File size: 2,861 Bytes
c28ffff | 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 | """Resolve original or size-bounded Wan components without importing torch.
DiffSynth accepts one string or a list of shard paths per ModelConfig. Do not
pass an index JSON to its binary loader or treat each shard as a separate model.
WAN_MODEL_ROOT may point to an independently downloaded model bundle.
"""
import json
import os
from pathlib import Path
from project_paths import project_path
def wan_base_root(root=None):
return str(Path(root or os.environ.get('WAN_MODEL_ROOT') or
project_path('models/Wan2.1-VACE-1.3B')).expanduser().resolve())
def indexed_shards(index_path):
index_path = Path(index_path)
index = json.loads(index_path.read_text())
mapping = index.get('weight_map')
if not isinstance(mapping, dict) or not mapping:
raise ValueError('empty or invalid model weight index: ' + str(index_path))
names = set()
for name in mapping.values():
if not isinstance(name, str) or Path(name).name != name or not name.endswith('.safetensors'):
raise ValueError('unsafe shard path in model index')
names.add(name)
paths = [index_path.parent / name for name in sorted(names)]
for path in paths:
if not path.is_file():
raise FileNotFoundError('missing model shard: ' + str(path))
return [str(path) for path in paths]
def component_path(root, original, indices, strict=False):
original_path = root / original
if original_path.is_file():
return str(original_path)
for name in indices:
index_path = root / name
if index_path.is_file():
return indexed_shards(index_path)
if strict:
raise FileNotFoundError('missing original or indexed model component: ' + str(original_path))
# Preserve historical command defaults when no weights are present (e.g. dry-run).
return str(original_path)
def wan_model_paths(root=None, strict=False):
root = Path(wan_base_root(root))
return [
component_path(root, 'diffusion_pytorch_model.safetensors', (
'transformer/diffusion_pytorch_model.safetensors.index.json',
'diffusion_pytorch_model.safetensors.index.json',
), strict),
component_path(root, 'models_t5_umt5-xxl-enc-bf16.pth', (
'text_encoder/model.safetensors.index.json',
'text_encoder/models_t5_umt5-xxl-enc-bf16.safetensors.index.json',
'models_t5_umt5-xxl-enc-bf16.safetensors.index.json',
), strict),
component_path(root, 'Wan2.1_VAE.pth', (), strict),
]
def wan_tokenizer_path(root=None, strict=False):
path = Path(wan_base_root(root)) / 'google/umt5-xxl'
if strict and not (path / 'tokenizer.json').is_file() and not (path / 'spiece.model').is_file():
raise FileNotFoundError('missing Wan umT5 tokenizer: ' + str(path))
return str(path)
|