bigcat88's progress on adding Google Gemini Image node

comfy_api_nodes/nodes_gemini.py

@@ -4,7 +4,6 @@ See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/infer
 """
 from __future__ import annotations
 
-
 import json
 import time
 import os
@@ -75,6 +74,131 @@ def get_gemini_endpoint(
     )
 
 
+def create_image_parts(image_input: torch.Tensor) -> list[GeminiPart]:
+    """
+    Convert image tensor input to Gemini API compatible parts.
+
+    Args:
+        image_input: Batch of image tensors from ComfyUI.
+
+    Returns:
+        List of GeminiPart objects containing the encoded images.
+    """
+    image_parts: list[GeminiPart] = []
+    for image_index in range(image_input.shape[0]):
+        image_as_b64 = tensor_to_base64_string(
+            image_input[image_index].unsqueeze(0)
+        )
+        image_parts.append(
+            GeminiPart(
+                inlineData=GeminiInlineData(
+                    mimeType=GeminiMimeType.image_png,
+                    data=image_as_b64,
+                )
+            )
+        )
+    return image_parts
+
+
+def create_text_part(text: str) -> GeminiPart:
+    """
+    Create a text part for the Gemini API request.
+
+    Args:
+        text: The text content to include in the request.
+
+    Returns:
+        A GeminiPart object with the text content.
+    """
+    return GeminiPart(text=text)
+
+
+def get_parts_from_response(
+    response: GeminiGenerateContentResponse
+) -> list[GeminiPart]:
+    """
+    Extract all parts from the Gemini API response.
+
+    Args:
+        response: The API response from Gemini.
+
+    Returns:
+        List of response parts from the first candidate.
+    """
+    return response.candidates[0].content.parts
+
+
+def get_parts_by_type(
+    response: GeminiGenerateContentResponse, part_type: Literal["text"] | str
+) -> list[GeminiPart]:
+    """
+    Filter response parts by their type.
+
+    Args:
+        response: The API response from Gemini.
+        part_type: Type of parts to extract ("text" or a MIME type).
+
+    Returns:
+        List of response parts matching the requested type.
+    """
+    parts = []
+    for part in get_parts_from_response(response):
+        if part_type == "text" and hasattr(part, "text") and part.text:
+            parts.append(part)
+        elif (
+            hasattr(part, "inlineData")
+            and part.inlineData
+            and part.inlineData.mimeType == part_type
+        ):
+            parts.append(part)
+        # Skip parts that don't match the requested type
+    return parts
+
+
+def get_text_from_response(response: GeminiGenerateContentResponse) -> str:
+    """
+    Extract and concatenate all text parts from the response.
+
+    Args:
+        response: The API response from Gemini.
+
+    Returns:
+        Combined text from all text parts in the response.
+    """
+    parts = get_parts_by_type(response, "text")
+    return "\n".join([part.text for part in parts])
+
+
+def get_image_from_response(response: GeminiGenerateContentResponse) -> torch.Tensor:
+    image_tensors: list[torch.Tensor] = []
+    # TODO:
+    """
+    TODO something like this but without download but getting it from response:   
+
+    # Process each image in the data array
+    async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=timeout)) as session:
+        for img_data in data:
+            img_bytes: bytes
+            if img_data.b64_json:
+                img_bytes = base64.b64decode(img_data.b64_json)
+            elif img_data.url:
+                if node_id:
+                    PromptServer.instance.send_progress_text(f"Result URL: {img_data.url}", node_id)
+                async with session.get(img_data.url) as resp:
+                    if resp.status != 200:
+                        raise ValueError("Failed to download generated image")
+                    img_bytes = await resp.read()
+            else:
+                raise ValueError("Invalid image payload – neither URL nor base64 data present.")
+
+            pil_img = Image.open(BytesIO(img_bytes)).convert("RGBA")
+            arr = np.asarray(pil_img).astype(np.float32) / 255.0
+            image_tensors.append(torch.from_numpy(arr))
+
+    """
+    return torch.stack(image_tensors, dim=0)
+
+
 class GeminiNode(ComfyNodeABC):
     """
     Node to generate text responses from a Gemini model.
@@ -159,59 +283,6 @@ class GeminiNode(ComfyNodeABC):
     CATEGORY = "api node/text/Gemini"
     API_NODE = True
 
-    def get_parts_from_response(
-        self, response: GeminiGenerateContentResponse
-    ) -> list[GeminiPart]:
-        """
-        Extract all parts from the Gemini API response.
-
-        Args:
-            response: The API response from Gemini.
-
-        Returns:
-            List of response parts from the first candidate.
-        """
-        return response.candidates[0].content.parts
-
-    def get_parts_by_type(
-        self, response: GeminiGenerateContentResponse, part_type: Literal["text"] | str
-    ) -> list[GeminiPart]:
-        """
-        Filter response parts by their type.
-
-        Args:
-            response: The API response from Gemini.
-            part_type: Type of parts to extract ("text" or a MIME type).
-
-        Returns:
-            List of response parts matching the requested type.
-        """
-        parts = []
-        for part in self.get_parts_from_response(response):
-            if part_type == "text" and hasattr(part, "text") and part.text:
-                parts.append(part)
-            elif (
-                hasattr(part, "inlineData")
-                and part.inlineData
-                and part.inlineData.mimeType == part_type
-            ):
-                parts.append(part)
-            # Skip parts that don't match the requested type
-        return parts
-
-    def get_text_from_response(self, response: GeminiGenerateContentResponse) -> str:
-        """
-        Extract and concatenate all text parts from the response.
-
-        Args:
-            response: The API response from Gemini.
-
-        Returns:
-            Combined text from all text parts in the response.
-        """
-        parts = self.get_parts_by_type(response, "text")
-        return "\n".join([part.text for part in parts])
-
     def create_video_parts(self, video_input: IO.VIDEO, **kwargs) -> list[GeminiPart]:
         """
         Convert video input to Gemini API compatible parts.
@@ -271,43 +342,6 @@ class GeminiNode(ComfyNodeABC):
             )
         return audio_parts
 
-    def create_image_parts(self, image_input: torch.Tensor) -> list[GeminiPart]:
-        """
-        Convert image tensor input to Gemini API compatible parts.
-
-        Args:
-            image_input: Batch of image tensors from ComfyUI.
-
-        Returns:
-            List of GeminiPart objects containing the encoded images.
-        """
-        image_parts: list[GeminiPart] = []
-        for image_index in range(image_input.shape[0]):
-            image_as_b64 = tensor_to_base64_string(
-                image_input[image_index].unsqueeze(0)
-            )
-            image_parts.append(
-                GeminiPart(
-                    inlineData=GeminiInlineData(
-                        mimeType=GeminiMimeType.image_png,
-                        data=image_as_b64,
-                    )
-                )
-            )
-        return image_parts
-
-    def create_text_part(self, text: str) -> GeminiPart:
-        """
-        Create a text part for the Gemini API request.
-
-        Args:
-            text: The text content to include in the request.
-
-        Returns:
-            A GeminiPart object with the text content.
-        """
-        return GeminiPart(text=text)
-
     async def api_call(
         self,
         prompt: str,
@@ -323,11 +357,11 @@ class GeminiNode(ComfyNodeABC):
         validate_string(prompt, strip_whitespace=False)
 
         # Create parts list with text prompt as the first part
-        parts: list[GeminiPart] = [self.create_text_part(prompt)]
+        parts: list[GeminiPart] = [create_text_part(prompt)]
 
         # Add other modal parts
         if images is not None:
-            image_parts = self.create_image_parts(images)
+            image_parts = create_image_parts(images)
             parts.extend(image_parts)
         if audio is not None:
             parts.extend(self.create_audio_parts(audio))
@@ -351,7 +385,7 @@ class GeminiNode(ComfyNodeABC):
         ).execute()
 
         # Get result output
-        output_text = self.get_text_from_response(response)
+        output_text = get_text_from_response(response)
         if unique_id and output_text:
             # Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button.
             render_spec = {
@@ -462,12 +496,112 @@ class GeminiInputFiles(ComfyNodeABC):
         return (files,)
 
 
+class GeminiImage(ComfyNodeABC):
+
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "prompt": (
+                    IO.STRING,
+                    {
+                        "multiline": True,
+                        "default": "",
+                        "tooltip": "Text prompt for generation",
+                    },
+                ),
+            },
+            "optional": {
+                "seed": (
+                    IO.INT,
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 2**31 - 1,
+                        "step": 1,
+                        "display": "number",
+                        "control_after_generate": True,
+                        "tooltip": "not implemented yet in backend",
+                    },
+                ),
+                # TODO: later we can add this parameter later
+                # "n": (
+                #     IO.INT,
+                #     {
+                #         "default": 1,
+                #         "min": 1,
+                #         "max": 8,
+                #         "step": 1,
+                #         "display": "number",
+                #         "tooltip": "How many images to generate",
+                #     },
+                # ),
+                "image": (
+                    IO.IMAGE,
+                    {
+                        "default": None,
+                        "tooltip": "Optional reference images (Max 3).",
+                    },
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = (IO.IMAGE, IO.STRING)
+    FUNCTION = "api_call"
+    CATEGORY = "api node/image/Gemini"
+    DESCRIPTION = "Edit images synchronously via Google API."
+    API_NODE = True
+
+    async def api_call(
+        self,
+        prompt,
+        n=1,
+        image=None,
+        **kwargs,
+    ):
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        parts: list[GeminiPart] = [create_text_part(prompt)]
+
+        if image is not None:
+            image_parts = create_image_parts(image)
+            parts.extend(image_parts)
+
+        response = await SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=f"{GEMINI_BASE_ENDPOINT}/gemini-2.5-flash-image-preview",
+                method=HttpMethod.POST,
+                request_model=GeminiGenerateContentRequest,
+                response_model=GeminiGenerateContentResponse,
+            ),
+            request=GeminiGenerateContentRequest(
+                contents=[
+                    GeminiContent(
+                        role="user",
+                        parts=parts,
+                    )
+                ]
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+
+        output_image = get_image_from_response(response)
+        output_text = get_text_from_response(response)
+        return output_image, output_text
+
+
 NODE_CLASS_MAPPINGS = {
     "GeminiNode": GeminiNode,
     "GeminiInputFiles": GeminiInputFiles,
+    "GeminiImage": GeminiImage,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "GeminiNode": "Google Gemini",
     "GeminiInputFiles": "Gemini Input Files",
+    "GeminiImage": "Gemini Image",
 }