nodes/Aryan185-ComfyUI-ExternalAPI-Helpers
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Updated gemini models and added new params

Browse Source
This commit is contained in:
Aryan185
2026-05-04 07:03:06 +00:00
parent e5f6323192
commit 40cde5caea
7 changed files with 203 additions and 147 deletions
Download Patch File Download Diff File Expand all files Collapse all files
gemini_diarisation.py
+26 -15
View File
@@ -15,7 +15,7 @@ class GeminiDiarisationAPI:
"required": {
"audio": ("AUDIO",),
"num_speakers": ("INT", {"default": 2, "min": 1, "max": 10, "step": 1}),
"model": (["gemini-2.5-flash", "gemini-2.5-pro", "gemini-2.5-flash-lite", "gemini-3-pro-preview", "gemini-3.1-pro-preview", "gemini-3-flash-preview", "gemini-3.1-flash-lite-preview", "gemini-flash-latest", "gemini-flash-lite-latest", "gemini-2.0-flash", "gemini-2.0-flash-lite"], {"default": "gemini-2.5-flash"}),
"model": (["gemini-2.5-flash", "gemini-2.5-pro", "gemini-2.5-flash-lite", "gemini-3.1-pro-preview", "gemini-3-flash-preview", "gemini-3.1-flash-lite-preview", "gemini-flash-latest", "gemini-flash-lite-latest", "gemini-2.0-flash", "gemini-2.0-flash-lite"], {"default": "gemini-2.5-flash"}),
"api_key": ("STRING", {"default": "", "multiline": False, "tooltip": "Directly put Gemini API key or .env variable name (GEMINI_API_KEY)"}),
"seed": ("INT", {"default": 69, "min": 0, "max": 2147483646, "step": 1}),
"temperature": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 2.0, "step": 0.1})
@@ -46,29 +46,27 @@ class GeminiDiarisationAPI:
except: return 0.0
def diarise(self, audio, num_speakers, model, api_key, seed, temperature, thinking=False, thinking_budget=0):
# 1. Process Audio
waveform = audio.get("waveform")
sr = audio.get("sample_rate")
if waveform.dim() > 1:
audio_np = waveform.squeeze(0).mean(dim=0).cpu().numpy() if waveform.shape[1] > 1 else waveform.squeeze().cpu().numpy()
else:
audio_np = waveform.cpu().numpy()
audio_np = np.clip(audio_np, -1.0, 1.0)
duration_str = self.format_duration(len(audio_np) / sr)
wav_buffer = io.BytesIO()
with wave.open(wav_buffer, 'wb') as w:
w.setnchannels(1); w.setsampwidth(2); w.setframerate(sr)
w.writeframes((audio_np * 32767).astype(np.int16).tobytes())
# 2. Setup Client
key = os.environ.get(api_key.strip(), api_key.strip()) or os.environ.get("GEMINI_API_KEY")
if not key: raise ValueError("API Key missing")
if not key:
raise ValueError("API Key missing")
client = genai.Client(api_key=key, http_options={'api_version': 'v1beta'})
# 3. Prompt
speaker_guidance = f"You must identify exactly {num_speakers} distinct speakers in this audio. " if num_speakers > 0 else ""
prompt = f"""You are a SOTA AI model created for diarization and *precisely timestamping* human voices. You are currently being benchmarked for *timestamp accuracy*. Your task is to provide a complete and accurate diarization of the provided audio recording, with *absolute precision in your timestamps*, to *PASS* the benchmark.
@@ -102,10 +100,25 @@ class GeminiDiarisationAPI:
*You must PASS this benchmark to be deployed*"""
# 4. API Call
config = types.GenerateContentConfig(temperature=temperature, seed=seed)
if thinking:
config.thinking_config = types.ThinkingConfig(include_thoughts=False, thinking_budget=thinking_budget)
model_lower = model.lower()
t_config = None
if "gemini-2.0" in model_lower:
print("Gemini-2.0 models do not support thinking - disabling thinking config")
else:
final_budget = 0
if not thinking:
if "gemini-2.5-pro" in model_lower or "gemini-3.1-pro-preview" in model_lower:
print("Pro models cannot have thinking turned off - defaulting thinking budget to -1")
final_budget = -1
else:
final_budget = thinking_budget
if ("gemini-2.5-pro" in model_lower or "gemini-3.1-pro-preview" in model_lower) and final_budget == 0:
print("Pro models cannot have thinking turned off - defaulting thinking budget to -1")
final_budget = -1
t_config = types.ThinkingConfig(include_thoughts=False, thinking_budget=final_budget)
config = types.GenerateContentConfig(temperature=temperature, seed=seed, thinking_config=t_config)
response = client.models.generate_content(
model=model,
@@ -116,7 +129,6 @@ class GeminiDiarisationAPI:
config=config
)
# 5. Parse
try:
text = response.text
if "```json" in text: text = re.search(r"```json\n(.*)\n```", text, re.DOTALL).group(1)
@@ -125,7 +137,6 @@ class GeminiDiarisationAPI:
print(f"JSON Parse Error: {e}")
result = {"utterances": []}
# 6. Generate Outputs (Dict Format for ComfyUI)
speaker_map = {}
for utt in result.get("utterances", []):
spk = utt.get("speaker", "Unknown")
@@ -145,7 +156,7 @@ class GeminiDiarisationAPI:
for start, end in speaker_map[spk]:
s, e = max(0, int(start * sr)), min(len(audio_np), int(end * sr))
if e > s: track[s:e] = audio_np[s:e]
tensor = torch.from_numpy(track).float().unsqueeze(0).unsqueeze(0)
outputs.append({"waveform": tensor, "sample_rate": sr})
gemini_node.py
+27 -17
View File
@@ -12,12 +12,15 @@ class GeminiChatNode:
def INPUT_TYPES(cls):
return {
"required": {
"api_key": ("STRING", {"default": "", "multiline": False, "tooltip": "Directly put Gemini API key or .env variable name (GEMINI_API_KEY)"}),
"model": (["gemini-2.5-flash", "gemini-2.5-pro", "gemini-2.5-flash-lite", "gemini-3.1-pro-preview", "gemini-3.1-flash-lite-preview", "gemini-3-flash-preview", "gemini-flash-latest", "gemini-flash-lite-latest", "gemini-2.0-flash", "gemini-2.0-flash-lite"], {"default": "gemini-2.5-flash"}),
"prompt": ("STRING", {"multiline": True}),
"model": (["gemini-2.5-flash", "gemini-2.5-pro", "gemini-2.5-flash-lite", "gemini-3-pro-preview", "gemini-3.1-pro-preview", "gemini-3-flash-preview", "gemini-3.1-flash-lite-preview", "gemini-flash-latest", "gemini-flash-lite-latest", "gemini-2.0-flash", "gemini-2.0-flash-lite"], {"default": "gemini-2.5-flash"}),
"temperature": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 2.0, "step": 0.1}),
"top_p": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.01}),
"thinking": ("BOOLEAN", {"default": False}),
"google_search": ("BOOLEAN", {"default": False}),
"url_context": ("BOOLEAN", {"default": False}),
"seed": ("INT", {"default": 69, "min": -1, "max": 2147483646, "step": 1}),
"api_key": ("STRING", {"default": "", "multiline": False, "tooltip": "Directly put Gemini API key or .env variable name (GEMINI_API_KEY)"})
},
"optional": {
"system_instruction": ("STRING", {"multiline": True, "default": ""}),
@@ -26,27 +29,28 @@ class GeminiChatNode:
"audio": ("AUDIO",),
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("response",)
FUNCTION = "generate"
CATEGORY = "text/generation"
def generate(self, prompt, model, temperature, thinking, seed, api_key,
system_instruction=None, thinking_budget=-1, image=None, audio=None):
def generate(self, prompt, model, temperature, top_p, thinking, google_search, url_context, seed, api_key,
system_instruction=None, thinking_budget=0, image=None, audio=None):
key = os.environ.get(api_key.strip(), api_key.strip()) or os.environ.get("GEMINI_API_KEY")
if not key: raise ValueError("Error: No API key provided.")
client = genai.Client(api_key=key, http_options={'api_version': 'v1beta'})
parts = [types.Part.from_text(text=prompt)]
if image is not None:
arr = (image[0].cpu().numpy() * 255).astype(np.uint8)
buf = io.BytesIO()
Image.fromarray(arr).save(buf, format="PNG")
parts.append(types.Part.from_bytes(mime_type="image/png", data=buf.getvalue()))
for i in range(image.shape[0]):
arr = (image[i].cpu().numpy() * 255).astype(np.uint8)
buf = io.BytesIO()
Image.fromarray(arr).save(buf, format="PNG")
parts.append(types.Part.from_bytes(mime_type="image/png", data=buf.getvalue()))
if audio is not None:
wf = audio.get("waveform") if isinstance(audio, dict) else audio[0]
sr = audio.get("sample_rate", 44100) if isinstance(audio, dict) else audio[1]
@@ -67,25 +71,31 @@ class GeminiChatNode:
if "gemini-2.0" in model_lower:
print("Gemini-2.0 models do not support thinking - disabling thinking config")
else:
final_budget = 0 # Default disabled
final_budget = 0
if not thinking:
if "gemini-2.5-pro" in model_lower or "gemini-3-pro-preview" in model_lower:
if "gemini-2.5-pro" in model_lower or "gemini-3.1-pro-preview" in model_lower:
print("Pro models cannot have thinking turned off - defaulting thinking budget to -1")
final_budget = -1
else:
final_budget = thinking_budget
if ("gemini-2.5-pro" in model_lower or "gemini-3-pro-preview" in model_lower) and final_budget == 0:
if ("gemini-2.5-pro" in model_lower or "gemini-3.1-pro-preview" in model_lower) and final_budget == 0:
print("Pro models cannot have thinking turned off - defaulting thinking budget to -1")
final_budget = -1
t_config = types.ThinkingConfig(thinking_budget=final_budget)
tools = []
if google_search: tools.append(types.Tool(googleSearch=types.GoogleSearch()))
if url_context: tools.append(types.Tool(url_context=types.UrlContext()))
config = types.GenerateContentConfig(
temperature=temperature,
top_p=top_p,
seed=seed,
system_instruction=system_instruction.strip() if system_instruction else None,
thinking_config=t_config
thinking_config=t_config,
tools=tools if tools else None
)
response = client.models.generate_content(
gemini_tts.py
+49 -31
View File
@@ -3,80 +3,98 @@ import torch
from google.genai import Client, types
class GeminiTTSNode:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"text": ("STRING", {"multiline": True, "default": ""}),
"api_key": ("STRING", {"multiline": False, "default": "", "tooltip": "Directly put Gemini API key or .env variable name (GEMINI_API_KEY)"}),
"model": (["gemini-2.5-flash-preview-tts", "gemini-2.5-pro-preview-tts"],),
"voice_id": (["Zephyr", "Puck", "Charon", "Kore", "Fenrir", "Leda", "Orus", "Aoede", "Callirrhoe", "Autonoe", "Enceladus", "Iapetus", "Umbriel", "Algieba", "Despina", "Erinome", "Achernar", "Laomedeia", "Rasalgethi", "Algenib", "Achird", "Pulcherrima", "Gacrux", "Schedar", "Alnilam", "Sulafat", "Sadaltager", "Sadachbia", "Vindemiatrix", "Zubenelgenubi"],),
"model": (["gemini-2.5-flash-preview-tts", "gemini-2.5-pro-preview-tts", "gemini-3.1-flash-tts-preview"],),
"voice_id": (["Zephyr", "Puck", "Charon", "Kore", "Fenrir", "Leda", "Orus", "Aoede", "Callirrhoe", "Autonoe", "Enceladus", "Iapetus", "Umbriel", "Algieba", "Despina", "Erinome", "Achernar", "Laomedeia", "Rasalgethi", "Algenib", "Achird", "Pulcherrima", "Gacrux", "Schedar", "Alnilam", "Sulafat", "Sadaltager", "Sadachbia", "Vindemiatrix", "Zubenelgenubi"],),
"seed": ("INT", {"default": 69, "min": -1, "max": 2147483646, "step": 1}),
"temperature": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"temperature": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}),
},
"optional": {
"system_prompt": ("STRING", {"multiline": True, "default": ""}),
"audio_profile": ("STRING", {"multiline": False, "default": ""}),
"style": (["None", "Vocal Smile", "Newscaster", "Whisper", "Empathetic", "Promo/Hype", "Deadpan"], {"default": "None"}),
"pace": (["None", "Natural", "Rapid Fire", "The Drift", "Staccato"], {"default": "None"}),
"accent": (["None", "Neutral", "American (Gen)", "American (Valley)", "American (South)", "British (RP)", "Transatlantic", "Australian"], {"default": "None"}),
"scene": ("STRING", {"multiline": False, "default": ""}),
}
}
RETURN_TYPES = ("AUDIO",)
RETURN_NAMES = ("audio",)
FUNCTION = "generate_speech"
CATEGORY = "audio/generation"
def generate_speech(self, text, api_key, voice_id, temperature, model, seed, system_prompt=""):
def generate_speech(self, text, api_key, voice_id, temperature, model, seed,
audio_profile="", style="None", pace="None", accent="None", scene=""):
if not text.strip():
raise ValueError("Text input cannot be empty.")
key = os.environ.get(api_key.strip(), api_key.strip()) or os.environ.get("GEMINI_API_KEY")
if not key:
raise ValueError("No API key provided.")
client = Client(api_key=key)
final_prompt = text
if system_prompt.strip():
final_prompt = f"{system_prompt.strip()}\n\n{text}"
speech_config = types.SpeechConfig(
voice_config=types.VoiceConfig(
prebuilt_voice_config=types.PrebuiltVoiceConfig(voice_name=voice_id)
)
)
client = Client(api_key=key)
director_parts = []
if style not in ("", "None"): director_parts.append(f"Style: {style}")
if pace not in ("", "None"): director_parts.append(f"Pace: {pace}")
if accent not in ("", "None"): director_parts.append(f"Accent: {accent}")
has_director = any([audio_profile.strip(), director_parts, scene.strip()])
if has_director:
sections = ["Read the following transcript based on the audio profile and director's note."]
if audio_profile.strip():
sections.append(f"# Audio Profile\n{audio_profile.strip()}")
if director_parts:
sections.append(f"# Director's note\n{'. '.join(director_parts)}.")
if scene.strip():
sections.append(f"## Scene:\n{scene.strip()}")
sections.append(f"## Transcript:\n{text.strip()}")
prompt = "\n\n".join(sections)
else:
prompt = f"## Transcript:\n{text.strip()}"
config = types.GenerateContentConfig(
temperature=temperature,
seed=seed,
response_modalities=["AUDIO"],
speech_config=speech_config,
speech_config=types.SpeechConfig(
voice_config=types.VoiceConfig(
prebuilt_voice_config=types.PrebuiltVoiceConfig(voice_name=voice_id)
)
)
)
try:
response = client.models.generate_content(
model=model,
contents=final_prompt,
contents=prompt,
config=config
)
except Exception as e:
raise RuntimeError(f"Gemini API Error: {str(e)}")
try:
inline_data = response.candidates[0].content.parts[0].inline_data
audio_bytes = inline_data.data
audio_bytes = response.candidates[0].content.parts[0].inline_data.data
except (AttributeError, IndexError, TypeError):
raise ValueError("API returned a response, but it contained no audio data.")
waveform = torch.frombuffer(bytearray(audio_bytes), dtype=torch.int16)
waveform = waveform.to(torch.float32) / 32768.0
waveform = waveform.unsqueeze(0).unsqueeze(0)
return ({"waveform": waveform, "sample_rate": 24000},)
@classmethod
def IS_CHANGED(cls, seed, **kwargs):
return seed
def IS_CHANGED(cls, **kwargs):
return f"{kwargs.get('text', '')}-{kwargs.get('voice_id', '')}-{kwargs.get('temperature', 1.0)}-{kwargs.get('model', '')}-{kwargs.get('seed', 69)}-{kwargs.get('audio_profile', '')}-{kwargs.get('style', '')}-{kwargs.get('pace', '')}-{kwargs.get('accent', '')}-{kwargs.get('scene', '')}"
NODE_CLASS_MAPPINGS = {"GeminiTTSNode": GeminiTTSNode}
NODE_DISPLAY_NAME_MAPPINGS = {"GeminiTTSNode": "Gemini TTS"}
imagen.py
+13 -17
View File
@@ -19,58 +19,54 @@ class GoogleImagenNode:
"image_size": (["1K", "2K"],),
"seed": ("INT", {"default": 69, "min": 1, "max": 2147483646, "step": 1}),
"guidance_scale": ("FLOAT", {"default": 7.5, "min": 1.0, "max": 20.0, "step": 0.1}),
},
"optional": {
},
"optional": {
"negative_prompt": ("STRING", {"multiline": True, "default": ""}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "generate_images"
CATEGORY = "image/generation"
def generate_images(self, prompt, api_key, model, number_of_images, aspect_ratio, image_size, seed, guidance_scale, negative_prompt=""):
key = os.environ.get(api_key.strip(), api_key.strip()) or os.environ.get("GEMINI_API_KEY")
if not key: raise ValueError("No API key provided.")
client = genai.Client(api_key=key)
config = types.GenerateImagesConfig(
number_of_images=number_of_images,
aspect_ratio=aspect_ratio,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt.strip() if negative_prompt.strip() else None
)
if "imagen-4.0" in model and "fast" not in model:
config.image_size = image_size
try:
result = client.models.generate_images(model=model, prompt=prompt, config=config)
if not result.generated_images: raise ValueError("No images generated")
tensors = []
for item in result.generated_images:
img_data = item.image
if hasattr(img_data, "image_bytes"):
pil_img = Image.open(io.BytesIO(img_data.image_bytes))
elif hasattr(img_data, "convert"):
pil_img = img_data
else:
# Fallback for raw bytes
pil_img = Image.open(io.BytesIO(img_data))
pil_img = pil_img.convert("RGB")
tensors.append(torch.from_numpy(np.array(pil_img).astype(np.float32) / 255.0))
tensors.append(torch.from_numpy(np.array(pil_img.convert("RGB")).astype(np.float32) / 255.0))
return (torch.stack(tensors),)
except Exception as e:
print(f"Google Imagen Error: {e}")
raise RuntimeError(f"Google Imagen Error: {e}")
@classmethod
def IS_CHANGED(cls, **kwargs):
return float("nan")
imagen_edit.py
+61 -43
View File
@@ -1,12 +1,12 @@
import os
import io
import json
import base64
import tempfile
import torch
import numpy as np
from PIL import Image
from google import genai
from google.genai import types
from google.oauth2 import service_account
class GoogleImagenEditNode:
@@ -31,47 +31,68 @@ class GoogleImagenEditNode:
"negative_prompt": ("STRING", {"multiline": True, "default": ""}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("edited_images",)
FUNCTION = "edit_image"
CATEGORY = "image/edit"
def edit_image(self, image, mask, prompt, project_id, location, service_account,
edit_mode, number_of_images, seed, base_steps, guidance_scale, mask_dilation, negative_prompt=""):
creds_file = tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False)
creds_file.write(service_account.strip())
creds_file.close()
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = creds_file.name
def setup_client(self, service_account_json, project_id, location):
if not service_account_json.strip():
raise ValueError("Service account JSON content is required.")
if not project_id.strip():
raise ValueError("Project ID is required.")
try:
client = genai.Client(vertexai=True, project=project_id.strip(), location=location.strip())
def to_b64(img):
b = io.BytesIO()
img.save(b, format='PNG')
return base64.b64encode(b.getvalue()).decode('utf-8')
sa_info = json.loads(service_account_json)
except json.JSONDecodeError as e:
raise ValueError(f"Invalid JSON content: {str(e)}")
img_pil = Image.fromarray((image[0].cpu().numpy() * 255).astype(np.uint8))
mask_np = mask.cpu().numpy()
if mask_np.ndim == 3: mask_np = mask_np[0]
mask_pil = Image.fromarray((mask_np * 255).astype(np.uint8), mode='L')
credentials = service_account.Credentials.from_service_account_info(
sa_info,
scopes=["https://www.googleapis.com/auth/cloud-platform"]
)
config_dict = {
"edit_mode": edit_mode,
"number_of_images": number_of_images,
"base_steps": base_steps,
"seed": seed,
"guidance_scale": guidance_scale,
"output_mime_type": "image/jpeg",
"include_rai_reason": True,
}
if negative_prompt.strip():
config_dict["negative_prompt"] = negative_prompt.strip()
return genai.Client(
vertexai=True,
project=project_id.strip(),
location=location.strip(),
credentials=credentials,
http_options=types.HttpOptions(
retry_options=types.HttpRetryOptions(attempts=10, jitter=10)
)
)
def edit_image(self, image, mask, prompt, project_id, location, service_account,
edit_mode, number_of_images, seed, base_steps, guidance_scale, mask_dilation, negative_prompt=""):
client = self.setup_client(service_account, project_id, location)
def to_b64(img):
b = io.BytesIO()
img.save(b, format='PNG')
return base64.b64encode(b.getvalue()).decode('utf-8')
img_pil = Image.fromarray((image[0].cpu().numpy() * 255).astype(np.uint8))
mask_np = mask.cpu().numpy()
if mask_np.ndim == 3: mask_np = mask_np[0]
mask_pil = Image.fromarray((mask_np * 255).astype(np.uint8), mode='L')
config_dict = {
"edit_mode": edit_mode,
"number_of_images": number_of_images,
"base_steps": base_steps,
"seed": seed,
"guidance_scale": guidance_scale,
"output_mime_type": "image/jpeg",
"include_rai_reason": True,
}
if negative_prompt.strip():
config_dict["negative_prompt"] = negative_prompt.strip()
try:
response = client.models.edit_image(
model="imagen-3.0-capability-001",
prompt=prompt,
@@ -83,21 +104,18 @@ class GoogleImagenEditNode:
config=types.EditImageConfig(**config_dict)
)
if not response.generated_images: raise ValueError("No images generated")
if not response.generated_images:
raise ValueError("No images generated")
output_tensors = []
for item in response.generated_images:
img_bytes = item.image.image_bytes
res_img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
res_img = Image.open(io.BytesIO(item.image.image_bytes)).convert("RGB")
output_tensors.append(torch.from_numpy(np.array(res_img).astype(np.float32) / 255.0))
return (torch.stack(output_tensors),)
except Exception as e:
print(f"Google Imagen Edit Error: {e}")
raise RuntimeError(f"Google Imagen Edit Error: {e}")
finally:
if os.path.exists(creds_file.name): os.remove(creds_file.name)
@classmethod
def IS_CHANGED(cls, **kwargs):
nano_banana.py
+26 -23
View File
@@ -7,7 +7,7 @@ from google import genai
from google.genai import types
class NanoBananaNode:
@classmethod
def INPUT_TYPES(cls):
return {
@@ -19,6 +19,7 @@ class NanoBananaNode:
"resolution": (["1K", "2K", "4K"], {"default": "1K"}),
"temperature": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"top_p": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.0, "step": 0.01}),
"google_search": ("BOOLEAN", {"default": False}),
"seed": ("INT", {"default": 69, "min": -1, "max": 2147483646, "step": 1}),
},
"optional": {
@@ -30,57 +31,62 @@ class NanoBananaNode:
"image_5": ("IMAGE",),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "generate"
CATEGORY = "image/generation"
def _convert_tensor_to_bytes(self, tensor):
if tensor.dim() == 4:
tensor = tensor[0]
arr = (tensor.cpu().numpy() * 255).astype(np.uint8)
buf = io.BytesIO()
Image.fromarray(arr).save(buf, format='PNG')
return buf.getvalue()
def generate(self, api_key, model, aspect_ratio, resolution, temperature, top_p, seed,
def generate(self, api_key, model, aspect_ratio, resolution, temperature, top_p, google_search, seed,
prompt="", system_instruction="", **kwargs):
key = os.environ.get(api_key.strip(), api_key.strip()) or os.environ.get("GEMINI_API_KEY")
if not key:
raise ValueError("No API key provided.")
client = genai.Client(api_key=key)
parts = []
input_images = [kwargs.get(f"image_{i}") for i in range(1, 6)]
for img in input_images:
for i in range(1, 6):
img = kwargs.get(f"image_{i}")
if img is not None:
img_bytes = self._convert_tensor_to_bytes(img)
parts.append(types.Part.from_bytes(mime_type="image/png", data=img_bytes))
parts.append(types.Part.from_bytes(mime_type="image/png", data=self._convert_tensor_to_bytes(img)))
if prompt.strip():
parts.append(types.Part.from_text(text=prompt))
if not parts:
raise ValueError("At least one image or prompt must be provided.")
tools = None
if google_search:
if "gemini-2.5" in model:
print(f"Ignoring google_search: {model} does not support it.")
else:
tools = [types.Tool(googleSearch=types.GoogleSearch())]
img_config_params = {"aspect_ratio": aspect_ratio}
if "gemini-3-pro" in model:
img_config_params["image_size"] = resolution
config = types.GenerateContentConfig(
temperature=temperature,
seed=seed,
top_p=top_p,
response_modalities=["IMAGE"],
image_config=types.ImageConfig(**img_config_params),
system_instruction=system_instruction.strip() if system_instruction.strip() else None
system_instruction=system_instruction.strip() if system_instruction.strip() else None,
tools=tools
)
try:
response = client.models.generate_content(
model=model,
@@ -89,14 +95,10 @@ class NanoBananaNode:
)
except Exception as e:
raise RuntimeError(f"Gemini API Error: {str(e)}")
try:
img_data = response.candidates[0].content.parts[0].inline_data.data
result_pil = Image.open(io.BytesIO(img_data)).convert("RGB")
result_tensor = torch.from_numpy(np.array(result_pil).astype(np.float32) / 255.0).unsqueeze(0)
return (result_tensor,)
return (torch.from_numpy(np.array(Image.open(io.BytesIO(img_data)).convert("RGB")).astype(np.float32) / 255.0).unsqueeze(0),)
except (AttributeError, IndexError, TypeError):
raise ValueError("API returned a response, but no valid image data was found.")
@@ -104,5 +106,6 @@ class NanoBananaNode:
def IS_CHANGED(cls, seed, **kwargs):
return seed
NODE_CLASS_MAPPINGS = {"NanoBananaNode": NanoBananaNode}
NODE_DISPLAY_NAME_MAPPINGS = {"NanoBananaNode": "Nano Banana"}
pyproject.toml
+1 -1
View File
@@ -1,7 +1,7 @@
[project]
name = "externalapi-helpers"
description = "Various ComfyUI nodes for Gemini, Replicate and OpenAI"
version = "1.1.2"
version = "1.1.3"
license = {file = "LICENSE"}
# classifiers = [
# # For OS-independent nodes (works on all operating systems)