Chunte/huggy-style-v1-lora

Huggy Style v1 - FLUX DreamBooth LoRA

A LoRA adapter for FLUX.1-dev trained with DreamBooth to generate Huggy — the HuggingFace mascot character.

Character Description

Huggy is a yellow circular character with:

• Round body (no arms, legs, or feet)
• Two floating hands
• Orange outlines (no dark black outlines)
• Clean flat vector art style with edge shadows
• Expressive face with various emotions

Trigger Word

Use huggy_style_v1 in your prompts to activate the character.

Usage

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()

# Load LoRA
pipe.load_lora_weights("Chunte/huggy-style-v1-lora")

image = pipe(
    prompt="a huggy_style_v1 mascot wearing a pirate hat, waving, happy",
    num_inference_steps=28,
    guidance_scale=3.5,
    width=768,
    height=768,
    generator=torch.Generator("cpu").manual_seed(42),
).images[0]
image.save("huggy.png")

Prompt Tips

• Always include huggy_style_v1 as the trigger word
• Describe what varies — costumes, poses, expressions, props
• Don't describe the character's base appearance (yellow, circular, etc.) — the LoRA already knows this
• Example: a huggy_style_v1 mascot wearing a santa hat, holding a gift, smiling

Checkpoints

Multiple checkpoints are available if the final weights are overfitting:

Checkpoint	Use Case
checkpoint-500	Early training — more creative, less accurate character
checkpoint-1000	Moderate — good balance for some use cases
checkpoint-1500	Strong character identity with good generalization
final (default)	Strongest character identity (2000 steps)

Load a specific checkpoint:

pipe.load_lora_weights("Chunte/huggy-style-v1-lora", subfolder="checkpoint-1000")

Training Details

Parameter	Value
Base model	FLUX.1-dev
Method	DreamBooth LoRA
Training script	train_dreambooth_lora_flux.py (diffusers v0.37.0)
Dataset	72 hand-captioned images (1024x1024, white background)
Resolution	768
LoRA rank	32
Learning rate	1e-4 (constant scheduler)
Warmup steps	100
Training steps	2000
Batch size	1 (gradient accumulation: 4, effective batch: 4)
Mixed precision	bf16
Guidance scale	1 (recommended for FLUX training)
Gradient checkpointing	Enabled
Hardware	NVIDIA L40S (48GB VRAM)
Final loss	0.021

Sample Images

License

This LoRA adapter inherits the FLUX.1-dev Non-Commercial License.