---
license: creativeml-openrail-m
base_model: "sayakpaul/FLUX.1-merged"
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - lora
  - template:sd-lora
inference: true
widget:
- text: 'unconditional (blank prompt)'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_0_0.png
- text: 'a photo of a man'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
---

# meme1

This is a LoRA derived from [sayakpaul/FLUX.1-merged](https://huggingface.co/sayakpaul/FLUX.1-merged).



The main validation prompt used during training was:



```
a photo of a man
```

## Validation settings
- CFG: `7.5`
- 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: 99
- Training steps: 2100
- Learning rate: 0.0005
- Effective batch size: 1
  - Micro-batch size: 1
  - Gradient accumulation steps: 1
  - Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: bf16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LoRA Rank: 32
- LoRA Alpha: 32.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default


## Datasets

### carbman1
- Repeats: 0
- Total number of images: 21
- Total number of aspect buckets: 1
- Resolution: 1024 px
- Cropped: False
- Crop style: None
- Crop aspect: None


## Inference


```python
import torch
from diffusers import DiffusionPipeline

model_id = 'sayakpaul/FLUX.1-merged'
adapter_id = 'ambientocclusion/meme1'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "a photo of a man"


pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
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=7.5,
).images[0]
image.save("output.png", format="PNG")
```