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

py/routes/stats_routes.py

@@ -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())
                 }
             })
             

static/js/statistics.js

@@ -240,6 +240,9 @@ export class StatisticsManager {
         
         // Storage efficiency chart
         this.createStorageEfficiencyChart();
+        
+        // Model types chart (Collection tab)
+        this.createModelTypesChart();
     }
 
     createCollectionPieChart() {
@@ -554,6 +557,68 @@ export class StatisticsManager {
         });
     }
 
+    createModelTypesChart() {
+        const ctx = document.getElementById('modelTypesChart');
+        if (!ctx || !this.data.collection || !this.data.collection.model_types_distribution) return;
+
+        const distribution = this.data.collection.model_types_distribution;
+        const typeDisplayNames = {
+            lora: 'LoRA',
+            locon: 'LyCORIS',
+            dora: 'DoRA',
+            checkpoint: 'Checkpoint',
+            diffusion_model: 'Diffusion Model',
+            embedding: 'Embeddings'
+        };
+
+        const colorPalette = {
+            lora: 'oklch(68% 0.28 256)',
+            locon: 'oklch(68% 0.25 190)',
+            dora: 'oklch(68% 0.25 330)',
+            checkpoint: 'oklch(68% 0.28 45)',
+            diffusion_model: 'oklch(68% 0.25 280)',
+            embedding: 'oklch(68% 0.25 120)'
+        };
+
+        const labels = Object.keys(distribution).map(k => typeDisplayNames[k] || k);
+        const values = Object.values(distribution);
+        const colors = Object.keys(distribution).map(k => colorPalette[k] || 'oklch(68% 0.15 0)');
+
+        const data = {
+            labels: labels,
+            datasets: [{
+                data: values,
+                backgroundColor: colors,
+                borderColor: getComputedStyle(document.documentElement).getPropertyValue('--border-color'),
+                borderWidth: 2
+            }]
+        };
+
+        this.charts.modelTypes = new Chart(ctx, {
+            type: 'doughnut',
+            data: data,
+            options: {
+                responsive: true,
+                maintainAspectRatio: false,
+                plugins: {
+                    legend: {
+                        position: 'bottom'
+                    },
+                    tooltip: {
+                        callbacks: {
+                            label: (context) => {
+                                const total = context.dataset.data.reduce((a, b) => a + b, 0);
+                                const value = context.parsed;
+                                const pct = ((value / total) * 100).toFixed(1);
+                                return ` ${context.label}: ${value} (${pct}%)`;
+                            }
+                        }
+                    }
+                }
+            }
+        });
+    }
+
     async initializeLists() {
         const listTypes = [
             { type: 'lora', containerId: 'topLorasList' },