Remove SaveImage node, adjust workflow executor, restore tensor-to-image saving and deduplication

core/pipelines/sd_image_pipeline.py

@@ -34,10 +34,10 @@ class SdImagePipeline(BasePipeline):
         """
         progress(0.4, desc="Executing workflow...")
         initial_objects = {}
-        # Execute the workflow; the SaveImage node returns the image tensor(s).
+        # Execute the workflow; it returns image tensor(s) from the VAE Decode node.
         decoded_images_tensor = WorkflowExecutor.execute_workflow(workflow, initial_objects=initial_objects)
 
-        # Convert tensors to PIL images, attach metadata, and save them to the output folder.
+        # Convert tensors to PIL images, embed metadata and save them to the output directory.
         out_dir = os.path.abspath(OUTPUT_DIR)
         os.makedirs(out_dir, exist_ok=True)
         saved_file_paths = []
@@ -65,22 +65,19 @@ class SdImagePipeline(BasePipeline):
             pil_image.save(filepath, "PNG")
             saved_file_paths.append(filepath)
 
-        # After both the internal SaveImage node (which already wrote files) and the above
-        # manual save, the output directory may contain duplicate images. Deduplicate by
-        # content hash (SHA‑256) and return a list of unique file paths.
+        # Deduplicate by file content hash (SHA‑256) to avoid identical images.
         import hashlib
         unique_hashes = set()
         deduped_paths = []
-        for p in sorted(os.listdir(out_dir)):
-            full_path = os.path.join(out_dir, p)
+        for p in saved_file_paths:
             try:
-                with open(full_path, "rb") as f:
+                with open(p, "rb") as f:
                     h = hashlib.sha256(f.read()).hexdigest()
                 if h not in unique_hashes:
                     unique_hashes.add(h)
-                    deduped_paths.append(full_path)
+                    deduped_paths.append(p)
             except Exception:
-                continue
+                deduped_paths.append(p)
         return deduped_paths
 
     def run(self, ui_inputs: Dict, progress):

core/pipelines/workflow_executor.py

@@ -95,16 +95,17 @@ class WorkflowExecutor:
                 result = execution_method(**kwargs)
                 computed_outputs[node_id] = result
             
+            # Determine the final output. If a SaveImage node exists, use its input image.
             final_node_id = None
             for node_id in reversed(sorted_node_ids):
-                 if workflow[node_id]['class_type'] == 'SaveImage':
-                     final_node_id = node_id
-                     break
-            
-            if not final_node_id:
-                raise RuntimeError("Workflow does not contain a 'SaveImage' node as the output.")
-
-            save_image_inputs = workflow[final_node_id]['inputs']
-            image_source_node_id, image_source_index = save_image_inputs['images']
-            
-            return get_value_at_index(computed_outputs[image_source_node_id], image_source_index)
+                if workflow[node_id]['class_type'] == 'SaveImage':
+                    final_node_id = node_id
+                    break
+            if final_node_id:
+                save_image_inputs = workflow[final_node_id]['inputs']
+                image_source_node_id, image_source_index = save_image_inputs['images']
+                return get_value_at_index(computed_outputs[image_source_node_id], image_source_index)
+            else:
+                # No SaveImage node – return the output of the last node in execution order.
+                last_node_id = sorted_node_ids[-1]
+                return computed_outputs[last_node_id]

core/pipelines/workflow_recipes/_partials/_base_sampler_sd.yaml

@@ -1,36 +1,29 @@
-nodes:
-  pos_prompt:
-    class_type: CLIPTextEncode
-    title: "CLIP Text Encode (Positive)"
-  neg_prompt:
-    class_type: CLIPTextEncode
-    title: "CLIP Text Encode (Negative)"
-  ksampler:
-    class_type: KSampler
-    title: "KSampler"
-    params:
-      denoise: 1.0
-  vae_decode:
-    class_type: VAEDecode
-    title: "VAE Decode"
-  save_image:
-    class_type: SaveImage
-    title: "Save Image"
-    params: {}
-
-connections:
-  - from: "ksampler:0"
-    to: "vae_decode:samples"
-  - from: "vae_decode:0"
-    to: "save_image:images"
-
-ui_map:
-  positive_prompt: "pos_prompt:text"
-  negative_prompt: "neg_prompt:text"
-  seed: "ksampler:seed"
-  steps: "ksampler:steps"
-  cfg: "ksampler:cfg"
-  sampler_name: "ksampler:sampler_name"
-  scheduler: "ksampler:scheduler"
-  denoise: "ksampler:denoise"
-  filename_prefix: "save_image:filename_prefix"
+nodes:
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+  ksampler:
+    class_type: KSampler
+    title: "KSampler"
+    params:
+      denoise: 1.0
+  vae_decode:
+    class_type: VAEDecode
+    title: "VAE Decode"
+
+connections:
+  - from: "ksampler:0"
+    to: "vae_decode:samples"
+
+ui_map:
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+  seed: "ksampler:seed"
+  steps: "ksampler:steps"
+  cfg: "ksampler:cfg"
+  sampler_name: "ksampler:sampler_name"
+  scheduler: "ksampler:scheduler"
+  denoise: "ksampler:denoise"