| """Local, reproducible model identities and safetensors inspection.""" |
| import hashlib |
| import json |
| import math |
| import struct |
| from pathlib import Path |
|
|
| SOURCE = Path('/Volumes/ZX6 1TB/Qwen-Image-2.1/models/bf16') |
| REVISION = 'b3179ad355be050328e483a9dfdd9e60cd62adfa' |
| RUNTIME_REVISION = '95b01ccad2d9f65a9e87f6a87bd1c5df69626261' |
| NOTICE = ('Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) 2026 ' |
| 'Hangzhou Tongyi Laboratory Technology Co., Ltd. All Rights Reserved.') |
| MODIFICATION = ('Modified by ixim / iximbox for Image21-MLX: converted from the pinned ' |
| 'BF16 source to MLX layout; eligible linear weights use groupwise affine ' |
| 'quantization. See conversion.json for precision and exceptions. Built with Qwen.') |
|
|
| def write_json(path, value): |
| path = Path(path) |
| path.parent.mkdir(parents=True, exist_ok=True) |
| path.write_text(json.dumps(value, ensure_ascii=False, indent=2) + '\n') |
|
|
| def sha256(path): |
| with Path(path).open('rb') as f: |
| return hashlib.file_digest(f, 'sha256').hexdigest() |
|
|
| def header(path): |
| with Path(path).open('rb') as f: |
| length = struct.unpack('<Q', f.read(8))[0] |
| if length > 100_000_000: |
| raise ValueError('Invalid safetensors header') |
| return json.loads(f.read(length)) |
|
|
| def tensors(root): |
| for path in sorted(Path(root).glob('*.safetensors')): |
| if path.name.startswith('._'): |
| continue |
| for name, info in header(path).items(): |
| if name != '__metadata__': |
| yield path, name, info |
|
|
| def eligible(component, name, shape): |
| |
| if len(shape) != 2 or shape[-1] % 64 or not name.endswith('.weight'): |
| return False |
| if component == 'transformer': |
| return name.startswith('transformer_blocks.') and ('.attn.to_' in name or '.img_mlp.' in name) |
| if component == 'text_encoder': |
| return 'language_model' in name and '.layers.' in name and ('.self_attn.' in name or '.mlp.' in name) |
| return False |
|
|
| def inventory(root): |
| result = {} |
| for component in ('transformer', 'text_encoder', 'vae'): |
| rows = list(tensors(Path(root) / component)) |
| params = sum(math.prod(v['shape']) for _, _, v in rows) |
| qparams = sum(math.prod(v['shape']) for _, n, v in rows if eligible(component, n, v['shape'])) |
| nbytes = sum(v['data_offsets'][1] - v['data_offsets'][0] for _, _, v in rows) |
| result[component] = dict(parameters=params, quantizable_parameters=qparams, tensor_bytes=nbytes, |
| dtypes=sorted({v['dtype'] for _, _, v in rows})) |
| return result |
|
|
