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---
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")
```
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