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Merge branch 'master' into matt/be-786-phase-1-remove-enable-assets-from-oss-assets-always-on-cloud

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Matt Miller 2026-06-30 14:28:25 -07:00 committed by GitHub
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9 changed files with 196 additions and 18 deletions
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1
comfy_api_nodes/apis/gemini.py
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@ -121,6 +121,7 @@ class GeminiGenerationConfig(BaseModel):
topK: int | None = Field(None, ge=1)
topP: float | None = Field(None, ge=0.0, le=1.0)
thinkingConfig: GeminiThinkingConfig | None = Field(None)
responseModalities: list[str] | None = Field(None)
class GeminiImageOutputOptions(BaseModel):

174
comfy_api_nodes/nodes_gemini.py
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@ -13,7 +13,7 @@ import torch
from typing_extensions import override
import folder_paths
from comfy_api.latest import IO, ComfyExtension, Input, Types
from comfy_api.latest import IO, ComfyExtension, Input, InputImpl, Types
from comfy_api_nodes.apis.gemini import (
GeminiContent,
GeminiFileData,
@ -37,6 +37,7 @@ from comfy_api_nodes.util import (
audio_to_base64_string,
bytesio_to_image_tensor,
download_url_to_image_tensor,
download_url_to_video_output,
get_number_of_images,
sync_op,
tensor_to_base64_string,
@ -45,6 +46,7 @@ from comfy_api_nodes.util import (
upload_images_to_comfyapi,
upload_video_to_comfyapi,
validate_string,
validate_video_duration,
video_to_base64_string,
)
@ -229,10 +231,29 @@ async def get_image_from_response(response: GeminiGenerateContentResponse, thoug
return torch.cat(image_tensors, dim=0)
async def get_video_from_response(
response: GeminiGenerateContentResponse, cls: type[IO.ComfyNode] | None = None
) -> InputImpl.VideoFromFile:
parts = get_parts_by_type(response, "video/*")
for part in parts:
if part.inlineData and part.inlineData.data:
return InputImpl.VideoFromFile(BytesIO(base64.b64decode(part.inlineData.data)))
if part.fileData and part.fileData.fileUri:
return await download_url_to_video_output(part.fileData.fileUri, cls=cls)
model_message = get_text_from_response(response).strip()
if model_message:
raise ValueError(f"Gemini did not generate a video. Model response: {model_message}")
raise ValueError(
"Gemini did not generate a video. Try rephrasing your prompt, "
"shortening the requested duration, or reducing the number of input images/videos."
)
def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | None:
if not response.modelVersion:
return None
# Define prices (Cost per 1,000,000 tokens), see https://cloud.google.com/vertex-ai/generative-ai/pricing
output_video_tokens_price = 0.0
if response.modelVersion == "gemini-2.5-pro":
input_tokens_price = 1.25
output_text_tokens_price = 10.0
@ -265,6 +286,11 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
input_tokens_price = 0.25
output_text_tokens_price = 1.50
output_image_tokens_price = 30.0
elif response.modelVersion == "gemini-omni-flash-preview":
input_tokens_price = 2.145
output_text_tokens_price = 12.87
output_image_tokens_price = 0.0
output_video_tokens_price = 25.025
else:
return None
final_price = response.usageMetadata.promptTokenCount * input_tokens_price
@ -272,6 +298,8 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
for i in response.usageMetadata.candidatesTokensDetails:
if i.modality == Modality.IMAGE:
final_price += output_image_tokens_price * i.tokenCount # for Nano Banana models
elif i.modality == Modality.VIDEO:
final_price += output_video_tokens_price * i.tokenCount # for Omni Flash
else:
final_price += output_text_tokens_price * i.tokenCount
if response.usageMetadata.thoughtsTokenCount:
@ -1531,6 +1559,149 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
)
OMNI_MAX_IMAGES = 14
OMNI_MAX_VIDEOS = 3
OMNI_MODELS: dict[str, str] = {
"Omni Flash": "gemini-omni-flash-preview",
}
def _omni_flash_inputs() -> list[Input]:
"""Per-model inputs for the Omni video DynamicCombo (prompt + reference media + sampling)."""
return [
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Describe the video to generate. Specify the length and aspect ratio directly in the "
'prompt, e.g. "a 6-second clip in 16:9". Length may be 3-10 seconds; the aspect ratio must be '
"16:9 (landscape) or 9:16 (portrait). The output is 720p, 24 FPS, with audio.",
),
IO.Autogrow.Input(
"images",
template=IO.Autogrow.TemplateNames(
IO.Image.Input("image"),
names=[f"image_{i}" for i in range(1, OMNI_MAX_IMAGES + 1)],
min=0,
),
tooltip=f"Optional reference image(s) to guide or animate the video. Up to {OMNI_MAX_IMAGES} images.",
),
IO.Autogrow.Input(
"videos",
template=IO.Autogrow.TemplateNames(
IO.Video.Input("video"),
names=[f"video_{i}" for i in range(1, OMNI_MAX_VIDEOS + 1)],
min=0,
),
tooltip=f"Optional reference video(s) to guide or edit. Up to {OMNI_MAX_VIDEOS} videos, "
f"each up to 10 seconds long.",
),
IO.Float.Input(
"temperature",
default=1.0,
min=0.0,
max=2.0,
step=0.01,
tooltip="Controls randomness. Lower is more focused/deterministic, higher is more varied.",
advanced=True,
),
IO.Float.Input(
"top_p",
default=0.95,
min=0.0,
max=1.0,
step=0.01,
tooltip="Nucleus sampling: sample from the smallest token set whose cumulative probability reaches top_p.",
advanced=True,
),
]
class GeminiVideoOmni(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="GeminiVideoOmni",
display_name="Google Gemini Omni (Video)",
category="partner/video/Gemini",
essentials_category="Video Generation",
description="Generate a video with audio from a text prompt using Google's Gemini Omni Flash model. "
"Optionally provide reference images and/or videos to guide or edit the result. Describe the desired "
"length (3-10s) and aspect ratio (16:9 or 9:16) directly in the prompt.",
inputs=[
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option("Omni Flash", _omni_flash_inputs()),
],
tooltip="The Gemini video model used to generate the video.",
),
IO.Int.Input(
"seed",
default=42,
min=0,
max=2147483647,
control_after_generate=True,
tooltip="Seed controls whether the node should re-run; "
"results are non-deterministic regardless of seed.",
),
],
outputs=[
IO.Video.Output(),
IO.String.Output(),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(
expr='{"type":"usd","usd":0.146,"format":{"suffix":"/second","approximate":true}}'
),
)
@classmethod
async def execute(cls, model: dict, seed: int) -> IO.NodeOutput:
prompt = model.get("prompt") or ""
validate_string(prompt, strip_whitespace=True, min_length=1)
model_id = OMNI_MODELS[model["model"]]
images = [t for t in (model.get("images") or {}).values() if t is not None]
videos = [v for v in (model.get("videos") or {}).values() if v is not None]
if sum(get_number_of_images(t) for t in images) > OMNI_MAX_IMAGES:
raise ValueError(f"The current maximum number of supported images is {OMNI_MAX_IMAGES}.")
if len(videos) > OMNI_MAX_VIDEOS:
raise ValueError(f"The current maximum number of supported videos is {OMNI_MAX_VIDEOS}.")
for video in videos:
validate_video_duration(video, max_duration=10)
parts: list[GeminiPart] = []
if images or videos:
parts.extend(await build_gemini_media_parts(cls, images, [], videos))
parts.append(GeminiPart(text=prompt))
response = await sync_op(
cls,
ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model_id}", method="POST"),
data=GeminiGenerateContentRequest(
contents=[GeminiContent(role=GeminiRole.user, parts=parts)],
generationConfig=GeminiGenerationConfig(
responseModalities=["TEXT", "VIDEO"],
temperature=model.get("temperature", 1.0),
topP=model.get("top_p", 0.95),
),
),
response_model=GeminiGenerateContentResponse,
price_extractor=calculate_tokens_price,
)
return IO.NodeOutput(
await get_video_from_response(response, cls=cls),
get_text_from_response(response),
)
class GeminiExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@ -1541,6 +1712,7 @@ class GeminiExtension(ComfyExtension):
GeminiImage2,
GeminiNanoBanana2,
GeminiNanoBanana2V2,
GeminiVideoOmni,
GeminiInputFiles,
]

10
comfy_extras/nodes_cond.py
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@ -8,7 +8,8 @@ class CLIPTextEncodeControlnet(io.ComfyNode):
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="CLIPTextEncodeControlnet",
category="experimental/conditioning",
display_name="CLIP Text Encode (Controlnet)",
category="model/conditioning",
inputs=[
io.Clip.Input("clip"),
io.Conditioning.Input("conditioning"),
@ -35,11 +36,12 @@ class T5TokenizerOptions(io.ComfyNode):
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="T5TokenizerOptions",
category="experimental/conditioning",
display_name="T5 Tokenizer Options",
category="model/conditioning",
inputs=[
io.Clip.Input("clip"),
io.Int.Input("min_padding", default=0, min=0, max=10000, step=1, advanced=True),
io.Int.Input("min_length", default=0, min=0, max=10000, step=1, advanced=True),
io.Int.Input("min_padding", default=0, min=0, max=10000, step=1),
io.Int.Input("min_length", default=0, min=0, max=10000, step=1),
],
outputs=[io.Clip.Output()],
is_experimental=True,

4
comfy_extras/nodes_custom_sampler.py
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@ -1070,7 +1070,7 @@ class AddNoise(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="AddNoise",
category="experimental/custom_sampling/noise",
category="model/sampling/noise",
is_experimental=True,
inputs=[
io.Model.Input("model"),
@ -1120,7 +1120,7 @@ class ManualSigmas(io.ComfyNode):
return io.Schema(
node_id="ManualSigmas",
search_aliases=["custom noise schedule", "define sigmas"],
category="experimental/custom_sampling",
category="model/sampling/sigmas",
is_experimental=True,
inputs=[
io.String.Input("sigmas", default="1, 0.5", multiline=False)

6
comfy_extras/nodes_photomaker.py
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@ -123,7 +123,8 @@ class PhotoMakerLoader(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="PhotoMakerLoader",
category="experimental/photomaker",
display_name="Load PhotoMaker Model",
category="model/loaders",
inputs=[
io.Combo.Input("photomaker_model_name", options=folder_paths.get_filename_list("photomaker")),
],
@ -149,7 +150,8 @@ class PhotoMakerEncode(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="PhotoMakerEncode",
category="experimental/photomaker",
display_name="PhotoMaker Encode",
category="model/conditioning/photomaker",
inputs=[
io.Photomaker.Input("photomaker"),
io.Image.Input("image"),

2
comfy_extras/nodes_stable_cascade.py
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@ -119,7 +119,7 @@ class StableCascade_SuperResolutionControlnet(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="StableCascade_SuperResolutionControlnet",
category="experimental/stable_cascade",
category="experimental/stable cascade",
is_experimental=True,
inputs=[
io.Image.Input("image"),

4
comfy_extras/nodes_triposplat.py
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@ -143,7 +143,7 @@ class VAEDecodeTripoSplat(IO.ComfyNode):
return IO.Schema(
node_id="VAEDecodeTripoSplat",
display_name="TripoSplat Decode",
category="3d/latent",
category="model/latent/triposplat",
description="Decode the sampled TripoSplat latent into a 3D gaussian splat. "
"Modify the number of gaussians to vary the density.",
inputs=[
@ -188,7 +188,7 @@ class TripoSplatSamplingPreview(IO.ComfyNode):
return IO.Schema(
node_id="TripoSplatSamplingPreview",
display_name="TripoSplat Sampling Preview",
category="3d/latent",
category="model/latent/triposplat",
description="Patch the TripoSplat model for the standard Ksampler node to show a live decoded "
"gaussian splat preview at each step.",
inputs=[

11
nodes.py
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@ -349,7 +349,7 @@ class VAEDecodeTiled:
RETURN_TYPES = ("IMAGE",)
FUNCTION = "decode"
CATEGORY = "experimental"
CATEGORY = "model/latent"
def decode(self, vae, samples, tile_size, overlap=64, temporal_size=64, temporal_overlap=8):
if tile_size < overlap * 4:
@ -396,7 +396,7 @@ class VAEEncodeTiled:
RETURN_TYPES = ("LATENT",)
FUNCTION = "encode"
CATEGORY = "experimental"
CATEGORY = "model/latent"
def encode(self, vae, pixels, tile_size, overlap, temporal_size=64, temporal_overlap=8):
t = vae.encode_tiled(pixels, tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)
@ -514,7 +514,7 @@ class SaveLatent:
OUTPUT_NODE = True
CATEGORY = "experimental"
CATEGORY = "model/latent"
def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
@ -559,7 +559,7 @@ class LoadLatent:
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")]
return {"required": {"latent": [sorted(files), ]}, }
CATEGORY = "experimental"
CATEGORY = "model/latent"
RETURN_TYPES = ("LATENT", )
FUNCTION = "load"
@ -2155,6 +2155,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"GLIGENTextBoxApply": "Apply GLIGEN Text Box",
"ConditioningZeroOut": "Conditioning Zero Out",
# Latent
"LoadLatent": "Load Latent",
"SaveLatent": "Save Latent",
"VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
"SetLatentNoiseMask": "Set Latent Noise Mask",
"VAEDecode": "VAE Decode",
@ -2189,7 +2191,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"ImageSharpen": "Sharpen Image",
"ImageScaleToTotalPixels": "Scale Image to Total Pixels",
"GetImageSize": "Get Image Size",
# experimental
"VAEDecodeTiled": "VAE Decode (Tiled)",
"VAEEncodeTiled": "VAE Encode (Tiled)",
}

2
requirements.txt
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@ -1,5 +1,5 @@
comfyui-frontend-package==1.45.20
comfyui-workflow-templates==0.10.7
comfyui-workflow-templates==0.11.1
comfyui-embedded-docs==0.5.6
torch
torchsde