Prompt
ylw realism, Captured at eye-level, a close-up shot of a young man with curly blonde hair stands against a backdrop of a light blue sky. The mans face is angled slightly to the left, with a slight smile on his face. His eyes are slightly open, as if he is looking to the right. His hair is a mix of brown and white, and his sweater is draped over his shoulders. The sweaters sweater is a blend of red and white stripes, and the sweaters collar is a darker shade of red.

Prompt
ylw realism, a close-up shot captures a young woman with long brown hair, wearing a mustard colored turtleneck and a yellow hat. Her left hand is resting on her head, adding a pop of color to the scene. The backdrop is a light blue, creating a stark contrast to the womans face.

Prompt
ylw realism, a young woman stands with her left hand on her hip. She is wearing a short-sleeved navy blue t-shirt with a white collar. The word "Welby" is embroidered in white on the front of the shirt. Her hair is long and wavy, and her lips are pursed. Her jeans are a light blue denim with a zipper on the side. The backdrop is a stark white wall.

The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.

Model description

strangerzonehf/Flux-YWL-Realism-LoRA

Image Processing Parameters

Parameter	Value	Parameter	Value
LR Scheduler	constant	Noise Offset	0.03
Optimizer	AdamW	Multires Noise Discount	0.1
Network Dim	64	Multires Noise Iterations	10
Network Alpha	32	Repeat & Steps	26 & 3100
Epoch	23	Save Every N Epochs	1

Labeling: florence2-en(natural language & English)

Total Images Used for Training : 22 [ Hi-Res ]

Best Dimensions

768 x 1024 (Best)
1024 x 1024 (Default)

Setting Up

import torch
from pipelines import DiffusionPipeline

base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)

lora_repo = "strangerzonehf/Flux-YWL-Realism-LoRA"
trigger_word = "ylw realism"  
pipe.load_lora_weights(lora_repo)

device = torch.device("cuda")
pipe.to(device)