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Disra/
lora-anime-test-02

Name: lora-anime-test-02
Availability: InStock

diffusers

stable-diffusion

stable-diffusion-diffusers

black-forest-labs/FLUX.1-dev

adapter

creativeml-openrail-m

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lora-anime-test-02

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

The main validation prompt used during training was:

anime style digital art of a girl with blue-green hair and green eyes wearing a one piece swimsuit

Example Images

Base flux - no lora - are on top, with the lora are on the bottom ( same promt and seed )

Validation settings

CFG: 3.5
CFG Rescale: 0.0
Steps: 20
Sampler: None
Seed: 42
Resolution: 1024

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

You can find some example images in the following gallery:

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

Training settings

Training epochs: 142
Training steps: 5000
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: bf16
Quantised: Yes: int8-quanto
Xformers: Not used
LoRA Rank: 4
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

anime-test-01

Repeats: 0
Total number of images: 35
Total number of aspect buckets: 1
Resolution: 0.262144 megapixels
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 = 'Disra/lora-anime-test-02'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "anime style digital art of a girl with blue-green hair and green eyes wearing a one piece swimsuit"


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