이미지 생성 — Mac에서 돌리는 확산 모델

# Run with mflux via its console script. (`python -m mflux.generate`
# was the old entry point — src/mflux/generate.py was removed 2025-12,
# so the module form no longer exists.)
# black-forest-labs/FLUX.1-schnell is a GATED repo:
# accept its terms on the Hugging Face page while logged in and have a
# token in place (`hf auth login`, or HF_TOKEN) or the first run
# 401s. That first run then pulls the full bf16 weights (~34 GB) into
# ~/.cache/huggingface/hub/; --quantize 4 quantizes them on the fly as
# they load. As written this command fetches no pre-quantized artifact —
# pre-quantized copies do exist on Hugging Face, and `mflux-save` writes
# one locally.
mflux-generate \
  --model schnell \
  --quantize 4 \
  --steps 4 \
  --width 1024 --height 1024 \
  --seed 42 \
  --prompt "A red panda data scientist debugging code at 3am, anime style, soft lighting." \
  --output ./red-panda.png

# Wall-clock varies by chip GENERATION — published reference points:
#   M4 Max (128 GB) : ~15 s generation for 1024x1024 schnell at 2 steps
#                     (~38 s total with model load) — mflux issue #92
#   M3 Pro          : ~30 s at 4-bit — mflux's earlier README table
# Expect roughly 2x the 2-step time at --steps 4, and budget about a
# minute per image on older or lower-tier chips.

# Verified against mflux 0.18 — the current programmatic API.
# (Older tutorials show `Flux1.from_alias(...)` + `Config(...)`;
# that shape predates 0.18 and no longer imports.)

from mflux.models.common.config.model_config import ModelConfig
from mflux.models.flux.variants.txt2img.flux import Flux1

flux = Flux1(
    quantize=4,
    model_config=ModelConfig.schnell(),
)

image = flux.generate_image(
    seed=42,
    prompt="A red panda data scientist debugging code at 3am, anime style.",
    num_inference_steps=4,
    height=1024,
    width=1024,
)

image.save("red-panda.png", export_json_metadata=True)