Usage

python
Copy code
import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.load_lora_weights("Mike0021/flux-dev-apple-emoji-lora", weight_name="pytorch_lora_weights.safetensors")
pipe.enable_model_cpu_offload()

image = pipe(
    "emj icon of a smiling dragon, centered on a plain white background",
    height=1024,
    width=1024,
    num_inference_steps=30,
    guidance_scale=4.0,
).images[0]

Training Summary

• Base model: black-forest-labs/FLUX.1-dev
• Trigger phrase: emj icon
• Dataset: 220 curated Apple emoji image/caption pairs prepared from iamcal/emoji-data
• Training images are RGB 512x512 composites on a plain white background
• Captions were metadata-derived per-image captions with the shared emj style trigger

Samples

The comparison sheet uses avatar-style prompts from the target Hub avatar generator prompt distribution. The right column is fofr/sdxl-emoji rendered with the same subject prompts where feasible.

Limitations

• This model is intended for research and experimentation.
• Apple emoji source assets are Apple IP; review the relevant terms before using derivatives commercially.
• The model may reproduce Apple-like visual conventions and should not be presented as an official Apple model.