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Mujeeb603/lora-training

sourceHugging Faceotherupdated 2y agoView on Hugging Face
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lora-training

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

The main validation prompt used during training was:

A simple, clean line drawing of a right-angled triangle. The right angle is at the bottom left corner. The base is labeled as '6 cm' and the height is labeled as '4 cm'. The drawing is set against a plain white background.

Validation settings

  • —CFG: 3.5
  • —CFG Rescale: 0.0
  • —Steps: 24
  • —Sampler: None
  • —Seed: 42
  • —Resolution: 512

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: 8
  • —Training steps: 3200
  • —Learning rate: 0.0008
  • —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: bf16
  • —Quantised: Yes: int8-quanto
  • —Xformers: Not used
  • —LoRA Rank: 8
  • —LoRA Alpha: None
  • —LoRA Dropout: 0.1
  • —LoRA initialisation style: default

Datasets

right-triangles

  • —Repeats: 0
  • —Total number of images: 380
  • —Total number of aspect buckets: 1
  • —Resolution: 512 px
  • —Cropped: True
  • —Crop style: center
  • —Crop aspect: square

Inference

python
import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'Mujeeb603/lora-training'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "A simple, clean line drawing of a right-angled triangle. The right angle is at the bottom left corner. The base is labeled as '6 cm' and the height is labeled as '4 cm'. The drawing is set against a plain white 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=24,
    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=512,
    guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")