vheretic/jennah_flux_lora

jennahfluxlora

This is a standard PEFT LoRA derived from black-forest-labs/FLUX.1-dev.

The main validation prompt used during training was:

a lighthouse, j3nn4h_style, bold fluid lines with simplified forms, some use of watercolor painting with realistic colors

Validation settings

• —CFG: 3.0
• —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.

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: 454
• —Training steps: 10000
• —Learning rate: 0.0001
• —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: Pure BF16
• —Quantised: No
• —Xformers: Not used
• —LoRA Rank: 16
• —LoRA Alpha: None
• —LoRA Dropout: 0.1
• —LoRA initialisation style: default

Datasets

paper_dataset

• —Repeats: 0
• —Total number of images: 22
• —Total number of aspect buckets: 1
• —Resolution: 1.048576 megapixels
• —Cropped: False
• —Crop style: None
• —Crop aspect: None

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'vheretic/jennah_flux_lora'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "a lighthouse, j3nn4h_style, bold fluid lines with simplified forms, some use of watercolor painting with realistic colors"

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=3.0,
).images[0]
image.save("output.png", format="PNG")