting by herself. She has long hair, wears glasses, faces away from the camera, and is wearing white shoes, black pants, a gray jacket, and a green scarf. with bright colors and a Paris night background featuring the Eiffel Tower. The composition is elegant, with the woman sitting on a high stool'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
---

# jazzy-st-1411

This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev).


The main validation prompt used during training was:



```
A girl in light blue sits at the bar counter, holding an ice-cold wine glass and drinking alone on top of the Eiffel Tower, with a night view outside the window.. It features a close-up shot of her sitting by herself. She has long hair, wears glasses, faces away from the camera, and is wearing white shoes, black pants, a gray jacket, and a green scarf. with bright colors and a Paris night background featuring the Eiffel Tower. The composition is elegant, with the woman sitting on a high stool
```

## Validation settings
- CFG: `3.0`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `None`
- Seed: `42`
- Resolution: `1024x1024`

Note: The validation settings are not necessarily the same as the [training settings](#training-settings).

You can find some example images in the following gallery:


<Gallery />

The text encoder **was not** trained.
You may reuse the base model text encoder for inference.


## Training settings

- Training epochs: 5
- Training steps: 5000
- Learning rate: 0.0004
- Max grad norm: 2.0
- Effective batch size: 1
  - Micro-batch size: 1
  - Gradient accumulation steps: 1
  - Number of GPUs: 1
- Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_value=1.0', 'flux_lora_target=all'])
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: No
- Xformers: Not used
- LoRA Rank: 32
- LoRA Alpha: 32.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### jazzy-512
- Repeats: 10
- Total number of images: 28
- Total number of aspect buckets: 2
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### jazzy-768
- Repeats: 10
- Total number of images: 28
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### jazzy-1024
- Repeats: 10
- Total number of images: 28
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No


## Inference


```python
import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'linhqyy/jazzy-st-1411'
pipeline = DiffusionPipeline.from_pretrained(model_id), torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "A girl in light blue sits at the bar counter, holding an ice-cold wine glass and drinking alone on top of the Eiffel Tower, with a night view outside the window.. It features a close-up shot of her sitting by herself. She has long hair, wears glasses, faces away from the camera, and is wearing white shoes, black pants, a gray jacket, and a green scarf. with bright colors and a Paris night background featuring the Eiffel Tower. The composition is elegant, with the woman sitting on a high stool"


## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
#from optimum.quanto import quantize, freeze, qint8
#quantize(pipeline.transformer, weights=qint8)
#freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
image = pipeline(
    prompt=prompt,
    num_inference_steps=20,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=1024,
    height=1024,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")
```