FLUX.1-dev LoRAs
Collection
The prior-gen back-catalogue — FLUX.1-dev character & style LoRAs. • 55 items • Updated
How to use ms2stationthis/hffinal_miladyii_30 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("ms2stationthis/hffinal_miladyii_30")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]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("ms2stationthis/hffinal_miladyii_30")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]This LoRA fine-tunes FLUX.1-dev for custom image generation. Use the trigger word miladyii in your prompts to activate the trained style. Trained using the StationThis pipeline.
Trigger word: miladyii
.safetensors file from the Files tabComfyUI/models/loras/0.8-1.0miladyii in your promptimport torch
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
torch_dtype=torch.bfloat16
)
pipe.load_lora_weights("ms2stationthis/hffinal_miladyii_30")
pipe.to("cuda")
image = pipe(
prompt="miladyii portrait, soft lighting, detailed",
guidance_scale=3.5-4.0,
num_inference_steps=20-30,
generator=torch.Generator("cuda").manual_seed(42)
).images[0]
image.save("output.png")
| Parameter | Value |
|---|---|
| LoRA Strength | 0.8-1.0 |
| Guidance Scale | 3.5-4.0 |
| Inference Steps | 20-30 |
| Resolution | 1024x1024 |
miladyii teary black eyesmiladyii pale skinmiladyii and longmiladyii gentle smile. at the bottom of the imagemiladyiiTrained using StationThis — an AI creative platform powered by $MS2. Train your own LoRAs via @stationthisbot on Telegram.
Generated by StationThis Training Pipeline
Base model
black-forest-labs/FLUX.1-dev