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fix(stats): implement Model Types chart in Collection tab with correct type distribution

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Will Miao
2026-06-18 06:48:46 +08:00
parent 8314b9bedb
commit b7721866e5
2 changed files with 84 additions and 1 deletions
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20
py/routes/stats_routes.py
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@@ -11,6 +11,8 @@ from ..config import config
from ..services.settings_manager import get_settings_manager
from ..services.server_i18n import server_i18n
from ..services.service_registry import ServiceRegistry
from ..services.model_query import normalize_sub_type, resolve_sub_type
from ..utils.constants import VALID_LORA_SUB_TYPES, VALID_CHECKPOINT_SUB_TYPES
from ..utils.usage_stats import UsageStats
logger = logging.getLogger(__name__)
@@ -140,6 +142,21 @@ class StatsRoutes:
# Get usage statistics
usage_data = await self.usage_stats.get_stats()
# CivitAI model type distribution across all model types
# Use the same logic as the filter panel: normalize_sub_type(resolve_sub_type(entry))
# with sub-type validation per model type
model_types_counter: Counter[str] = Counter()
for entry in lora_cache.raw_data:
ntype = normalize_sub_type(resolve_sub_type(entry))
if ntype and ntype in VALID_LORA_SUB_TYPES:
model_types_counter[ntype] += 1
for entry in checkpoint_cache.raw_data:
ntype = normalize_sub_type(resolve_sub_type(entry))
if ntype and ntype in VALID_CHECKPOINT_SUB_TYPES:
model_types_counter[ntype] += 1
# Embeddings: always count as "embedding" regardless of CivitAI sub-type
model_types_counter['embedding'] = len(embedding_cache.raw_data)
return web.json_response({
'success': True,
'data': {
@@ -154,7 +171,8 @@ class StatsRoutes:
'total_generations': usage_data.get('total_executions', 0),
'unused_loras': self._count_unused_models(lora_cache.raw_data, usage_data.get('loras', {})),
'unused_checkpoints': self._count_unused_models(checkpoint_cache.raw_data, usage_data.get('checkpoints', {})),
'unused_embeddings': self._count_unused_models(embedding_cache.raw_data, usage_data.get('embeddings', {}))
'unused_embeddings': self._count_unused_models(embedding_cache.raw_data, usage_data.get('embeddings', {})),
'model_types_distribution': dict(model_types_counter.most_common())
}
})