Automotive 3D
Collection
4w's & 2w's β’ 2 items β’ Updated β’ 2
How to use strangerzonehf/Flux-Automotive-X1-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-X1-LoRA")
prompt = "Automotive X1, Captured at eye-level on a dark night, a yellow jeep is parked on a dirt road. The jeeps front bumper is adorned with a black grille, and a yellow headlight. A green inflatable raft is on top of the jeep, with the word \"Tropor\" written in white on the side. The front of the vehicle is covered in dirt, and there are rocks and grass on the ground."
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 | 25 & 2900 |
| Epoch | 15 | Save Every N Epochs | 1 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 20
| 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-X1-LoRA"
trigger_word = "Automotive X1"
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
You should use Automotive X1 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