File size: 2,712 Bytes
4f03424 | 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 | """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):
# Preserve all visual encoder, embeddings, norms, modulation and boundary layers.
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
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