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---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# flux-lora-littletinies
This is a LoRA derived from [FLUX.1-dev/](https://huggingface.co/black-forest-labs/FLUX.1-dev).
The main validation prompt used during training was:
```
ethnographic photography of teddy bear at a picnic
```
## Validation settings
- CFG: `7.5`
- CFG Rescale: `0.7`
- Steps: `50`
- Sampler: `None`
- Seed: `42`
- Resolution: `1024`
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: 23
- Training steps: 1800
- Learning rate: 0.0001
- Effective batch size: 16
- Micro-batch size: 8
- Gradient accumulation steps: 2
- Number of GPUs: 1
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Enabled
- LoRA Rank: 64
- LoRA Alpha: 16
- LoRA Dropout: 0.1
- LoRA initialisation style: default
## Datasets
### little-tinies
- Repeats: 18
- Total number of images: 78
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
## Inference
```python
import torch
from diffusers import DiffusionPipeline
model_id = '/black-forest-labs/FLUX.1-dev'
adapter_id = '/pzc163/flux-lora-littletinies'
pipeline = DiffusionPipeline.from_pretrained(model_id)\pipeline.load_adapter(adapter_id)
prompt = "ethnographic photography of teddy bear at a picnic"
negative_prompt = "blurry, cropped, ugly"
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
negative_prompt='blurry, cropped, ugly',
num_inference_steps=50,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=1152,
height=768,
guidance_scale=7.5,
guidance_rescale=0.7,
).images[0]
image.save("output.png", format="PNG")
```
inference: true
widget:
- text: 'unconditional (blank prompt)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./image0.png
- text: 'ethnographic photography of teddy bear at a picnic'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./image1.png
- text: 'a robot walking on the street,surrounded by a group of girls'
parameters:
negative_prompt: 'blurry, cropped, ugly' |