diff --git a/test/test-detailer-api.py b/test/test-detailer-api.py
new file mode 100644
index 000000000..acc4622d7
--- /dev/null
+++ b/test/test-detailer-api.py
@@ -0,0 +1,653 @@
+#!/usr/bin/env python
+"""
+API tests for YOLO Detailer endpoints.
+
+Tests:
+- GET /sdapi/v1/detailers — model enumeration
+- POST /sdapi/v1/detect — object detection on test images
+- POST /sdapi/v1/txt2img — generation with detailer enabled
+
+Requires a running SD.Next instance with a model loaded.
+
+Usage:
+    python test/test-detailer-api.py [--url URL] [--image PATH]
+"""
+
+import io
+import os
+import sys
+import time
+import json
+import base64
+import argparse
+import requests
+import urllib3
+
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+# Reference model cover images with faces (best for detailer testing)
+FACE_TEST_IMAGES = [
+    'models/Reference/ponyRealism_V23.jpg',                               # realistic woman, clear face
+    'models/Reference/HiDream-ai--HiDream-I1-Fast.jpg',                   # realistic man, clear face + text
+    'models/Reference/stabilityai--stable-diffusion-xl-base-1.0.jpg',     # realistic woman portrait
+    'models/Reference/CalamitousFelicitousness--Anima-sdnext-diffusers.jpg',  # anime face (non-realistic test)
+]
+
+# Fallback images (no guaranteed faces)
+FALLBACK_IMAGES = [
+    'html/sdnext-robot-2k.jpg',
+    'html/favicon.png',
+]
+
+
+class DetailerAPITest:
+    """Test harness for YOLO Detailer API endpoints."""
+
+    def __init__(self, base_url, image_path=None, timeout=300):
+        self.base_url = base_url.rstrip('/')
+        self.test_images = {}  # name -> base64
+        self.timeout = timeout
+        self.results = {
+            'enumerate': {'passed': 0, 'failed': 0, 'skipped': 0, 'tests': []},
+            'detect': {'passed': 0, 'failed': 0, 'skipped': 0, 'tests': []},
+            'generate': {'passed': 0, 'failed': 0, 'skipped': 0, 'tests': []},
+            'detailer_params': {'passed': 0, 'failed': 0, 'skipped': 0, 'tests': []},
+        }
+        self._category = 'enumerate'
+        self._critical_error = None
+        self._load_images(image_path)
+
+    def _encode_image(self, path):
+        from PIL import Image
+        image = Image.open(path)
+        if image.mode == 'RGBA':
+            image = image.convert('RGB')
+        buf = io.BytesIO()
+        image.save(buf, 'JPEG')
+        return base64.b64encode(buf.getvalue()).decode(), image.size
+
+    def _load_images(self, image_path=None):
+        if image_path and os.path.exists(image_path):
+            b64, size = self._encode_image(image_path)
+            name = os.path.basename(image_path)
+            self.test_images[name] = b64
+            print(f"  Test image: {image_path} ({size})")
+            return
+
+        # Load all available face test images
+        for p in FACE_TEST_IMAGES:
+            if os.path.exists(p):
+                b64, size = self._encode_image(p)
+                name = os.path.basename(p)
+                self.test_images[name] = b64
+                print(f"  Loaded: {name} ({size[0]}x{size[1]})")
+
+        # Fallback if no face images found
+        if not self.test_images:
+            for p in FALLBACK_IMAGES:
+                if os.path.exists(p):
+                    b64, size = self._encode_image(p)
+                    name = os.path.basename(p)
+                    self.test_images[name] = b64
+                    print(f"  Fallback: {name} ({size[0]}x{size[1]})")
+                    break
+
+        if not self.test_images:
+            print("  WARNING: No test images found, detect tests will be skipped")
+
+    @property
+    def image_b64(self):
+        """Return the first available test image for backwards compat."""
+        if self.test_images:
+            return next(iter(self.test_images.values()))
+        return None
+
+    def _get(self, endpoint):
+        try:
+            r = requests.get(f'{self.base_url}{endpoint}', timeout=self.timeout, verify=False)
+            if r.status_code != 200:
+                return {'error': r.status_code, 'reason': r.reason}
+            return r.json()
+        except requests.exceptions.ConnectionError:
+            return {'error': 'connection_refused', 'reason': 'Server not running'}
+        except Exception as e:
+            return {'error': 'exception', 'reason': str(e)}
+
+    def _post(self, endpoint, data):
+        try:
+            r = requests.post(f'{self.base_url}{endpoint}', json=data, timeout=self.timeout, verify=False)
+            if r.status_code != 200:
+                return {'error': r.status_code, 'reason': r.reason}
+            return r.json()
+        except requests.exceptions.ConnectionError:
+            return {'error': 'connection_refused', 'reason': 'Server not running'}
+        except Exception as e:
+            return {'error': 'exception', 'reason': str(e)}
+
+    def record(self, passed, name, detail=''):
+        status = 'PASS' if passed else 'FAIL'
+        self.results[self._category]['passed' if passed else 'failed'] += 1
+        self.results[self._category]['tests'].append((status, name))
+        msg = f'  {status}: {name}'
+        if detail:
+            msg += f' ({detail})'
+        print(msg)
+
+    def skip(self, name, reason):
+        self.results[self._category]['skipped'] += 1
+        self.results[self._category]['tests'].append(('SKIP', name))
+        print(f'  SKIP: {name} ({reason})')
+
+    # =========================================================================
+    # Tests: Model Enumeration
+    # =========================================================================
+
+    def test_detailers_list(self):
+        """GET /sdapi/v1/detailers returns a list of available models."""
+        self._category = 'enumerate'
+        print("\n--- Detailer Model Enumeration ---")
+
+        data = self._get('/sdapi/v1/detailers')
+        if 'error' in data:
+            self.record(False, 'detailers_list', f"error: {data}")
+            self._critical_error = f"Server error: {data}"
+            return []
+
+        if not isinstance(data, list):
+            self.record(False, 'detailers_list', f"expected list, got {type(data).__name__}")
+            return []
+
+        self.record(True, 'detailers_list', f"{len(data)} models found")
+
+        # Verify each entry has expected fields
+        if len(data) > 0:
+            sample = data[0]
+            has_name = 'name' in sample
+            self.record(has_name, 'detailer_entry_has_name', f"sample: {sample}")
+            if not has_name:
+                self.record(False, 'detailer_entry_schema', "missing 'name' field")
+
+        return data
+
+    # =========================================================================
+    # Tests: Detection
+    # =========================================================================
+
+    def _validate_detect_response(self, data, label):
+        """Validate detection response schema and return detection count."""
+        expected_keys = ['classes', 'labels', 'boxes', 'scores']
+        for key in expected_keys:
+            if key not in data:
+                self.record(False, f'{label}_schema_{key}', f"missing '{key}'")
+                return -1
+
+        # All arrays should have the same length
+        lengths = [len(data[key]) for key in expected_keys]
+        all_same = len(set(lengths)) <= 1
+        if not all_same:
+            self.record(False, f'{label}_array_lengths', f"mismatched: {dict(zip(expected_keys, lengths))}")
+            return -1
+
+        n = lengths[0]
+
+        if n > 0:
+            # Scores should be in [0, 1]
+            scores_valid = all(0 <= s <= 1 for s in data['scores'])
+            if not scores_valid:
+                self.record(False, f'{label}_scores_range', f"scores: {data['scores']}")
+
+            # Boxes should be lists of 4 numbers
+            boxes_valid = all(isinstance(b, list) and len(b) == 4 for b in data['boxes'])
+            if not boxes_valid:
+                self.record(False, f'{label}_boxes_format', "bad box format")
+
+        return n
+
+    # Face detection models to try (in priority order)
+    FACE_MODELS = ['face-yolo8n', 'face-yolo8m', 'anzhc-face-1024-seg-8n']
+
+    def _pick_face_model(self, available_models):
+        """Pick the best face detection model from available ones."""
+        available_names = [m.get('name', '') for m in available_models] if available_models else []
+        for model in self.FACE_MODELS:
+            if model in available_names:
+                return model
+        return ''  # fall back to server default
+
+    def test_detect_all_images(self, available_models=None):
+        """POST /sdapi/v1/detect on each loaded test image with a face model."""
+        self._category = 'detect'
+        print("\n--- Detection Tests (per-image) ---")
+
+        if not self.test_images:
+            self.skip('detect_all', 'no test images')
+            return
+
+        if self._critical_error:
+            self.skip('detect_all', self._critical_error)
+            return
+
+        face_model = self._pick_face_model(available_models)
+        if face_model:
+            print(f"  Using face model: {face_model}")
+        else:
+            print("  No face model available, using server default")
+
+        total_detections = 0
+        any_face_found = False
+
+        for img_name, img_b64 in self.test_images.items():
+            short = img_name.replace('.jpg', '')[:40]
+            data = self._post('/sdapi/v1/detect', {'image': img_b64, 'model': face_model})
+
+            if 'error' in data:
+                self.record(False, f'detect_{short}', f"error: {data}")
+                continue
+
+            n = self._validate_detect_response(data, f'detect_{short}')
+            if n < 0:
+                continue
+
+            labels = data.get('labels', [])
+            scores = data.get('scores', [])
+            detail_parts = [f"{n} detections"]
+            if labels:
+                detail_parts.append(f"labels={labels}")
+            if scores:
+                detail_parts.append(f"top_score={max(scores):.3f}")
+
+            self.record(True, f'detect_{short}', ', '.join(detail_parts))
+            total_detections += n
+            if n > 0:
+                any_face_found = True
+
+        self.record(any_face_found, 'detect_found_faces',
+                    f"{total_detections} total detections across {len(self.test_images)} images")
+
+    def test_detect_with_model(self, model_name):
+        """POST /sdapi/v1/detect with a specific model on all images."""
+        if not self.test_images:
+            self.skip(f'detect_model_{model_name}', 'no test images')
+            return
+
+        total = 0
+        for _img_name, img_b64 in self.test_images.items():
+            data = self._post('/sdapi/v1/detect', {'image': img_b64, 'model': model_name})
+            if 'error' not in data:
+                total += len(data.get('scores', []))
+
+        self.record(True, f'detect_model_{model_name}', f"{total} detections across {len(self.test_images)} images")
+
+    # =========================================================================
+    # Tests: Generation with Detailer
+    # =========================================================================
+
+    def test_txt2img_with_detailer(self):
+        """POST /sdapi/v1/txt2img with detailer_enabled=True."""
+        self._category = 'generate'
+        print("\n--- Generation with Detailer ---")
+
+        if self._critical_error:
+            self.skip('txt2img_detailer', self._critical_error)
+            return
+
+        payload = {
+            'prompt': 'a photo of a person, face, portrait',
+            'negative_prompt': '',
+            'steps': 10,
+            'width': 512,
+            'height': 512,
+            'seed': 42,
+            'save_images': False,
+            'send_images': True,
+            'detailer_enabled': True,
+            'detailer_strength': 0.3,
+            'detailer_steps': 5,
+            'detailer_conf': 0.3,
+            'detailer_max': 3,
+        }
+
+        t0 = time.time()
+        # Detailer generation is multi-pass (generate + detect + inpaint per region), use longer timeout
+        try:
+            r = requests.post(f'{self.base_url}/sdapi/v1/txt2img', json=payload, timeout=600, verify=False)
+            if r.status_code != 200:
+                data = {'error': r.status_code, 'reason': r.reason}
+            else:
+                data = r.json()
+        except requests.exceptions.ConnectionError as e:
+            self.record(False, 'txt2img_detailer', f"connection error (is a model loaded?): {e}")
+            return
+        except requests.exceptions.ReadTimeout:
+            self.record(False, 'txt2img_detailer', 'timeout after 600s')
+            return
+        t1 = time.time()
+
+        if 'error' in data:
+            self.record(False, 'txt2img_detailer', f"error: {data} (ensure a model is loaded)")
+            return
+
+        # Should have images
+        has_images = 'images' in data and len(data['images']) > 0
+        self.record(has_images, 'txt2img_detailer_has_images', f"time={t1 - t0:.1f}s")
+
+        if has_images:
+            # Decode and verify image
+            from PIL import Image
+            img_data = data['images'][0].split(',', 1)[0]
+            img = Image.open(io.BytesIO(base64.b64decode(img_data)))
+            self.record(True, 'txt2img_detailer_image_valid', f"size={img.size}")
+
+        # Check info field for detailer metadata
+        if 'info' in data:
+            info = data['info'] if isinstance(data['info'], str) else json.dumps(data['info'])
+            has_detailer_info = 'detailer' in info.lower() or 'Detailer' in info
+            self.record(has_detailer_info, 'txt2img_detailer_metadata',
+                        'detailer info found in metadata' if has_detailer_info else 'no detailer metadata (detection may have found nothing)')
+
+    def test_txt2img_without_detailer(self):
+        """POST /sdapi/v1/txt2img baseline without detailer (sanity check)."""
+        if self._critical_error:
+            self.skip('txt2img_baseline', self._critical_error)
+            return
+
+        payload = {
+            'prompt': 'a simple landscape',
+            'steps': 5,
+            'width': 512,
+            'height': 512,
+            'seed': 42,
+            'save_images': False,
+            'send_images': True,
+        }
+
+        data = self._post('/sdapi/v1/txt2img', payload)
+        if 'error' in data:
+            self.record(False, 'txt2img_baseline', f"error: {data}")
+            return
+
+        has_images = 'images' in data and len(data['images']) > 0
+        self.record(has_images, 'txt2img_baseline', 'generation works without detailer')
+
+    # =========================================================================
+    # Tests: Per-Request Detailer Param Validation
+    # =========================================================================
+
+    def _txt2img(self, extra_params=None):
+        """Helper: generate a portrait with optional param overrides."""
+        payload = {
+            'prompt': 'a photo of a person, face, portrait',
+            'steps': 10,
+            'width': 512,
+            'height': 512,
+            'seed': 42,
+            'save_images': False,
+            'send_images': True,
+        }
+        if extra_params:
+            payload.update(extra_params)
+        try:
+            r = requests.post(f'{self.base_url}/sdapi/v1/txt2img', json=payload, timeout=600, verify=False)
+            if r.status_code != 200:
+                return {'error': r.status_code, 'reason': r.reason}
+            return r.json()
+        except requests.exceptions.ConnectionError as e:
+            return {'error': 'connection_refused', 'reason': str(e)}
+        except requests.exceptions.ReadTimeout:
+            return {'error': 'timeout', 'reason': 'timeout after 600s'}
+
+    def _decode_image(self, data):
+        """Decode first image from generation response into numpy array."""
+        import numpy as np
+        from PIL import Image
+        if 'images' not in data or len(data['images']) == 0:
+            return None
+        img_data = data['images'][0].split(',', 1)[0]
+        img = Image.open(io.BytesIO(base64.b64decode(img_data))).convert('RGB')
+        return np.array(img, dtype=np.float32)
+
+    def _pixel_diff(self, arr_a, arr_b):
+        """Mean absolute pixel difference between two images."""
+        import numpy as np
+        if arr_a is None or arr_b is None or arr_a.shape != arr_b.shape:
+            return -1.0
+        return float(np.abs(arr_a - arr_b).mean())
+
+    def _get_info(self, data):
+        """Extract info string from generation response."""
+        if 'info' not in data:
+            return ''
+        info = data['info']
+        return info if isinstance(info, str) else json.dumps(info)
+
+    def run_detailer_param_tests(self, available_models=None):
+        """Verify per-request detailer params change the output."""
+        self._category = 'detailer_params'
+        print("\n--- Per-Request Detailer Param Validation ---")
+
+        if self._critical_error:
+            self.skip('detailer_params_all', self._critical_error)
+            return
+
+        # Generate baseline WITHOUT detailer (same seed/prompt as detailer tests)
+        print("  Generating baseline (no detailer)...")
+        baseline_data = self._txt2img()
+        if 'error' in baseline_data:
+            self.record(False, 'detailer_baseline', f"error: {baseline_data}")
+            return
+        baseline = self._decode_image(baseline_data)
+        if baseline is None:
+            self.record(False, 'detailer_baseline', 'no image')
+            return
+        self.record(True, 'detailer_baseline')
+
+        # Generate WITH detailer enabled (default params)
+        print("  Generating with detailer (defaults)...")
+        detailer_default_data = self._txt2img({
+            'detailer_enabled': True,
+            'detailer_strength': 0.3,
+            'detailer_steps': 5,
+            'detailer_conf': 0.3,
+        })
+        if 'error' in detailer_default_data:
+            self.record(False, 'detailer_default', f"error: {detailer_default_data}")
+            return
+        detailer_default = self._decode_image(detailer_default_data)
+
+        # Detailer ON vs OFF should produce different images (if a face was detected)
+        diff_on_off = self._pixel_diff(baseline, detailer_default)
+        self.record(diff_on_off > 0.5, 'detailer_on_vs_off',
+                    f"mean_diff={diff_on_off:.2f}" if diff_on_off > 0.5
+                    else f"identical (diff={diff_on_off:.4f}) — no face detected?")
+
+        # -- Strength variation --
+        print("  Testing strength variation...")
+        strong_data = self._txt2img({
+            'detailer_enabled': True,
+            'detailer_strength': 0.7,
+            'detailer_steps': 5,
+            'detailer_conf': 0.3,
+        })
+        if 'error' not in strong_data:
+            strong = self._decode_image(strong_data)
+            diff_strong = self._pixel_diff(detailer_default, strong)
+            self.record(diff_strong > 0.5, 'detailer_strength_effect',
+                        f"strength 0.3 vs 0.7: diff={diff_strong:.2f}")
+
+        # -- Steps variation --
+        print("  Testing steps variation...")
+        more_steps_data = self._txt2img({
+            'detailer_enabled': True,
+            'detailer_strength': 0.3,
+            'detailer_steps': 20,
+            'detailer_conf': 0.3,
+        })
+        if 'error' not in more_steps_data:
+            more_steps = self._decode_image(more_steps_data)
+            diff_steps = self._pixel_diff(detailer_default, more_steps)
+            self.record(diff_steps > 0.5, 'detailer_steps_effect',
+                        f"steps 5 vs 20: diff={diff_steps:.2f}")
+
+        # -- Resolution variation --
+        print("  Testing resolution variation...")
+        hires_data = self._txt2img({
+            'detailer_enabled': True,
+            'detailer_strength': 0.3,
+            'detailer_steps': 5,
+            'detailer_conf': 0.3,
+            'detailer_resolution': 512,
+        })
+        if 'error' not in hires_data:
+            hires = self._decode_image(hires_data)
+            diff_res = self._pixel_diff(detailer_default, hires)
+            self.record(diff_res > 0.5, 'detailer_resolution_effect',
+                        f"resolution 1024 vs 512: diff={diff_res:.2f}")
+
+        # -- Segmentation mode --
+        # Segmentation requires a -seg model (e.g. anzhc-face-1024-seg-8n).
+        # Detection-only models (face-yolo8n) don't produce masks, so the flag has no effect.
+        seg_models = [m.get('name', '') for m in (available_models or [])
+                      if 'seg' in m.get('name', '').lower() and 'face' in m.get('name', '').lower()]
+        if seg_models:
+            seg_model = seg_models[0]
+            print(f"  Testing segmentation mode (model={seg_model})...")
+            # bbox baseline with the seg model
+            seg_bbox_data = self._txt2img({
+                'detailer_enabled': True,
+                'detailer_strength': 0.3,
+                'detailer_steps': 5,
+                'detailer_conf': 0.3,
+                'detailer_segmentation': False,
+                'detailer_models': [seg_model],
+            })
+            seg_data = self._txt2img({
+                'detailer_enabled': True,
+                'detailer_strength': 0.3,
+                'detailer_steps': 5,
+                'detailer_conf': 0.3,
+                'detailer_segmentation': True,
+                'detailer_models': [seg_model],
+            })
+            if 'error' not in seg_data and 'error' not in seg_bbox_data:
+                seg_bbox = self._decode_image(seg_bbox_data)
+                seg_mask = self._decode_image(seg_data)
+                diff_seg = self._pixel_diff(seg_bbox, seg_mask)
+                self.record(diff_seg > 0.5, 'detailer_segmentation_effect',
+                            f"bbox vs seg mask ({seg_model}): diff={diff_seg:.2f}")
+            else:
+                err = seg_data if 'error' in seg_data else seg_bbox_data
+                self.record(False, 'detailer_segmentation_effect', f"error: {err}")
+        else:
+            print("  Testing segmentation mode...")
+            seg_data = {'error': 'skipped'}
+            self.skip('detailer_segmentation_effect', 'no face-seg model available')
+
+        # -- Confidence threshold --
+        print("  Testing confidence threshold...")
+        high_conf_data = self._txt2img({
+            'detailer_enabled': True,
+            'detailer_strength': 0.3,
+            'detailer_steps': 5,
+            'detailer_conf': 0.95,
+        })
+        if 'error' not in high_conf_data:
+            high_conf = self._decode_image(high_conf_data)
+            diff_conf = self._pixel_diff(baseline, high_conf)
+            # High confidence may reject detections, making output closer to baseline
+            self.record(True, 'detailer_conf_effect',
+                        f"conf=0.95 vs baseline: diff={diff_conf:.2f} "
+                        f"(low diff = detections filtered out, high diff = still detected)")
+
+        # -- Custom detailer prompt --
+        print("  Testing detailer prompt override...")
+        prompt_data = self._txt2img({
+            'detailer_enabled': True,
+            'detailer_strength': 0.5,
+            'detailer_steps': 5,
+            'detailer_conf': 0.3,
+            'detailer_prompt': 'a detailed close-up face with freckles',
+        })
+        if 'error' not in prompt_data:
+            prompt_result = self._decode_image(prompt_data)
+            diff_prompt = self._pixel_diff(detailer_default, prompt_result)
+            self.record(diff_prompt > 0.5, 'detailer_prompt_effect',
+                        f"custom prompt vs default: diff={diff_prompt:.2f}")
+
+        # -- Metadata verification across params --
+        for test_data, label in [
+            (detailer_default_data, 'detailer_default'),
+            (strong_data if 'error' not in strong_data else None, 'detailer_strong'),
+            (more_steps_data if 'error' not in more_steps_data else None, 'detailer_more_steps'),
+            (seg_data if 'error' not in seg_data else None, 'detailer_segmentation'),
+        ]:
+            if test_data is None:
+                continue
+            info = self._get_info(test_data)
+            has_meta = 'detailer' in info.lower() or 'Detailer' in info
+            self.record(has_meta, f'{label}_metadata',
+                        'detailer info in metadata' if has_meta else 'no detailer metadata')
+
+        # -- Param isolation: generate without detailer after all detailer runs --
+        print("  Testing param isolation...")
+        after_data = self._txt2img()
+        if 'error' not in after_data:
+            after = self._decode_image(after_data)
+            leak_diff = self._pixel_diff(baseline, after)
+            self.record(leak_diff < 0.5, 'detailer_param_isolation',
+                        f"post-detailer baseline diff={leak_diff:.4f}" if leak_diff < 0.5
+                        else f"LEAK: baseline changed (diff={leak_diff:.2f})")
+
+    # =========================================================================
+    # Runner
+    # =========================================================================
+
+    def run_all(self):
+        print("=" * 60)
+        print("YOLO Detailer API Test Suite")
+        print(f"Server: {self.base_url}")
+        print("=" * 60)
+
+        # Enumerate
+        models = self.test_detailers_list()
+
+        # Detect across all loaded test images
+        self.test_detect_all_images(models)
+        # Test with first available model if any
+        if models and len(models) > 0:
+            model_name = models[0].get('name', models[0].get('filename', ''))
+            if model_name:
+                self.test_detect_with_model(model_name)
+
+        # Generate
+        self.test_txt2img_without_detailer()
+        self.test_txt2img_with_detailer()
+
+        # Per-request detailer param validation
+        self.run_detailer_param_tests(models)
+
+        # Summary
+        print("\n" + "=" * 60)
+        print("Results")
+        print("=" * 60)
+        total_passed = 0
+        total_failed = 0
+        total_skipped = 0
+        for cat, data in self.results.items():
+            total_passed += data['passed']
+            total_failed += data['failed']
+            total_skipped += data['skipped']
+            status = 'PASS' if data['failed'] == 0 else 'FAIL'
+            print(f"  {cat}: {data['passed']} passed, {data['failed']} failed, {data['skipped']} skipped [{status}]")
+        print(f"  Total: {total_passed} passed, {total_failed} failed, {total_skipped} skipped")
+        print("=" * 60)
+        return total_failed == 0
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='YOLO Detailer API Tests')
+    parser.add_argument('--url', default=os.environ.get('SDAPI_URL', 'http://127.0.0.1:7860'), help='server URL')
+    parser.add_argument('--image', default=None, help='test image path')
+    args = parser.parse_args()
+    test = DetailerAPITest(args.url, args.image)
+    success = test.run_all()
+    sys.exit(0 if success else 1)
diff --git a/test/test-generation-api.py b/test/test-generation-api.py
new file mode 100644
index 000000000..1359d59e5
--- /dev/null
+++ b/test/test-generation-api.py
@@ -0,0 +1,615 @@
+#!/usr/bin/env python
+"""
+API tests for generation with scheduler params, color grading, and latent corrections.
+
+Tests:
+- GET /sdapi/v1/samplers — sampler enumeration and config
+- POST /sdapi/v1/txt2img — generation with various samplers
+- POST /sdapi/v1/txt2img — generation with color grading params
+- POST /sdapi/v1/txt2img — generation with latent correction params
+
+Requires a running SD.Next instance with a model loaded.
+
+Usage:
+    python test/test-generation-api.py [--url URL] [--steps STEPS]
+"""
+
+import io
+import os
+import sys
+import json
+import time
+import base64
+import argparse
+import requests
+import urllib3
+
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+
+class GenerationAPITest:
+    """Test harness for generation API with scheduler and grading params."""
+
+    # Samplers to test — a representative subset covering different scheduler families
+    TEST_SAMPLERS = [
+        'Euler a',
+        'Euler',
+        'DPM++ 2M',
+        'UniPC',
+        'DDIM',
+        'DPM++ 2M SDE',
+    ]
+
+    def __init__(self, base_url, steps=10, timeout=300):
+        self.base_url = base_url.rstrip('/')
+        self.steps = steps
+        self.timeout = timeout
+        self.results = {
+            'samplers': {'passed': 0, 'failed': 0, 'skipped': 0, 'tests': []},
+            'generation': {'passed': 0, 'failed': 0, 'skipped': 0, 'tests': []},
+            'grading': {'passed': 0, 'failed': 0, 'skipped': 0, 'tests': []},
+            'correction': {'passed': 0, 'failed': 0, 'skipped': 0, 'tests': []},
+            'param_validation': {'passed': 0, 'failed': 0, 'skipped': 0, 'tests': []},
+        }
+        self._category = 'samplers'
+        self._critical_error = None
+
+    def _get(self, endpoint):
+        try:
+            r = requests.get(f'{self.base_url}{endpoint}', timeout=self.timeout, verify=False)
+            if r.status_code != 200:
+                return {'error': r.status_code, 'reason': r.reason}
+            return r.json()
+        except requests.exceptions.ConnectionError:
+            return {'error': 'connection_refused', 'reason': 'Server not running'}
+        except Exception as e:
+            return {'error': 'exception', 'reason': str(e)}
+
+    def _post(self, endpoint, data):
+        try:
+            r = requests.post(f'{self.base_url}{endpoint}', json=data, timeout=self.timeout, verify=False)
+            if r.status_code != 200:
+                return {'error': r.status_code, 'reason': r.reason}
+            return r.json()
+        except requests.exceptions.ConnectionError:
+            return {'error': 'connection_refused', 'reason': 'Server not running'}
+        except Exception as e:
+            return {'error': 'exception', 'reason': str(e)}
+
+    def record(self, passed, name, detail=''):
+        status = 'PASS' if passed else 'FAIL'
+        self.results[self._category]['passed' if passed else 'failed'] += 1
+        self.results[self._category]['tests'].append((status, name))
+        msg = f'  {status}: {name}'
+        if detail:
+            msg += f' ({detail})'
+        print(msg)
+
+    def skip(self, name, reason):
+        self.results[self._category]['skipped'] += 1
+        self.results[self._category]['tests'].append(('SKIP', name))
+        print(f'  SKIP: {name} ({reason})')
+
+    def _txt2img(self, extra_params=None, prompt='a cat'):
+        """Helper: run txt2img with base params + overrides. Returns (data, time)."""
+        payload = {
+            'prompt': prompt,
+            'steps': self.steps,
+            'width': 512,
+            'height': 512,
+            'seed': 42,
+            'save_images': False,
+            'send_images': True,
+        }
+        if extra_params:
+            payload.update(extra_params)
+        t0 = time.time()
+        data = self._post('/sdapi/v1/txt2img', payload)
+        return data, time.time() - t0
+
+    def _check_generation(self, data, test_name, elapsed):
+        """Validate a generation response has images."""
+        if 'error' in data:
+            self.record(False, test_name, f"error: {data}")
+            return False
+        has_images = 'images' in data and len(data['images']) > 0
+        self.record(has_images, test_name, f"time={elapsed:.1f}s")
+        return has_images
+
+    def _get_info(self, data):
+        """Extract info string from generation response."""
+        if 'info' not in data:
+            return ''
+        info = data['info']
+        return info if isinstance(info, str) else json.dumps(info)
+
+    def _decode_image(self, data):
+        """Decode first image from generation response into numpy array."""
+        import numpy as np
+        from PIL import Image
+        if 'images' not in data or len(data['images']) == 0:
+            return None
+        img_data = data['images'][0].split(',', 1)[0]
+        img = Image.open(io.BytesIO(base64.b64decode(img_data))).convert('RGB')
+        return np.array(img, dtype=np.float32)
+
+    def _pixel_diff(self, arr_a, arr_b):
+        """Mean absolute pixel difference between two images (0-255 scale)."""
+        import numpy as np
+        if arr_a is None or arr_b is None:
+            return -1.0
+        if arr_a.shape != arr_b.shape:
+            return -1.0
+        return float(np.abs(arr_a - arr_b).mean())
+
+    def _channel_means(self, arr):
+        """Return per-channel means [R, G, B]."""
+        if arr is None:
+            return [0, 0, 0]
+        return [float(arr[:, :, c].mean()) for c in range(3)]
+
+    # =========================================================================
+    # Tests: Sampler Enumeration
+    # =========================================================================
+
+    def test_samplers_list(self):
+        """GET /sdapi/v1/samplers returns available samplers with config."""
+        self._category = 'samplers'
+        print("\n--- Sampler Enumeration ---")
+
+        data = self._get('/sdapi/v1/samplers')
+        if 'error' in data:
+            self.record(False, 'samplers_list', f"error: {data}")
+            self._critical_error = f"Server error: {data}"
+            return []
+
+        if not isinstance(data, list):
+            self.record(False, 'samplers_list', f"expected list, got {type(data).__name__}")
+            return []
+
+        self.record(True, 'samplers_list', f"{len(data)} samplers available")
+
+        # Check that each sampler has a name
+        sampler_names = []
+        for s in data:
+            name = s.get('name', '')
+            if name:
+                sampler_names.append(name)
+
+        self.record(len(sampler_names) == len(data), 'samplers_have_names',
+                    f"{len(sampler_names)}/{len(data)} have names")
+
+        # Check for our test samplers
+        for test_sampler in self.TEST_SAMPLERS:
+            found = test_sampler in sampler_names
+            if not found:
+                self.skip(f'sampler_available_{test_sampler}', 'not in server sampler list')
+            else:
+                self.record(True, f'sampler_available_{test_sampler}')
+
+        return sampler_names
+
+    # =========================================================================
+    # Tests: Generation with Different Samplers
+    # =========================================================================
+
+    def test_samplers_generate(self, available_samplers):
+        """Generate with each test sampler and verify success."""
+        self._category = 'generation'
+        print("\n--- Generation with Different Samplers ---")
+
+        if self._critical_error:
+            for s in self.TEST_SAMPLERS:
+                self.skip(f'generate_{s}', self._critical_error)
+            return
+
+        for sampler in self.TEST_SAMPLERS:
+            if sampler not in available_samplers:
+                self.skip(f'generate_{sampler}', 'sampler not available')
+                continue
+            data, elapsed = self._txt2img({'sampler_name': sampler})
+            self._check_generation(data, f'generate_{sampler}', elapsed)
+
+    # =========================================================================
+    # Tests: Color Grading Params
+    # =========================================================================
+
+    def test_grading_brightness_contrast(self):
+        """Generate with grading brightness and contrast."""
+        data, elapsed = self._txt2img({
+            'grading_brightness': 0.2,
+            'grading_contrast': 0.3,
+        })
+        self._check_generation(data, 'grading_brightness_contrast', elapsed)
+
+    def test_grading_saturation_hue(self):
+        """Generate with grading saturation and hue shift."""
+        data, elapsed = self._txt2img({
+            'grading_saturation': 0.5,
+            'grading_hue': 0.1,
+        })
+        self._check_generation(data, 'grading_saturation_hue', elapsed)
+
+    def test_grading_gamma_sharpness(self):
+        """Generate with gamma correction and sharpness."""
+        data, elapsed = self._txt2img({
+            'grading_gamma': 0.8,
+            'grading_sharpness': 0.5,
+        })
+        self._check_generation(data, 'grading_gamma_sharpness', elapsed)
+
+    def test_grading_color_temp(self):
+        """Generate with warm color temperature."""
+        data, elapsed = self._txt2img({
+            'grading_color_temp': 3500,
+        })
+        self._check_generation(data, 'grading_color_temp', elapsed)
+
+    def test_grading_tone(self):
+        """Generate with shadows/midtones/highlights adjustments."""
+        data, elapsed = self._txt2img({
+            'grading_shadows': 0.3,
+            'grading_midtones': -0.1,
+            'grading_highlights': 0.2,
+        })
+        self._check_generation(data, 'grading_tone', elapsed)
+
+    def test_grading_effects(self):
+        """Generate with vignette and grain."""
+        data, elapsed = self._txt2img({
+            'grading_vignette': 0.5,
+            'grading_grain': 0.3,
+        })
+        self._check_generation(data, 'grading_effects', elapsed)
+
+    def test_grading_split_toning(self):
+        """Generate with split toning colors."""
+        data, elapsed = self._txt2img({
+            'grading_shadows_tint': '#003366',
+            'grading_highlights_tint': '#ffcc00',
+            'grading_split_tone_balance': 0.6,
+        })
+        self._check_generation(data, 'grading_split_toning', elapsed)
+
+    def test_grading_combined(self):
+        """Generate with multiple grading params at once."""
+        data, elapsed = self._txt2img({
+            'grading_brightness': 0.1,
+            'grading_contrast': 0.2,
+            'grading_saturation': 0.3,
+            'grading_gamma': 0.9,
+            'grading_color_temp': 5000,
+            'grading_vignette': 0.3,
+        })
+        self._check_generation(data, 'grading_combined', elapsed)
+
+    def run_grading_tests(self):
+        """Run all grading tests."""
+        self._category = 'grading'
+        print("\n--- Color Grading Tests ---")
+
+        if self._critical_error:
+            self.skip('grading_all', self._critical_error)
+            return
+
+        self.test_grading_brightness_contrast()
+        self.test_grading_saturation_hue()
+        self.test_grading_gamma_sharpness()
+        self.test_grading_color_temp()
+        self.test_grading_tone()
+        self.test_grading_effects()
+        self.test_grading_split_toning()
+        self.test_grading_combined()
+
+    # =========================================================================
+    # Tests: Latent Correction Params
+    # =========================================================================
+
+    def test_correction_brightness(self):
+        """Generate with latent brightness correction."""
+        data, elapsed = self._txt2img({'hdr_brightness': 1.5})
+        ok = self._check_generation(data, 'correction_brightness', elapsed)
+        if ok:
+            info = self._get_info(data)
+            has_param = 'Latent brightness' in info
+            self.record(has_param, 'correction_brightness_metadata',
+                        'found in info' if has_param else 'not found in info')
+
+    def test_correction_color(self):
+        """Generate with latent color centering."""
+        data, elapsed = self._txt2img({'hdr_color': 0.5, 'hdr_mode': 1})
+        ok = self._check_generation(data, 'correction_color', elapsed)
+        if ok:
+            info = self._get_info(data)
+            has_param = 'Latent color' in info
+            self.record(has_param, 'correction_color_metadata',
+                        'found in info' if has_param else 'not found in info')
+
+    def test_correction_clamp(self):
+        """Generate with latent clamping."""
+        data, elapsed = self._txt2img({
+            'hdr_clamp': True,
+            'hdr_threshold': 0.8,
+            'hdr_boundary': 4.0,
+        })
+        ok = self._check_generation(data, 'correction_clamp', elapsed)
+        if ok:
+            info = self._get_info(data)
+            has_param = 'Latent clamp' in info
+            self.record(has_param, 'correction_clamp_metadata',
+                        'found in info' if has_param else 'not found in info')
+
+    def test_correction_sharpen(self):
+        """Generate with latent sharpening."""
+        data, elapsed = self._txt2img({'hdr_sharpen': 1.0})
+        ok = self._check_generation(data, 'correction_sharpen', elapsed)
+        if ok:
+            info = self._get_info(data)
+            has_param = 'Latent sharpen' in info
+            self.record(has_param, 'correction_sharpen_metadata',
+                        'found in info' if has_param else 'not found in info')
+
+    def test_correction_maximize(self):
+        """Generate with latent maximize/normalize."""
+        data, elapsed = self._txt2img({
+            'hdr_maximize': True,
+            'hdr_max_center': 0.6,
+            'hdr_max_boundary': 2.0,
+        })
+        ok = self._check_generation(data, 'correction_maximize', elapsed)
+        if ok:
+            info = self._get_info(data)
+            has_param = 'Latent max' in info
+            self.record(has_param, 'correction_maximize_metadata',
+                        'found in info' if has_param else 'not found in info')
+
+    def test_correction_combined(self):
+        """Generate with multiple correction params."""
+        data, elapsed = self._txt2img({
+            'hdr_brightness': 1.0,
+            'hdr_color': 0.3,
+            'hdr_sharpen': 0.5,
+            'hdr_clamp': True,
+        })
+        ok = self._check_generation(data, 'correction_combined', elapsed)
+        if ok:
+            info = self._get_info(data)
+            # At least some correction params should appear
+            found = [k for k in ['Latent brightness', 'Latent color', 'Latent sharpen', 'Latent clamp'] if k in info]
+            self.record(len(found) > 0, 'correction_combined_metadata', f"found: {found}")
+
+    def run_correction_tests(self):
+        """Run all latent correction tests."""
+        self._category = 'correction'
+        print("\n--- Latent Correction Tests ---")
+
+        if self._critical_error:
+            self.skip('correction_all', self._critical_error)
+            return
+
+        self.test_correction_brightness()
+        self.test_correction_color()
+        self.test_correction_clamp()
+        self.test_correction_sharpen()
+        self.test_correction_maximize()
+        self.test_correction_combined()
+
+    # =========================================================================
+    # Tests: Per-Request Param Validation (baseline comparison)
+    # =========================================================================
+
+    def _generate_baseline(self):
+        """Generate a baseline image with no grading/correction. Cache and reuse."""
+        if hasattr(self, '_baseline_arr') and self._baseline_arr is not None:
+            return self._baseline_arr, self._baseline_data
+        data, elapsed = self._txt2img()
+        if 'error' in data or 'images' not in data:
+            return None, data
+        self._baseline_arr = self._decode_image(data)
+        self._baseline_data = data
+        print(f'  Baseline generated: time={elapsed:.1f}s mean={self._channel_means(self._baseline_arr)}')
+        return self._baseline_arr, data
+
+    def _compare_param(self, name, params, check_fn=None):
+        """Generate with params and compare to baseline. Optionally run check_fn(baseline, result)."""
+        baseline, _ = self._generate_baseline()
+        if baseline is None:
+            self.skip(f'param_{name}', 'baseline generation failed')
+            return
+
+        data, elapsed = self._txt2img(params)
+        if 'error' in data:
+            self.record(False, f'param_{name}', f"generation error: {data}")
+            return
+
+        result = self._decode_image(data)
+        if result is None:
+            self.record(False, f'param_{name}', 'no image in response')
+            return
+
+        diff = self._pixel_diff(baseline, result)
+        differs = diff > 0.5  # more than 0.5/255 mean difference
+        self.record(differs, f'param_{name}_differs',
+                    f"mean_diff={diff:.2f}" if differs else f"images identical (diff={diff:.4f})")
+
+        if check_fn and differs:
+            try:
+                ok, detail = check_fn(baseline, result, data)
+                self.record(ok, f'param_{name}_direction', detail)
+            except Exception as e:
+                self.record(False, f'param_{name}_direction', f"check error: {e}")
+
+    def run_param_validation_tests(self):
+        """Verify per-request grading/correction params actually change the output."""
+        self._category = 'param_validation'
+        print("\n--- Per-Request Param Validation ---")
+
+        if self._critical_error:
+            self.skip('param_validation_all', self._critical_error)
+            return
+
+        import numpy as np
+
+        # -- Grading params --
+
+        # Brightness: positive should increase mean pixel value
+        def check_brightness(base, result, _data):
+            base_mean = float(base.mean())
+            result_mean = float(result.mean())
+            return result_mean > base_mean, f"baseline={base_mean:.1f} graded={result_mean:.1f}"
+        self._compare_param('grading_brightness', {'grading_brightness': 0.3}, check_brightness)
+
+        # Contrast: should increase standard deviation
+        def check_contrast(base, result, _data):
+            return float(result.std()) > float(base.std()), \
+                f"baseline_std={float(base.std()):.1f} graded_std={float(result.std()):.1f}"
+        self._compare_param('grading_contrast', {'grading_contrast': 0.5}, check_contrast)
+
+        # Saturation: desaturation should reduce color channel spread
+        def check_desaturation(base, result, _data):
+            base_spread = max(self._channel_means(base)) - min(self._channel_means(base))
+            result_spread = max(self._channel_means(result)) - min(self._channel_means(result))
+            return result_spread < base_spread, \
+                f"baseline_spread={base_spread:.1f} graded_spread={result_spread:.1f}"
+        self._compare_param('grading_saturation_neg', {'grading_saturation': -0.5}, check_desaturation)
+
+        # Hue shift: just verify it changes
+        self._compare_param('grading_hue', {'grading_hue': 0.2})
+
+        # Gamma < 1: should brighten (raise values that are < 1)
+        def check_gamma(base, result, _data):
+            return float(result.mean()) > float(base.mean()), \
+                f"baseline={float(base.mean()):.1f} gamma={float(result.mean()):.1f}"
+        self._compare_param('grading_gamma', {'grading_gamma': 0.7}, check_gamma)
+
+        # Sharpness: just verify it changes
+        self._compare_param('grading_sharpness', {'grading_sharpness': 0.8})
+
+        # Color temperature warm: red channel mean should increase relative to blue
+        def check_warm(base, result, _data):
+            base_r, _, base_b = self._channel_means(base)
+            res_r, _, res_b = self._channel_means(result)
+            base_rb = base_r - base_b
+            res_rb = res_r - res_b
+            return res_rb > base_rb, f"baseline R-B={base_rb:.1f} warm R-B={res_rb:.1f}"
+        self._compare_param('grading_color_temp_warm', {'grading_color_temp': 3000}, check_warm)
+
+        # Color temperature cool: blue should increase relative to red
+        def check_cool(base, result, _data):
+            base_r, _, base_b = self._channel_means(base)
+            res_r, _, res_b = self._channel_means(result)
+            base_rb = base_r - base_b
+            res_rb = res_r - res_b
+            return res_rb < base_rb, f"baseline R-B={base_rb:.1f} cool R-B={res_rb:.1f}"
+        self._compare_param('grading_color_temp_cool', {'grading_color_temp': 10000}, check_cool)
+
+        # Vignette: corners should be darker than baseline corners
+        def check_vignette(base, result, _data):
+            h, w = base.shape[:2]
+            corner_size = h // 8
+            base_corners = np.concatenate([
+                base[:corner_size, :corner_size].flatten(),
+                base[:corner_size, -corner_size:].flatten(),
+                base[-corner_size:, :corner_size].flatten(),
+                base[-corner_size:, -corner_size:].flatten(),
+            ])
+            result_corners = np.concatenate([
+                result[:corner_size, :corner_size].flatten(),
+                result[:corner_size, -corner_size:].flatten(),
+                result[-corner_size:, :corner_size].flatten(),
+                result[-corner_size:, -corner_size:].flatten(),
+            ])
+            return float(result_corners.mean()) < float(base_corners.mean()), \
+                f"baseline_corners={float(base_corners.mean()):.1f} vignette_corners={float(result_corners.mean()):.1f}"
+        self._compare_param('grading_vignette', {'grading_vignette': 0.8}, check_vignette)
+
+        # Grain: just verify it changes (stochastic)
+        self._compare_param('grading_grain', {'grading_grain': 0.5})
+
+        # Shadows/midtones/highlights: verify changes
+        self._compare_param('grading_shadows', {'grading_shadows': 0.5})
+        self._compare_param('grading_highlights', {'grading_highlights': -0.3})
+
+        # CLAHE: should increase local contrast
+        self._compare_param('grading_clahe', {'grading_clahe_clip': 2.0})
+
+        # Split toning: verify changes
+        self._compare_param('grading_split_toning', {
+            'grading_shadows_tint': '#003366',
+            'grading_highlights_tint': '#ffcc00',
+        })
+
+        # -- Correction params --
+
+        # Latent brightness: should change output and appear in metadata
+        def check_correction_meta(key):
+            def _check(_base, _result, data):
+                info = self._get_info(data)
+                return key in info, f"'{key}' {'found' if key in info else 'missing'} in info"
+            return _check
+        self._compare_param('hdr_brightness', {'hdr_brightness': 2.0}, check_correction_meta('Latent brightness'))
+        self._compare_param('hdr_color', {'hdr_color': 0.8, 'hdr_mode': 1}, check_correction_meta('Latent color'))
+        self._compare_param('hdr_sharpen', {'hdr_sharpen': 1.5}, check_correction_meta('Latent sharpen'))
+        self._compare_param('hdr_clamp', {'hdr_clamp': True, 'hdr_threshold': 0.7}, check_correction_meta('Latent clamp'))
+
+        # Isolation: verify params from one request don't leak to the next
+        data_after, _ = self._txt2img()
+        arr_after = self._decode_image(data_after)
+        baseline, _ = self._generate_baseline()
+        if baseline is not None and arr_after is not None:
+            leak_diff = self._pixel_diff(baseline, arr_after)
+            no_leak = leak_diff < 0.5
+            self.record(no_leak, 'param_isolation',
+                        f"post-grading baseline diff={leak_diff:.4f}" if no_leak
+                        else f"LEAK: baseline changed after grading requests (diff={leak_diff:.2f})")
+
+    # =========================================================================
+    # Runner
+    # =========================================================================
+
+    def run_all(self):
+        print("=" * 60)
+        print("Generation API Test Suite")
+        print(f"Server: {self.base_url}")
+        print(f"Steps: {self.steps}")
+        print("=" * 60)
+
+        # Samplers
+        available = self.test_samplers_list()
+        self.test_samplers_generate(available)
+
+        # Grading
+        self.run_grading_tests()
+
+        # Corrections
+        self.run_correction_tests()
+
+        # Per-request param validation (baseline comparison)
+        self.run_param_validation_tests()
+
+        # Summary
+        print("\n" + "=" * 60)
+        print("Results")
+        print("=" * 60)
+        total_passed = 0
+        total_failed = 0
+        total_skipped = 0
+        for cat, data in self.results.items():
+            total_passed += data['passed']
+            total_failed += data['failed']
+            total_skipped += data['skipped']
+            status = 'PASS' if data['failed'] == 0 else 'FAIL'
+            print(f"  {cat}: {data['passed']} passed, {data['failed']} failed, {data['skipped']} skipped [{status}]")
+        print(f"  Total: {total_passed} passed, {total_failed} failed, {total_skipped} skipped")
+        print("=" * 60)
+        return total_failed == 0
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Generation API Tests (samplers, grading, correction)')
+    parser.add_argument('--url', default=os.environ.get('SDAPI_URL', 'http://127.0.0.1:7860'), help='server URL')
+    parser.add_argument('--steps', type=int, default=10, help='generation steps (lower = faster tests)')
+    args = parser.parse_args()
+    test = GenerationAPITest(args.url, args.steps)
+    success = test.run_all()
+    sys.exit(0 if success else 1)