ohwx-man

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

The main validation prompt used during training was:

a photograph of ohwx man

Validation settings

CFG: 3.5
CFG Rescale: 0.0
Steps: 28
Sampler: None
Seed: 42
Resolution: 1024

Note: The validation settings are not necessarily the same as the training settings.

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

Training epochs: 499
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, stochastic bf16
Precision: Pure BF16
Quantised: Yes: int8-quanto
Xformers: Not used
LoRA Rank: 16
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

ohwx-man-v3

Repeats: 0
Total number of images: 4
Total number of aspect buckets: 1
Resolution: 512 px
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 = 'AA12312424/ohwx-man'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "a photograph of ohwx man"


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=1024,
    height=1024,
    guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")