Realistic wide shot photo of woman posing in a luxurious satin lingerie set, featuring a plunging bra, delicate thong and a classic garter belt with black stockings. The satin lingerie shimmers softly in the light, and the cut emphasizes both sophistication and a hint of allure. The lingerie is detailed with fine lace edges, highlighting her alluring figure. She elegantly styled hair as if getting ready for a formal event. The photo has a cinematic quality with rays of light and dramatic play of shadow and light

Validation settings

CFG: 3.5
CFG Rescale: 0.0
Steps: 20
Sampler: FlowMatchEulerDiscreteScheduler
Seed: 42
Resolution: 832x1216
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:

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

Training settings

Training epochs: 10
Training steps: 2000
Learning rate: 0.0001
- Learning rate schedule: constant
- Warmup steps: 500
Max grad value: 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
Base model precision: no_change
Caption dropout probability: 10.0%
LoRA Rank: 16
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

reddy-v2-512

Repeats: 10
Total number of images: 13
Total number of aspect buckets: 1
Resolution: 0.262144 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

reddy-v2-1024

Repeats: 10
Total number of images: 5
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

Inference

python

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'Unmapped2895/reddy-v4'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "Realistic wide shot photo of woman posing in a luxurious satin lingerie set, featuring a plunging bra, delicate thong and a classic garter belt with black stockings. The satin lingerie shimmers softly in the light, and the cut emphasizes both sophistication and a hint of allure. The lingerie is detailed with fine lace edges, highlighting her alluring figure. She elegantly styled hair as if getting ready for a formal event. The photo has a cinematic quality with rays of light and dramatic play of shadow and light"


## 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
model_output = 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=832,
    height=1216,
    guidance_scale=3.5,
).images[0]

model_output.save("output.png", format="PNG")