FLUX.1-dev-dreambooth-renca_multi_res

FLUX.1-dev-dreambooth-renca_multi_res

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

The main validation prompt used during training was:

ohwx woman, simple background

Validation settings

• CFG: 3.0
• CFG Rescale: 0.0
• Steps: 28
• Sampler: None
• Seed: 42
• Resolutions: 512x768,1280x768

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: 33
• Training steps: 500
• Learning rate: 0.0001
• Effective batch size: 4
  • Micro-batch size: 1
  • Gradient accumulation steps: 4
  • Number of GPUs: 1
• Prediction type: flow-matching
• Rescaled betas zero SNR: False
• Optimizer: adamw_bf16
• Precision: bf16
• Quantised: No
• Xformers: Not used
• LoRA Rank: 16
• LoRA Alpha: 16.0
• LoRA Dropout: 0.1
• LoRA initialisation style: default

Datasets

renca_512

• Repeats: 0
• Total number of images: 20
• Total number of aspect buckets: 1
• Resolution: 0.262144 megapixels
• Cropped: True
• Crop style: center
• Crop aspect: random

renca_768

• Repeats: 0
• Total number of images: 20
• Total number of aspect buckets: 1
• Resolution: 0.589824 megapixels
• Cropped: True
• Crop style: center
• Crop aspect: random

renca_1024

• Repeats: 0
• Total number of images: 19
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: center
• Crop aspect: random

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'trongg/FLUX.1-dev-dreambooth-renca_multi_res'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "ohwx woman, simple background"

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=28,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=512,
    height=768,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")