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---
license: other
license_name: bespoke-lora-trained-license
license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Image&allowDerivatives=True&allowDifferentLicense=False
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- migrated
- vintage
- style
- vintage style

base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Vintage cover
widget:
- text: ' '
  
  output:
    url: >-
      25088592.jpeg
- text: ' '
  
  output:
    url: >-
      25088594.jpeg
- text: ' '
  
  output:
    url: >-
      25088589.jpeg
- text: ' '
  
  output:
    url: >-
      25088588.jpeg
- text: ' '
  
  output:
    url: >-
      25088593.jpeg
- text: ' '
  
  output:
    url: >-
      25088590.jpeg
- text: ' '
  
  output:
    url: >-
      25088591.jpeg

---

# Vintage Cover [FLUX] 

<Gallery />



([CivitAI](https://civitai.com/models/))

## Model description

<p>An attempt to train a "Vintage cover" style Flux LoRA (I think I did a better job with the <a rel="ugc" href="https://civitai.com/models/141048/sdxl-vintage-magazine-style-lora">SDXL version</a>)</p><p>Use 'Vintage cover' in your prompts</p>

## Trigger words
You should use `Vintage cover` to trigger the image generation.
    

## Download model

Weights for this model are available in Safetensors format.

[Download](/Norod78/vintage-cover-flux/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

pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Norod78/vintage-cover-flux', weight_name='b0f2d15fd0d2413bbd1aa12b1dbb4b69_lora.safetensors')
image = pipeline('`Vintage cover`').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)