Support FLUX reference images through real edit workflow

image_test.py

@@ -46,9 +46,10 @@ def select_gpus(devices,model_size,encoder_size,vae_size,offload=False):
     return max(images,key=lambda g:g['free_mib']),encoders[0]
 
 def workflow(prompt,params,seed,family='qwen',references=()):
-    if references and family!='qwen':raise ValueError('Dieses Bildprofil unterstützt keine Referenzbilder.')
+    if family not in ('qwen','flux'):raise ValueError('Für diese Modellfamilie fehlt eine Bildlaufzeit-Anbindung.')
+    if len(references)>4:raise ValueError('Maximal vier Referenzbilder pro Auftrag.')
     if family=='flux':
-        return {
+        graph={
           '1':{'class_type':'UNETLoader','inputs':{'unet_name':'model.safetensors','weight_dtype':'default'}},
           '2':{'class_type':'DeckTextEncoderLoader','inputs':{'clip_name':'encoder.safetensors','family':'flux2'}},
           '3':{'class_type':'VAELoader','inputs':{'vae_name':'vae.safetensors'}},
@@ -64,6 +65,20 @@ def workflow(prompt,params,seed,family='qwen',references=()):
           '13':{'class_type':'ConditioningZeroOut','inputs':{'conditioning':['4',0]}}
         }
 
+        # FLUX.2 editing uses reference latents on both conditioning branches,
+        # with fresh output noise; never silently discard uploaded references.
+        positive=['4',0];negative=['13',0]
+        for index,name in enumerate(references):
+            load,scale,encode,pos,neg=map(str,range(20+index*5,25+index*5))
+            graph[load]={'class_type':'LoadImage','inputs':{'image':name}}
+            graph[scale]={'class_type':'ImageScaleToTotalPixels','inputs':{'image':[load,0],'upscale_method':'lanczos','megapixels':1.0,'resolution_steps':1}}
+            graph[encode]={'class_type':'VAEEncode','inputs':{'pixels':[scale,0],'vae':['3',0]}}
+            graph[pos]={'class_type':'ReferenceLatent','inputs':{'conditioning':positive,'latent':[encode,0]}}
+            graph[neg]={'class_type':'ReferenceLatent','inputs':{'conditioning':negative,'latent':[encode,0]}}
+            positive=[pos,0];negative=[neg,0]
+        graph['10']['inputs'].update(positive=positive,negative=negative)
+        return graph
+
     graph={
       '1':{'class_type':'UnetLoaderGGUF','inputs':{'unet_name':'model.gguf'}},
       '2':{'class_type':'DeckTextEncoderLoader','inputs':{'clip_name':'encoder.safetensors'}},