Add profile-aware image reference editing

endpoint.py

@@ -183,6 +183,15 @@ class APIHandler(BaseHTTPRequestHandler):
             if self.command!='POST':raise APIError('Route nicht gefunden.',404)
             if ep.video and ep.scheduler.gpu_mode!='llm' and self.path!='/v1/audio/transcriptions':raise APIError('Video-Modus aktiv; Chat-, Bild- und TTS-Aufträge sind gesperrt. Auf LLM zurückschalten.',503,'video_mode_active')
             if self.path=='/v1/audio/transcriptions':return self.transcription(ep)
+            if self.path=='/v1/images/edits':
+                from image_upload import read_image_upload
+                try:
+                    fields,images=read_image_upload(self)
+                    if not images:raise ValueError('Mindestens ein Referenzbild erforderlich.')
+                    if set(fields)-{'model','prompt','size','n','response_format'}:raise ValueError('Nicht unterstützte Bildparameter.')
+                    fields['n']=int(fields.get('n','1'))
+                    return self.image(ep,fields,images)
+                except ValueError as exc:raise APIError(str(exc)) from None
             if self.path not in ('/v1/chat/completions','/v1/images/generations','/v1/audio/speech'):raise APIError('Route nicht implementiert.',404)
             if self.headers.get('Transfer-Encoding'):raise APIError('Chunked Upload wird nicht unterstützt.')
             try:length=int(self.headers.get('Content-Length','0'))
@@ -286,18 +295,21 @@ class APIHandler(BaseHTTPRequestHandler):
                     self.wfile.write(chunk);self.wfile.flush()
                 raise InferenceError('Zeitlimit der Streaming-Antwort überschritten.')
             finally:conn.close()
-    def image(self,ep,data):
+    def image(self,ep,data,reference_images=None):
         if set(data)-{'model','prompt','n','size','response_format','user'}:raise APIError('Nicht unterstützte Bildparameter. Schritte/Guidance/Seed stehen im Profil.')
         if data.get('n',1)!=1 or data.get('response_format','b64_json')!='b64_json':raise APIError('Unterstützt werden n=1 und response_format=b64_json.')
         profile=ep.find_profile(data.get('model'),'image');params=profile['parameters']
+        from profiles import image_capabilities
+        if len(reference_images or [])>image_capabilities(profile.get('model'))['reference_images']:raise APIError('Das aktive Bildprofil unterstützt diese Anzahl Referenzbilder nicht.')
         size=data.get('size')
         if size is not None and (not isinstance(size,str) or (size!='auto' and not re.fullmatch(r'[1-9][0-9]{1,4}x[1-9][0-9]{1,4}',size))):raise APIError('size muss auto oder eine Auflösung wie 1024x1024 sein.')
         # The selected profile owns resource limits; client size is only a preference.
         def allowed():return ep.allowed() and any(p['id']==profile['id'] and p['revision']==profile['revision'] and p['enabled'] for p in ep.rows())
         with ep.scheduler.lease(('image',),allowed=allowed):
-            return self.generate_image(ep,profile,data)
-    def generate_image(self,ep,profile,data):
-        job=ep.images.start(profile['id'],data.get('prompt'),reserved=True)
+            return self.generate_image(ep,profile,data,reference_images or [])
+    def generate_image(self,ep,profile,data,reference_images=None):
+        options={'reference_images':reference_images} if reference_images else {}
+        job=ep.images.start(profile['id'],data.get('prompt'),reserved=True,**options)
         deadline=time.monotonic()+2100
         while time.monotonic()<deadline:
             current=ep.images.status()['job']