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-y2k")
prompt = "a photo of a transparent Y2K device"
image = pipe(prompt).images[0]Flux Y2K
- Prompt
- a photo of a transparent Y2K device
- Prompt
- a Y2K portrait photo of a cyberpunk
Run on Replicate:
https://replicate.com/fofr/flux-y2k
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
Trigger words
You should use Y2K 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-y2k', 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
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Model tree for fofr/flux-y2k
Base model
black-forest-labs/FLUX.1-dev