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Keltezaa
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mckenna-grace-768-portrait

Text-to-Image
Diffusers
stable-diffusion
lora
template:sd-lora
migrated
celebrity
Model card Files Community
2
mckenna-grace-768-portrait
File size: 2,562 Bytes 
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---
license: other
license_name: bespoke-lora-trained-license
license_link: https://multimodal.art/civitai-licenses?allowNoCredit=False&allowCommercialUse=RentCivit&allowDerivatives=False&allowDifferentLicense=False
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- migrated
- celebrity

base_model: black-forest-labs/FLUX.1-dev
instance_prompt: 
widget:
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  output:
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      30071437.jpeg
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      29865282.jpeg

---

# Mckenna Grace, 768 portrait 

<Gallery />





## Model description

<p>Specially for portraits and closeup portraits. Selected eyes look at camera. HD and sharp images selected to dataset. Used 100-150 photos.</p><p>Trained on Comfyui FluxTrainer on 16gb Vram</p><p>Better use resolution 768 (minimize face-body proportion distortions) and than upscale whatever you want.</p><p>Small size of LORA because of training only 2 blocks: 7 and 20.</p><p>Top line of the GRID is this checkpoint. Grids for lora strength 0.8 and 1.2</p>



## Download model

Weights for this model are available in Safetensors format.

[Download](/Keltezaa/mckenna-grace-768-portrait/tree/main) them in the Files & versions tab.

## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)

```py
from diffusers import AutoPipelineForText2Image
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device)
pipeline.load_lora_weights('Keltezaa/mckenna-grace-768-portrait', weight_name='mckenna_768_rank128_bf16-step03500.safetensors')
image = pipeline('Your custom prompt').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)