Flux LoRA Collections
Collection
Flux THE LoRA β’ 131 items β’ Updated β’ 33
How to use prithivMLmods/Flux.1-Dev-Pov-DoorEye-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.1-Dev-Pov-DoorEye-LoRA")
prompt = "look in 2, Captured at eye-level, a full view of two cartoon characters, a yellow cartoon character with big eyes and a wide mouth, stands on a gray carpeted floor. The character on the left is wearing a blue and white striped shirt, and black shoes. His arms are out to his sides, and he has a smile on his face. His head is turned to the right, and his eyes are open, as if he is looking at the camera. The creature on the right is facing the camera, and its mouth is open. They are standing in front of an elevator, which is gray in color. The elevator is surrounded by brown wooden walls, and there is a white ceiling above the elevator."
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.1-Dev-Pov-DoorEye-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 & 2000 |
| Epoch | 12 | Save Every N Epochs | 1 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 13
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.1-Dev-Pov-DoorEye-LoRA"
trigger_word = "look in 2"
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
You should use look in 2 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