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import argparse
import json
import logging
from pathlib import Path
def model_arguments(parser):
parser.add_argument(
"--model",
required=True,
help="Downloaded variant or original image model directory",
)
parser.add_argument(
"--text-encoder",
required=True,
help="Original MiniMax-H3/text_encoder directory",
)
parser.add_argument("--cache-dir", default="cache")
def main(argv=None):
parser = argparse.ArgumentParser(
prog="h3-mlx", description="MiniMax H3 Image on MLX/Metal"
)
parser.add_argument("--verbose", action="store_true")
sub = parser.add_subparsers(dest="command", required=True)
sub.add_parser(
"doctor", help="Report Metal availability, memory and installed versions"
)
download = sub.add_parser(
"download", help="Download one variant and shared components"
)
download.add_argument(
"--variant", choices=("bf16", "packed12", "q8", "q4"), default="packed12"
)
download.add_argument("--model-dir", required=True)
download.add_argument(
"--text-encoder-path", help="Reuse an existing text encoder directory"
)
download.add_argument("--skip-text-encoder", action="store_true")
download.add_argument("--local-files-only", action="store_true")
download.add_argument("--catalog", help="Use an alternate release catalog JSON")
download.add_argument(
"--verify", action="store_true", help="Rehash all downloaded model files"
)
generate = sub.add_parser("generate", help="Run six native PDD steps locally")
model_arguments(generate)
generate.add_argument("prompt")
generate.add_argument("--width", type=int, default=2048)
generate.add_argument("--height", type=int, default=2048)
generate.add_argument("--seed", type=int, default=731)
generate.add_argument("--min-generation-area", type=int, default=4194304)
generate.add_argument(
"--rng",
choices=("torch", "mlx"),
default="torch",
help="torch CPU matches original native noise; mlx is a different seed stream",
)
generate.add_argument("--output", default="outputs/image.png")
generate.add_argument(
"--image",
action="append",
default=[],
help="Ordered edit reference; repeat up to sixteen times",
)
generate.add_argument("--dit-cache-gb", type=float, default=38)
generate.add_argument(
"--quantize",
type=int,
choices=(4, 8),
help="Experimental weight-only DiT quantization; AdaLN/FP32 islands stay native",
)
generate.add_argument("--encoder-quantize", type=int, choices=(4, 8))
generate.add_argument("--group-size", type=int, choices=(32, 64, 128), default=64)
generate.add_argument("--mlp-chunk", type=int, default=0)
generate.add_argument(
"--attention-chunk",
type=int,
default=0,
help="0 uses one memory-efficient Metal SDPA call",
)
generate.add_argument(
"--kernel",
choices=("native", "metal", "metal-qk", "metal-exact", "metal-qk-exact"),
default="native",
help="metal-exact fuses M5 norms and Q/K/RoPE; metal-qk-exact keeps native full-width norms. Both retain native fallback",
)
generate.add_argument(
"--tune-matmul",
action="store_true",
help="Use measured M5 BF16 FC2 tiles for 4096–8192 joint tokens; native fallback otherwise",
)
generate.add_argument(
"--norm-threads", type=int, choices=(128, 256, 512), default=256
)
generate.add_argument("--vae-batch", type=int, default=1)
generate.add_argument("--compile-vae", action="store_true")
generate.add_argument("--vae-backend", choices=("mlx", "coreml"), default="mlx")
generate.add_argument(
"--coreml-package",
help="Fixed-tile decoder exported by experiments.v2_coreml_vae",
)
generate.add_argument(
"--coreml-compute-units",
choices=("CPU_ONLY", "CPU_AND_GPU", "CPU_AND_NE", "ALL"),
default="CPU_AND_GPU",
)
generate.add_argument(
"--vae-dtype", choices=("bfloat16", "float32"), default="bfloat16"
)
generate.add_argument(
"--experimental-steps", help="Original head indices, e.g. 0,2,4; changes images"
)
generate.add_argument("--save-trace", action="store_true")
generate.add_argument(
"--no-refiner-cache",
action="store_true",
help="Recompute refined conditioning instead of using its exact disk cache",
)
generate.add_argument(
"--prefetch-blocks",
type=int,
default=0,
help="Overlap weight loading with GPU work, with at most N blocks in flight",
)
generate.add_argument(
"--async-blocks",
action="store_true",
help="Enqueue each block asynchronously; use --eval-every to bound in-flight work",
)
generate.add_argument(
"--compile-blocks",
action="store_true",
help="Compile each DiT block and fuse elementwise operators",
)
generate.add_argument(
"--eval-every",
type=int,
default=1,
help="Synchronize every N DiT blocks; 0 evaluates the whole step",
)
generate.add_argument(
"--keep-refiners",
action="store_true",
help="Retain token-refiner weights across resident requests",
)
generate.add_argument(
"--wired-gb",
type=float,
help="Process-local MLX wired memory; capped at Metal's recommended set",
)
generate.add_argument(
"--weight-storage",
choices=("bf16", "packed12"),
default="bf16",
help="packed12 caches exact BF16 weights in a smaller representation",
)
generate.add_argument(
"--quantized-core", help="Preconverted DiT core from python -m h3_mlx.convert"
)
generate.add_argument(
"--offload-after-denoise",
action="store_true",
help="Release DiT before VAE for smaller-memory Macs",
)
args = parser.parse_args(argv)
logging.basicConfig(
level=logging.DEBUG if args.verbose else logging.INFO,
format="%(asctime)s %(message)s",
datefmt="%H:%M:%S",
)
if args.command == "download":
from .hub import download_variant
result = download_variant(
args.variant,
args.model_dir,
encoder_path=args.text_encoder_path,
skip_encoder=args.skip_text_encoder,
local_files_only=args.local_files_only,
catalog_path=args.catalog,
verify=args.verify,
)
print(json.dumps(result, indent=2))
return
if args.command == "doctor":
from .runtime import configure_memory, environment
configure_memory()
print(json.dumps(environment(), indent=2))
return
if args.command == "generate":
for p in (args.model, args.text_encoder):
if not Path(p).is_dir():
parser.error(f"Model directory does not exist: {p}")
if (
args.mlp_chunk < 0
or args.attention_chunk < 0
or args.vae_batch < 1
or args.dit_cache_gb < 0
or args.eval_every < 0
or args.prefetch_blocks < 0
):
parser.error(
"Chunk sizes/cache must be nonnegative and VAE batch must be positive"
)
if len(args.image) > 16:
parser.error("MiniMax-H3-Image accepts at most sixteen reference images")
steps = (
None
if args.experimental_steps is None
else [int(s) for s in args.experimental_steps.split(",")]
)
from .runtime import Pipeline
pipe = Pipeline(
args.model,
args.text_encoder,
cache_dir=args.cache_dir,
dit_cache_gb=args.dit_cache_gb,
bits=args.quantize,
encoder_bits=args.encoder_quantize,
group_size=args.group_size,
mlp_chunk=args.mlp_chunk,
query_chunk=args.attention_chunk,
kernel=args.kernel,
threads=args.norm_threads,
vae_batch=args.vae_batch,
vae_dtype=args.vae_dtype,
keep_resident=not args.offload_after_denoise,
quantized_path=args.quantized_core,
compile_blocks=args.compile_blocks,
eval_every=args.eval_every,
keep_refiners=args.keep_refiners,
wired_gb=args.wired_gb,
weight_storage=args.weight_storage,
vae_compile=args.compile_vae,
vae_backend=args.vae_backend,
coreml_package=args.coreml_package,
coreml_compute_units=args.coreml_compute_units,
async_blocks=args.async_blocks,
cache_refined_text=not args.no_refiner_cache,
prefetch_blocks=args.prefetch_blocks,
tune_matmul=args.tune_matmul,
)
_, record = pipe.generate(
args.prompt,
width=args.width,
height=args.height,
seed=args.seed,
min_area=args.min_generation_area,
rng=args.rng,
output=args.output,
images=args.image,
steps=steps,
save_trace=args.save_trace,
)
print(
json.dumps(
{
"output": record["output"],
"timings": record["timings"],
"memory": record["memory"],
},
indent=2,
)
)
if __name__ == "__main__":
main()
|