Flux-Super-Detail-LoRA

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

Prompt
Super Detail, A close-up shot of a man with a brown hat on his head. His eyes are blue and he has brown hair. His hair is wet from the rain. The background is blurred.
Prompt
Super Detail, A close-up shot of a womans face, taken from a low-angle perspective. The womans eyes are a piercing of turquoise, and her hair is a vibrant shade of brown. Her lips are painted a deep red, with a slight smile. Her eyebrows are a light brown, adding a touch of texture to her face. The background is dark, creating a stark contrast to the womans skin.
Prompt
Super Detail, a close-up shot of a womans head and shoulders is seen against a vibrant red backdrop. The womans face is adorned with a white face, adorned with blue eyes, and her brown hair cascades over her shoulders. She is wearing a red turtleneck, with a ribbed collar. Her lips are painted a vibrant shade of red, adding a pop of color to her face. Her eyebrows are a darker shade of blue, adding depth to the composition.

prithivMLmods/Flux-Fine-Detail-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 15 & 2470
Epoch 10 Save Every N Epochs 1
Labeling: florence2-en(natural language & English)

Total Images Used for Training : 15

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 = "prithivMLmods/Flux-Fine-Detail-LoRA"
trigger_word = "Super Detail"  
pipe.load_lora_weights(lora_repo)

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

Trigger words

You should use Super Detail to trigger the image generation.

Download model

Weights for this model are available in Safetensors format.

Download them in the Files & versions tab.

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