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Added support for an input to be load_image node + gemini

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This commit is contained in:
Ganapathy Hari Narayan 2025-05-16 15:14:03 -07:00
parent 88faf138c3
commit 90cf3fccfa
1 changed files with 24 additions and 3 deletions
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27
comfy_api_nodes/nodes_vertex_gemini.py
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@ -4,7 +4,10 @@ from inspect import cleandoc
from google import genai
from google.genai.types import HttpOptions, Part
from google.cloud import storage
from PIL import Image
import numpy as np
import folder_paths
from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
from comfy_api_nodes.apinode_utils import validate_string
from server import PromptServer
@ -49,6 +52,9 @@ class VertexGeminiAPI(ComfyNodeABC):
@classmethod
def INPUT_TYPES(cls) -> InputTypeDict:
input_dir = folder_paths.get_input_directory()
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
files = folder_paths.filter_files_content_types(files, ["image"])
return {
"required": {
"prompt": (
@ -68,6 +74,9 @@ class VertexGeminiAPI(ComfyNodeABC):
)
},
"optional": {
"image": (IO.IMAGE, {
"tooltip": "uploaded image for gemini api"
}),
"image_path": (
IO.STRING,
{
@ -93,18 +102,30 @@ class VertexGeminiAPI(ComfyNodeABC):
prompt,
model="gemini-2.0-flash-001",
image_path=None,
image=None,
unique_id=None,
**kwargs
):
validate_string(prompt, strip_whitespace=False)
client = genai.Client(http_options=HttpOptions(api_version="v1"))
contents = [prompt]
if image_path:
storage_client = storage.Client()
if image is not None:
print("Processing input img")
if image.dim() == 4:
image = image[0]
# Ensure shape is (H, W, C)
image_np = image.detach().cpu().numpy()
# Convert from float32 (0–1) to uint8 (0–255)
image_np = (image_np * 255).clip(0, 255).astype(np.uint8)
# Convert to PIL image and save
img = Image.fromarray(image_np)
source_file = "./input/temp_img.jpg"
img.save(source_file, format="JPEG")
print("processed input image")
storage_client = storage.Client()
# Define bucket and file info
bucket_name = "comfyui-interview-temp"
source_file = image_path # local path
file_name = os.path.basename(source_file)
destination_blob = f"{uuid.uuid4()}/{file_name}" # name in bucket
# Upload