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08d72be | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | #!/usr/bin/env python3
"""Run one Mage-Flow Edit-Turbo XPO3 image edit."""
from __future__ import annotations
import argparse
import json
import os
from pathlib import Path
import sys
import time
from PIL import Image
ROOT = Path(__file__).resolve().parent
for path in (ROOT / "runtime", ROOT / "vendor"):
sys.path.insert(0, str(path))
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("reference", type=Path)
parser.add_argument("instruction")
parser.add_argument("--output", type=Path, default=Path("edited.png"))
parser.add_argument("--seed", type=int, default=1)
parser.add_argument("--max-size", type=int, default=1024)
parser.add_argument("--disable-fused-gelu-up", action="store_true")
parser.add_argument("--disable-fp4-bridge", action="store_true")
parser.add_argument("--disable-accelerated-attention", action="store_true")
parser.add_argument("--disable-direct-hnd", action="store_true")
parser.add_argument("--report", type=Path)
return parser.parse_args()
def main() -> int:
args = parse_args()
os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8")
import torch
from portable_turbo_runtime import (
close_pipeline_optimization_runtimes,
generation_optimization_context,
load_pipeline_from_files,
transformer_config,
)
if not torch.cuda.is_available():
raise RuntimeError("XPO3 requires an NVIDIA CUDA GPU")
torch.cuda.set_device(0)
properties = torch.cuda.get_device_properties(0)
if (properties.major, properties.minor) != (12, 0):
raise RuntimeError(
"this XPO3 build requires an NVIDIA Blackwell SM120 GPU; "
f"found compute capability {properties.major}.{properties.minor}"
)
diffusion_model = (
ROOT / "Mage-Flow-Edit-Turbo-XPO3-NVFP4.safetensors"
)
text_encoder = ROOT / "qwen3vl_4b_fp8_scaled.safetensors"
vae = ROOT / "Mage-Flow-VAE.safetensors"
config = transformer_config(diffusion_model)
profile = config["quantization_config"]["xpo3_runtime_profile"]
bridge_blocks = ",".join(
sorted(
profile["fp4_bridge_scales"],
key=lambda value: int(value),
)
)
attention_steps = ",".join(
str(value) for value in profile["attention_steps"]
)
attention_blocks = ",".join(
str(value) for value in profile["attention_blocks"]
)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.cuda.reset_peak_memory_stats(0)
started = time.perf_counter()
pipe, load_report = load_pipeline_from_files(
diffusion_model=diffusion_model,
text_encoder=text_encoder,
vae=vae,
support_root=ROOT / "resources" / "turbo",
fused_gelu_library=ROOT / "runtime/libmage_nvfp4_gelu_up.so",
bridge_up_library=ROOT / "runtime/libmage_nvfp4_bridge_up.so",
bridge_down_library=(
ROOT / "runtime/libmage_nvfp4_prequantized_down.so"
),
torch=torch,
)
for module in (
pipe.model.txt_enc,
pipe.model.transformer,
pipe.model.vae,
):
module.to("cuda:0")
torch.cuda.synchronize()
load_seconds = time.perf_counter() - started
reference = Image.open(args.reference).convert("RGB")
generation_started = time.perf_counter()
with torch.inference_mode():
with generation_optimization_context(
pipe=pipe,
torch=torch,
enable_fused_gelu_up=not args.disable_fused_gelu_up,
enable_fp4_bridge=not args.disable_fp4_bridge,
bridge_blocks=bridge_blocks,
enable_attention_accel=(
not args.disable_accelerated_attention
),
enable_direct_hnd=not args.disable_direct_hnd,
attention_steps=attention_steps,
attention_blocks=attention_blocks,
steps=int(profile["steps"]),
static_shift=float(profile["static_shift"]),
cfg=float(profile["cfg"]),
) as feature_manifest:
images = pipe.edit(
[args.instruction],
[[reference]],
seeds=[args.seed],
steps=int(profile["steps"]),
cfg=float(profile["cfg"]),
max_size=args.max_size,
static_shift=float(profile["static_shift"]),
prompt_template="mage-flow-edit",
vl_cond_long_edge=384,
)
torch.cuda.synchronize()
generation_seconds = time.perf_counter() - generation_started
if not feature_manifest["restoration"]["all_restored"]:
raise RuntimeError("XPO3 feature state did not restore after editing")
args.output.parent.mkdir(parents=True, exist_ok=True)
images[0].save(args.output)
report = {
"schema_version": "mage-flow-edit-turbo-xpo3-cli-v1",
"reference": str(args.reference.resolve()),
"instruction": args.instruction,
"output": str(args.output.resolve()),
"seed": args.seed,
"max_size": args.max_size,
"gpu": {
"name": properties.name,
"compute_capability": (
f"{properties.major}.{properties.minor}"
),
},
"load_and_placement_seconds": load_seconds,
"generation_seconds": generation_seconds,
"peak_allocated_bytes": int(torch.cuda.max_memory_allocated(0)),
"load": load_report,
"active_feature_manifest": feature_manifest,
}
if args.report is not None:
args.report.parent.mkdir(parents=True, exist_ok=True)
args.report.write_text(
json.dumps(report, indent=2, sort_keys=True) + "\n",
encoding="utf-8",
)
print(json.dumps(report, indent=2, sort_keys=True))
cleanup = close_pipeline_optimization_runtimes(pipe)
if not cleanup["all_closed_without_error"]:
raise RuntimeError(f"XPO3 native cleanup failed: {cleanup}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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