flux-dev-china-girl

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

No validation prompt was used during training.

None

Validation settings

CFG: 3.0
CFG Rescale: 0.0
Steps: 20
Sampler: FlowMatchEulerDiscreteScheduler
Seed: 42
Resolutions: 1024x1024,1280x768
Skip-layer guidance:

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

You can find some example images in the following gallery:

Prompt
unconditional (blank prompt)

Negative Prompt
blurry, cropped, ugly

Prompt
a breathtaking anime-style portrait of girl, capturing her essence with vibrant colors and expressive features

Negative Prompt
blurry, cropped, ugly

Prompt
a high-quality, detailed photograph of girl as a sous-chef, immersed in the art of culinary creation

Negative Prompt
blurry, cropped, ugly

Prompt
a lifelike and intimate portrait of girl, showcasing her unique personality and charm

Negative Prompt
blurry, cropped, ugly

Prompt
a cinematic, visually stunning photo of girl, emphasizing her dramatic and captivating presence

Negative Prompt
blurry, cropped, ugly

Prompt
an elegant and timeless portrait of girl, exuding grace and sophistication

Negative Prompt
blurry, cropped, ugly

Prompt
a dynamic and adventurous photo of girl, captured in an exciting, action-filled moment

Negative Prompt
blurry, cropped, ugly

Prompt
a mysterious and enigmatic portrait of girl, shrouded in shadows and intrigue

Negative Prompt
blurry, cropped, ugly

Prompt
a vintage-style portrait of girl, evoking the charm and nostalgia of a bygone era

Negative Prompt
blurry, cropped, ugly

Prompt
an artistic and abstract representation of girl, blending creativity with visual storytelling

Negative Prompt
blurry, cropped, ugly

Prompt
a futuristic and cutting-edge portrayal of girl, set against a backdrop of advanced technology

Negative Prompt
blurry, cropped, ugly

Prompt
a beautifully crafted portrait of a woman, highlighting her natural beauty and unique features

Negative Prompt
blurry, cropped, ugly

Prompt
a powerful and striking portrait of a man, capturing his strength and character

Negative Prompt
blurry, cropped, ugly

Prompt
a playful and spirited portrait of a boy, capturing youthful energy and innocence

Negative Prompt
blurry, cropped, ugly

Prompt
a charming and vibrant portrait of a girl, emphasizing her bright personality and joy

Negative Prompt
blurry, cropped, ugly

Prompt
a heartwarming and cohesive family portrait, showcasing the bonds and connections between loved ones

Negative Prompt
blurry, cropped, ugly

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

Training settings

Training epochs: 1
Training steps: 3500
Learning rate: 0.0001
- Learning rate schedule: polynomial
- Warmup steps: 100
Max grad norm: 2.0
Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
Gradient checkpointing: True
Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible', 'flux_lora_target=all'])
Optimizer: adamw_bf16
Trainable parameter precision: Pure BF16
Caption dropout probability: 10.0%
LoRA Rank: 16
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

dreambooth-512

Repeats: 1
Total number of images: 473
Total number of aspect buckets: 1
Resolution: 0.262144 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

dreambooth-1024

Repeats: 1
Total number of images: 473
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'likewendy/flux-dev-china-girl'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "An astronaut is riding a horse through the jungles of Thailand."


## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
#from optimum.quanto import quantize, freeze, qint8
#quantize(pipeline.transformer, weights=qint8)
#freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
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(42),
    width=1024,
    height=1024,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")

likewendy
/

flux-dev-china-girl