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("fofr/flux-tesla-robovan")
prompt = "A photo of a Tesla ROBOVAN. Futuristic streamlined vehicle parked on a city street, sleek aerodynamic design, silver and black color scheme with horizontal stripes. Next to the Golden Gate bridge"
image = pipe(prompt).images[0]Flux Tesla Robovan
- Prompt
- A photo of a Tesla ROBOVAN. Futuristic streamlined vehicle parked on a city street, sleek aerodynamic design, silver and black color scheme with horizontal stripes. Next to the Golden Gate bridge
Run on Replicate:
https://replicate.com/fofr/flux-tesla-robovan
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
Trigger words
You should use ROBOVAN to trigger the image generation.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('fofr/flux-tesla-robovan', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
- Downloads last month
- 5
Model tree for fofr/flux-tesla-robovan
Base model
black-forest-labs/FLUX.1-dev