Flux LoRA Collections
Collection
Flux THE LoRA β’ 131 items β’ Updated β’ 33
How to use prithivMLmods/Flux-Fine-Detail-LoRA with Diffusers:
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("prithivMLmods/Flux-Fine-Detail-LoRA")
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."
image = pipe(prompt).images[0]The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.



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
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)
You should use Super Detail to trigger the image generation.
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Base model
black-forest-labs/FLUX.1-dev