data-is-better-together/open-image-preferences-v1-flux-dev-lora

Flux DreamBooth LoRA - data-is-better-together/image-preferences-flux-dev-lora

Prompt

a bustling manga street, devoid of vehicles, detailed with vibrant colors and dynamic line work, characters in the background adding life and movement, under a soft golden hour light, with rich textures and a lively atmosphere, high resolution, sharp focus

Prompt

a boat in the canals of Venice, painted in gouache with soft, flowing brushstrokes and vibrant, translucent colors, capturing the serene reflection on the water under a misty ambiance, with rich textures and a dynamic perspective

Prompt

A vibrant orange poppy flower, enclosed in an ornate golden frame, against a black backdrop, rendered in anime style with bold outlines, exaggerated details, and a dramatic chiaroscuro lighting.

Prompt

Armored armadillo, detailed anatomy, precise shading, labeled diagram, cross-section, high resolution.

Prompt

A photographic photo of a hedgehog in a forest 4k

Prompt

Grainy shot of a robot cooking in the kitchen, with soft shadows and nostalgic film texture.

Model description

These are davidberenstein1957/image-preferences-flux-schnell-lora DreamBooth LoRA weights for black-forest-labs/FLUX.1-schnell.

The weights were trained using DreamBooth with the Flux diffusers trainer.

Was LoRA for the text encoder enabled? False.

Trigger words

You should use ["Cinematic", "Photographic", "Anime", "Manga", "Digital art", "Pixel art", "Fantasy art", "Neonpunk", "3D Model", “Painting”, “Animation” “Illustration”] to trigger the image generation.

Download model

Download the *.safetensors LoRA in the Files & versions tab.

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('davidberenstein1957/image-preferences-flux-dev-lora', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('["Cinematic", "Photographic", "Anime", "Manga", "Digital art", "Pixel art", "Fantasy art", "Neonpunk", "3D Model", “Painting”, “Animation” “Illustration”]').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

License

Please adhere to the licensing terms as described here.

Intended uses & limitations

How to use

# TODO: add an example code snippet for running this diffusion pipeline

Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

Training details

[TODO: describe the data used to train the model]