import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/flux-3-image-klein-base", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("diffusers-modular/flux3-tarot-lora")
prompt = "a trtcrd of a person giving a talk on a stage under a glowing emblem, \"the speaker\" arcana, tarot style"
image = pipe(prompt).images[0]FLUX.3 Tarot LoRA
A Rider-Waite tarot-style DreamBooth LoRA for the FLUX.3 image (klein) model, trained with the diffusers DreamBooth FLUX.3 trainer. Trigger the style with trtcrd, e.g. "a trtcrd of ..., tarot style".
- Prompt
- a trtcrd of a person giving a talk on a stage under a glowing emblem, "the speaker" arcana, tarot style
- Prompt
- a trtcrd of a man carving meat from a tall vertical rotisserie that reads "Döner", "the carver" arcana, tarot style
- Prompt
- a trtcrd of a dog eating a big bowl of spaghetti at a dinner table, "the glutton" arcana, tarot style
- Prompt
- a trtcrd of an astronaut planting a flag on a tiny planet among stars, "the explorer" arcana, tarot style
Inference
import torch
from diffusers.modular_pipelines import Flux3AutoBlocks
pipe = Flux3AutoBlocks().init_pipeline("black-forest-labs/flux-3-image-klein-base")
pipe.load_components(dtype=torch.bfloat16)
pipe.to("cuda")
pipe.load_lora_weights("diffusers-modular/flux3-tarot-lora", weight_name="pytorch_lora_weights.safetensors")
image = pipe(
prompt="a trtcrd of a dog eating a big bowl of spaghetti, tarot style",
height=1344, width=800, num_inference_steps=28, negative_prompt="",
max_sequence_length=512, text_encoder_out_layers=(4, 8, 12, 16, 20, 24, 28, 32),
output="images",
)[0]
image.save("tarot.png")
Trigger
Use trtcrd in the prompt: "a trtcrd of {your subject}, tarot style".
Training
- Base model:
black-forest-labs/flux-3-image-klein-base - Dataset: multimodalart/1920-raider-waite-tarot-public-domain
- 1500 steps · seed 0 · resolution 1024 · aspect-ratio buckets
- Default weights (
pytorch_lora_weights.safetensors): AdamW (8-bit), rank 16, lr 1e-4
Sweep variants
This repo also includes the configs from a small sweep (one setting changed vs the default). Load one with weight_name="<folder>/pytorch_lora_weights.safetensors":
| variant | change | weight_name |
|---|---|---|
| default | AdamW, rank 16 | pytorch_lora_weights.safetensors |
| rank 8 | rank → 8 | rank8/pytorch_lora_weights.safetensors |
| rank 32 | rank → 32 | rank32/pytorch_lora_weights.safetensors |
| more layers | + modulation layers | more-layers/pytorch_lora_weights.safetensors |
| prodigy | Prodigy optimizer, lr 1 | prodigy/pytorch_lora_weights.safetensors |
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Model tree for diffusers-modular/flux3-tarot-lora
Base model
black-forest-labs/flux-3-image-klein-base