Fix Node Interface Issues - Session 5 Priority 2
- Fix MetaMan Extract Components: Use input connector instead of text field - Fix MetaMan Embed & Save: Use input connector for metadata_json - Fix MetaMan Embed & Save: Actually save files to output directory - Remove image output from save node (OUTPUT_NODE = True) - Add proper metadata parsing and A1111 format conversion - Add UI feedback for saved files Now supports clean workflow chaining: MetaMan Load Image → MetaMan Extract Components → Individual outputs MetaMan Load Image → MetaMan Embed & Save (auto-saves with metadata)
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PXL8D
+70
-29
@@ -344,7 +344,7 @@ class MetaManExtractComponents:
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def INPUT_TYPES(cls):
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return {
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"required": {
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"metadata_json": ("STRING", {"default": "", "multiline": True})
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"metadata_json": ("STRING", {"forceInput": True})
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}
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}
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@@ -412,7 +412,7 @@ class MetaManExtractComponents:
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class MetaManEmbedAndSave:
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"""
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Embed metadata into image and save as PNG with metadata
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Embed metadata into image and save as PNG file to output directory
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"""
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@classmethod
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@@ -420,7 +420,7 @@ class MetaManEmbedAndSave:
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return {
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"required": {
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"image": ("IMAGE",),
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"metadata_text": ("STRING", {"default": "", "multiline": True}),
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"metadata_json": ("STRING", {"forceInput": True}),
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},
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"optional": {
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"filename_prefix": ("STRING", {"default": "MetaMan_converted"})
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@@ -428,16 +428,39 @@ class MetaManEmbedAndSave:
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}
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CATEGORY = "MetaMan"
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("final_image",)
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RETURN_TYPES = ()
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RETURN_NAMES = ()
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FUNCTION = "embed_and_save"
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DESCRIPTION = "Embed metadata into image and save as PNG"
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OUTPUT_NODE = True
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DESCRIPTION = "Embed metadata into image and save as PNG file"
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def embed_and_save(self, image, metadata_text, filename_prefix="MetaMan_converted"):
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def embed_and_save(self, image, metadata_json, filename_prefix="MetaMan_converted"):
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"""
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Embed metadata into image and return final image with metadata
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Embed metadata into image and save as PNG file to output directory
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"""
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try:
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# Parse metadata JSON to extract target format
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if metadata_json and metadata_json.strip():
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try:
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metadata_data = json.loads(metadata_json)
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# Extract target metadata or use universal metadata
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if 'target_metadata' in metadata_data:
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metadata_text = metadata_data['target_metadata']
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print(f"MetaMan Embed & Save: Using target metadata ({len(metadata_text)} chars)")
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elif 'metadata' in metadata_data:
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# Convert metadata dict to A1111 format
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metadata_text = self._format_metadata_for_embedding(metadata_data['metadata'])
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print(f"MetaMan Embed & Save: Converted metadata to A1111 format ({len(metadata_text)} chars)")
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else:
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metadata_text = json.dumps(metadata_data, indent=2)
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print(f"MetaMan Embed & Save: Using raw JSON ({len(metadata_text)} chars)")
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except Exception as e:
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print(f"MetaMan Embed & Save: Error parsing metadata JSON: {e}")
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metadata_text = metadata_json # Fallback to raw input
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else:
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metadata_text = "MetaMan processed image"
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print(f"MetaMan Embed & Save: No metadata provided, using default")
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# Convert tensor to PIL Image
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if isinstance(image, torch.Tensor):
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img_tensor = image[0]
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@@ -446,43 +469,61 @@ class MetaManEmbedAndSave:
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else:
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pil_image = image
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# Embed metadata in image
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result_image = self._embed_metadata_in_image(pil_image, metadata_text)
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# Save to ComfyUI output directory
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# Save to ComfyUI output directory with metadata
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output_dir = folder_paths.get_output_directory()
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"{filename_prefix}_{timestamp}.png"
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filepath = os.path.join(output_dir, filename)
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# Save with metadata
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# Create PNG info with metadata
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png_info = PngInfo()
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png_info.add_text("parameters", metadata_text)
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png_info.add_text("metaman_converted", datetime.now().isoformat())
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png_info.add_text("metaman_version", "1.0.0")
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result_image.save(filepath, "PNG", pnginfo=png_info)
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print(f"MetaMan Embed & Save: Saved image with metadata to {filepath}")
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# Save image with embedded metadata
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pil_image.save(filepath, "PNG", pnginfo=png_info)
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# Convert back to tensor
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if isinstance(image, torch.Tensor):
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import numpy as np
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img_array = np.array(result_image).astype(np.float32) / 255.0
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output_tensor = torch.from_numpy(img_array).unsqueeze(0)
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return (output_tensor,)
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print(f"MetaMan Embed & Save: Successfully saved image with metadata to {filepath}")
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print(f"MetaMan Embed & Save: File size: {os.path.getsize(filepath)} bytes")
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return (result_image,)
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return {"ui": {"images": [{"filename": filename, "subfolder": "", "type": "output"}]}}
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except Exception as e:
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print(f"MetaMan Embed & Save Error: {e}")
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return (image,)
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return {"ui": {"text": [f"Error: {str(e)}"]}}
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def _embed_metadata_in_image(self, image: Image.Image, metadata_text: str) -> Image.Image:
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"""Embed metadata into image PNG chunks"""
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# Create new image copy
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result_image = image.copy() if hasattr(image, 'copy') else image
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def _format_metadata_for_embedding(self, metadata: dict) -> str:
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"""Convert metadata dict to A1111 format for embedding"""
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lines = []
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# This is handled in the save process with PngInfo
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return result_image
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# Positive prompt
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if 'prompt' in metadata:
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lines.append(metadata['prompt'])
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# Negative prompt
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if 'negative_prompt' in metadata:
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lines.append(f"Negative prompt: {metadata['negative_prompt']}")
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# Parameters line
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params = []
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param_order = ["steps", "sampler", "cfg_scale", "seed", "width", "height", "model_name", "scheduler"]
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for param in param_order:
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if param in metadata and metadata[param] is not None:
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if param == "cfg_scale":
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params.append(f"CFG scale: {metadata[param]}")
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elif param == "model_name":
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params.append(f"Model: {metadata[param]}")
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elif param == "width" and "height" in metadata:
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params.append(f"Size: {metadata['width']}x{metadata['height']}")
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elif param != "height": # Skip height since it's handled with width
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params.append(f"{param.title()}: {metadata[param]}")
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if params:
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lines.append(", ".join(params))
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return "\n".join(lines)
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class MetaManLoadImage:
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