fixed batch

nodes/FL_GeminiImageEditor.py

@@ -40,8 +40,9 @@ class FL_GeminiImageEditor:
             }
         }
 
-    RETURN_TYPES = ("IMAGE", "*", "STRING")
-    RETURN_NAMES = ("image", "image_list", "API Respond")
+    RETURN_TYPES = ("IMAGE", "STRING")
+    RETURN_NAMES = ("image", "API Respond")
+    OUTPUT_IS_LIST = (True, False)
     FUNCTION = "generate_image"
     CATEGORY = "🏵️Fill Nodes/AI"
 
@@ -414,13 +415,9 @@ class FL_GeminiImageEditor:
                 error_img = self._create_error_image("API key required")
                 full_text = "## Error\n" + error_message + "\n\n## Instructions\n1. Enter your Google API key in the node"
 
-                # For batch size > 1, create batch of error images
-                if batch_size > 1:
-                    error_imgs = [error_img] * batch_size
-                    batch_tensor = torch.cat(error_imgs, dim=0)
-                    return (batch_tensor, full_text)
-                else:
-                    return (error_img, full_text)
+                # Create list of error images for all batch sizes
+                error_imgs = [error_img] * batch_size
+                return (error_imgs, full_text)
 
             self._log(f"Starting batch generation of {batch_size} images")
 
@@ -453,7 +450,8 @@ class FL_GeminiImageEditor:
                         max_retries=max_retries,
                         batch_id=i + 1,
                         seed=batch_seed,
-                        reference_images=reference_pil_images
+                        reference_images=reference_pil_images,
+                        always_square=always_square
                     )
                     tasks.append(task)
 
@@ -479,22 +477,19 @@ class FL_GeminiImageEditor:
             except concurrent.futures.TimeoutError:
                 self._log("Async processing timed out after 5 minutes")
                 error_imgs = [self._create_error_image("Processing timeout")] * batch_size
-                batch_tensor = torch.cat(error_imgs, dim=0)
-                return (batch_tensor, "Processing timed out after 5 minutes")
+                return (error_imgs, "Processing timed out after 5 minutes")
             except Exception as e:
                 self._log(f"Error in async processing: {str(e)}")
                 traceback.print_exc()
-                # Create batch of error images
+                # Create list of error images
                 error_imgs = [self._create_error_image(f"Async processing error: {str(e)}")] * batch_size
-                batch_tensor = torch.cat(error_imgs, dim=0)
-                return (batch_tensor, f"Async processing error: {str(e)}")
+                return (error_imgs, f"Async processing error: {str(e)}")
             
             # Process results (ensure results is not None if an error occurred before assignment)
             if results is None:
                 self._log("Async processing did not yield results, possibly due to an earlier error before gather.")
                 error_imgs = [self._create_error_image("Async processing failed to produce results")] * batch_size
-                batch_tensor = torch.cat(error_imgs, dim=0)
-                return (batch_tensor, "Async processing failed to produce results")
+                return (error_imgs, "Async processing failed to produce results")
 
             # Process results
             all_tensors = []
@@ -506,31 +501,22 @@ class FL_GeminiImageEditor:
                 all_tensors.append(img_tensor)
                 batch_texts.append(f"## Batch {batch_id} Response\n{text}")
 
-            # Combine all tensors into a batch
-            if len(all_tensors) == 1:
-                # Just return the single tensor
-                batch_tensor = all_tensors[0]
-            else:
-                # Concatenate all tensors along batch dimension
-                batch_tensor = torch.cat(all_tensors, dim=0)
-
-            self._log(f"Successfully created batch of {len(all_tensors)} images, final shape: {batch_tensor.shape}")
+            self._log(f"Successfully created list of {len(all_tensors)} images")
 
             # Combine all texts
             all_response_text = "## Batch Processing Results\n" + "\n".join(self.log_messages) + "\n\n" + "\n\n".join(
                 batch_texts)
 
-            return (batch_tensor, all_response_text)
+            return (all_tensors, all_response_text)
 
         except Exception as e:
             error_message = f"Error during batch processing: {str(e)}"
             self._log(error_message)
             traceback.print_exc()
 
-            # Create batch of error images
+            # Create list of error images
             error_imgs = [self._create_error_image(f"Error: {str(e)}")] * batch_size
-            batch_tensor = torch.cat(error_imgs, dim=0)
 
             # Combine logs and error info
             full_text = "## Processing Log\n" + "\n".join(self.log_messages) + "\n\n## Error\n" + error_message
-            return (batch_tensor, full_text)
+            return (error_imgs, full_text)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui_fill-nodes"
 description = "Fill-Nodes is a versatile collection of custom nodes for ComfyUI that extends functionality across multiple domains. Features include advanced image processing (pixelation, slicing, masking), visual effects generation (glitch, halftone, pixel art), comprehensive file handling (PDF creation/extraction, Google Drive integration), AI model interfaces (GPT, DALL-E, Hugging Face), utility nodes for workflow enhancement, and specialized tools for video processing, captioning, and batch operations. The pack provides both practical workflow solutions and creative tools within a unified node collection."
-version = "1.7.1"
+version = "1.7.2"
 license = "LICENSE"
 dependencies = ["diffusers", "librosa", "sounddevice", "glitch_this", "PyOpenGL", "glfw", "scipy>=1.13.1", "requests", "aiohttp", "moviepy", "matplotlib", "reportlab", "openai", "PyPDF2", "pdf2image", "PyMuPDF", "reportlab", "PyPDF2", "ollama", "kornia", "opencv-python", "gdown", "open_clip_torch", "google-genai"]