added batching for GPT image-1 node
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Fill
+61
-43
@@ -109,31 +109,33 @@ class FL_GPT_Image1:
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return img_tensor
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def _process_tensor_to_pil(self, tensor, name="Image"):
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"""Convert a tensor to a PIL image for API submission"""
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"""Convert a tensor (or a batch of tensors) to a list of PIL images."""
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try:
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if tensor is None:
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self._log(f"{name} is None, skipping")
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return None
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return []
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# Ensure tensor is in correct format [1, H, W, 3]
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if len(tensor.shape) == 4 and tensor.shape[0] == 1:
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# Get first frame image
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image_np = tensor[0].cpu().numpy()
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# Handle batch of images [B, H, W, 3] or single image [1, H, W, 3]
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if len(tensor.shape) == 4:
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pil_images = []
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for i in range(tensor.shape[0]):
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image_np = tensor[i].cpu().numpy()
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image_np = (image_np * 255).astype(np.uint8)
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pil_image = Image.fromarray(image_np)
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pil_images.append(pil_image)
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if not pil_images:
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self._log(f"{name} processed but no images were created.")
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return []
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# Convert to uint8 format for PIL
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image_np = (image_np * 255).astype(np.uint8)
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# Create PIL image
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pil_image = Image.fromarray(image_np)
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self._log(f"{name} processed successfully, size: {pil_image.width}x{pil_image.height}")
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return pil_image
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self._log(f"{name} batch processed successfully, {len(pil_images)} images, size: {pil_images[0].width}x{pil_images[0].height}")
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return pil_images
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else:
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self._log(f"{name} format incorrect: {tensor.shape}")
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return None
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return []
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except Exception as e:
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self._log(f"Error processing {name}: {str(e)}")
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return None
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return []
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def _encode_image_to_base64(self, pil_image, format="PNG"):
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"""Convert PIL image to base64 string"""
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@@ -161,33 +163,40 @@ class FL_GPT_Image1:
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"Authorization": f"Bearer {api_key}"
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}
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# Create a multipart form-data request
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multipart_data = {}
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# Create a multipart form-data request.
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# We use a list of tuples for 'files' to support multiple images.
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form_data = []
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# Add all text fields to the multipart data
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for key, value in payload.items():
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if key not in ["image", "mask"]:
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multipart_data[key] = (None, str(value))
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# Add image file if present
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form_data.append((key, (None, str(value))))
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# Add image file(s) if present
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if "image" in payload and payload["image"] is not None:
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if isinstance(payload["image"], bytes):
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multipart_data["image"] = ("image.png", payload["image"], "image/png")
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self._log("Added image file to multipart request")
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images = payload["image"]
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if not isinstance(images, list):
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images = [images] # Ensure it's a list for consistency
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for i, img_bytes in enumerate(images):
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if isinstance(img_bytes, bytes):
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form_data.append(('image[]', (f"image_{i}.png", img_bytes, "image/png")))
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self._log(f"Added {len(images)} image file(s) to multipart request")
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# Add mask file if present
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if "mask" in payload and payload["mask"] is not None:
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if isinstance(payload["mask"], bytes):
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multipart_data["mask"] = ("mask.png", payload["mask"], "image/png")
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form_data.append(('mask', ("mask.png", payload["mask"], "image/png")))
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self._log("Added mask file to multipart request")
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self._log(f"Sending multipart request with {len(multipart_data)} fields")
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self._log(f"Sending multipart request with {len(form_data)} parts")
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# Use requests to send the multipart form data
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response = requests.post(
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url,
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headers=headers,
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files=multipart_data, # This automatically sets the correct content-type
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files=form_data, # Pass the list of tuples here
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timeout=120
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)
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else:
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@@ -365,26 +374,35 @@ class FL_GPT_Image1:
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if image is not None:
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endpoint = "edits"
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# Process the input image
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pil_image = self._process_tensor_to_pil(image, "Input Image")
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if pil_image is None:
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return self._create_error_image("Failed to process input image"), "Error: Failed to process input image"
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# Process the input image batch
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pil_images = self._process_tensor_to_pil(image, "Input Image")
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if not pil_images:
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return self._create_error_image("Failed to process input image(s)"), "Error: Failed to process input image(s)"
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if len(pil_images) > 16:
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self._log("Error: A maximum of 16 images can be provided for editing.")
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return self._create_error_image("Too many images (max 16)"), "Error: A maximum of 16 images can be provided for editing."
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self._log(f"Setting up image editing request for {len(pil_images)} image(s)")
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self._log("Setting up image editing request")
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# Convert PIL images to a list of bytes
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image_bytes_list = []
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for i, pil_image in enumerate(pil_images):
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img_byte_arr = BytesIO()
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pil_image.save(img_byte_arr, format='PNG')
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image_bytes_list.append(img_byte_arr.getvalue())
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# Convert PIL image directly to bytes
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img_byte_arr = BytesIO()
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pil_image.save(img_byte_arr, format='PNG')
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img_bytes = img_byte_arr.getvalue()
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self._log(f"Converted image to bytes, size: {len(img_bytes)} bytes")
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self._log(f"Converted {len(image_bytes_list)} image(s) to bytes")
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# Add image bytes to payload
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payload["image"] = img_bytes
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# Add image bytes list to payload
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payload["image"] = image_bytes_list
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# Process mask if provided
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# Process mask if provided. A single mask is applied to all images.
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if mask is not None:
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pil_mask = self._process_tensor_to_pil(mask, "Mask Image")
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if pil_mask is not None:
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# The mask is still a single image tensor, so we expect a list with one item
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pil_masks = self._process_tensor_to_pil(mask, "Mask Image")
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if pil_masks:
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pil_mask = pil_masks[0] # Get the first (and only) mask
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# Convert mask to bytes
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mask_byte_arr = BytesIO()
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pil_mask.save(mask_byte_arr, format='PNG')
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+1
-1
@@ -1,7 +1,7 @@
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[project]
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name = "comfyui_fill-nodes"
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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."
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version = "1.6.0"
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version = "1.6.1"
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license = "LICENSE"
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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"]
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