Automotive 3D
Collection
4w's & 2w's β’ 2 items β’ Updated β’ 2
How to use strangerzonehf/Flux-Automotive-X2-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("strangerzonehf/Flux-Automotive-X2-LoRA")
prompt = "Automotive X2, a sleek black and silver motorcycle is parked against a gray backdrop. The motorcycle has a black seat, a black headlight, and a black handlebar. The front wheel of the motorcycle is adorned with a yellow emblem, adding a pop of color to the otherwise monochromatic scene. The bikes exhaust pipes are visible, adding texture to the composition. The backdrop is a solid gray, creating a stark contrast to the motorcycle."
image = pipe(prompt).images[0]





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 | 17 & 2100 |
| Epoch | 13 | Save Every N Epochs | 1 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 12
| Dimensions | Aspect Ratio | Recommendation |
|---|---|---|
| 1280 x 832 | 3:2 | Best |
| 1024 x 1024 | 1:1 | Default |
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-Automotive-X2-LoRA"
trigger_word = "Automotive X2"
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
You should use Automotive X2 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