mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-06-08 16:27:23 +08:00
Compare commits
1 Commits
feat/load-
...
cloud-open
| Author | SHA1 | Date | |
|---|---|---|---|
| d55ae62807 |
@ -1,27 +1,28 @@
|
||||
As of the time of writing this you need a recent driver. Updating to the latest driver is recommended.
|
||||
|
||||
HOW TO RUN:
|
||||
|
||||
If you have a AMD gpu:
|
||||
|
||||
run_amd_gpu.bat
|
||||
|
||||
If you have memory issues you can try enabling the new dynamic memory management by running comfyui with:
|
||||
|
||||
run_amd_gpu_enable_dynamic_vram.bat
|
||||
|
||||
IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
|
||||
|
||||
You can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors
|
||||
|
||||
|
||||
RECOMMENDED WAY TO UPDATE:
|
||||
To update the ComfyUI code: update\update_comfyui.bat
|
||||
|
||||
|
||||
TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
|
||||
In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
|
||||
Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
|
||||
|
||||
|
||||
|
||||
As of the time of writing this you need this driver for best results:
|
||||
https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-7-1-1.html
|
||||
|
||||
HOW TO RUN:
|
||||
|
||||
If you have a AMD gpu:
|
||||
|
||||
run_amd_gpu.bat
|
||||
|
||||
If you have memory issues you can try disabling the smart memory management by running comfyui with:
|
||||
|
||||
run_amd_gpu_disable_smart_memory.bat
|
||||
|
||||
IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
|
||||
|
||||
You can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors
|
||||
|
||||
|
||||
RECOMMENDED WAY TO UPDATE:
|
||||
To update the ComfyUI code: update\update_comfyui.bat
|
||||
|
||||
|
||||
TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
|
||||
In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
|
||||
Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
|
||||
|
||||
|
||||
|
||||
|
||||
@ -651,7 +651,8 @@ def ensure_pin_budget(size, evict_active=False):
|
||||
to_free = shortfall + PIN_PRESSURE_HYSTERESIS
|
||||
return free_pins(to_free, evict_active=evict_active) >= shortfall
|
||||
|
||||
def free_registrations(shortfall, evict_active=True):
|
||||
def ensure_pin_registerable(size, evict_active=True):
|
||||
shortfall = TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY
|
||||
if MAX_PINNED_MEMORY <= 0:
|
||||
return False
|
||||
if shortfall <= 0:
|
||||
@ -673,9 +674,6 @@ def free_registrations(shortfall, evict_active=True):
|
||||
return True
|
||||
return shortfall <= REGISTERABLE_PIN_HYSTERESIS
|
||||
|
||||
def ensure_pin_registerable(size, evict_active=True):
|
||||
return free_registrations(TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY, evict_active=evict_active)
|
||||
|
||||
class LoadedModel:
|
||||
def __init__(self, model: ModelPatcher):
|
||||
self._set_model(model)
|
||||
|
||||
@ -89,26 +89,13 @@ def pin_memory(module, subset="weights", size=None):
|
||||
not comfy.model_management.ensure_pin_registerable(registerable_size)):
|
||||
return _steal_pin(module, stack, buckets, size, priority)
|
||||
|
||||
extended = False
|
||||
try:
|
||||
hostbuf.extend(size=size, register=False)
|
||||
extended = True
|
||||
pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)[offset:offset + size]
|
||||
pin.untyped_storage()._comfy_hostbuf = hostbuf
|
||||
if torch.cuda.cudart().cudaHostRegister(pin.data_ptr(), size, 1) != 0:
|
||||
comfy.model_management.discard_cuda_async_error()
|
||||
comfy.model_management.free_registrations(size)
|
||||
if torch.cuda.cudart().cudaHostRegister(pin.data_ptr(), size, 1) != 0:
|
||||
comfy.model_management.discard_cuda_async_error()
|
||||
del pin
|
||||
hostbuf.truncate(offset, do_unregister=False)
|
||||
return _steal_pin(module, stack, buckets, size, priority)
|
||||
hostbuf.extend(size=size)
|
||||
except RuntimeError:
|
||||
if extended:
|
||||
hostbuf.truncate(offset, do_unregister=False)
|
||||
return _steal_pin(module, stack, buckets, size, priority)
|
||||
|
||||
module._pin = pin
|
||||
module._pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)[offset:offset + size]
|
||||
module._pin.untyped_storage()._comfy_hostbuf = hostbuf
|
||||
stack.append((module, offset))
|
||||
module._pin_registered = True
|
||||
module._pin_stack_index = len(stack) - 1
|
||||
|
||||
@ -755,18 +755,6 @@ class File3DKSPLAT(ComfyTypeIO):
|
||||
Type = File3D
|
||||
|
||||
|
||||
@comfytype(io_type="FILE_3D_SPLAT_ANY")
|
||||
class File3DSplatAny(ComfyTypeIO):
|
||||
"""General 3D Gaussian splat file type - accepts any supported splat container (.ply / .spz / .splat / .ksplat)."""
|
||||
Type = File3D
|
||||
|
||||
|
||||
@comfytype(io_type="FILE_3D_POINT_CLOUD_ANY")
|
||||
class File3DPointCloudAny(ComfyTypeIO):
|
||||
"""General point cloud file type - accepts any supported point cloud container (currently .ply)."""
|
||||
Type = File3D
|
||||
|
||||
|
||||
@comfytype(io_type="HOOKS")
|
||||
class Hooks(ComfyTypeIO):
|
||||
if TYPE_CHECKING:
|
||||
@ -2348,8 +2336,6 @@ __all__ = [
|
||||
"File3DSPLAT",
|
||||
"File3DSPZ",
|
||||
"File3DKSPLAT",
|
||||
"File3DSplatAny",
|
||||
"File3DPointCloudAny",
|
||||
"Hooks",
|
||||
"HookKeyframes",
|
||||
"TimestepsRange",
|
||||
|
||||
@ -108,19 +108,13 @@ class GeminiVideoMetadata(BaseModel):
|
||||
startOffset: GeminiOffset | None = Field(None)
|
||||
|
||||
|
||||
class GeminiThinkingConfig(BaseModel):
|
||||
includeThoughts: bool | None = Field(None)
|
||||
thinkingLevel: str = Field(...)
|
||||
|
||||
|
||||
class GeminiGenerationConfig(BaseModel):
|
||||
maxOutputTokens: int | None = Field(None, ge=16, le=65536)
|
||||
maxOutputTokens: int | None = Field(None, ge=16, le=8192)
|
||||
seed: int | None = Field(None)
|
||||
stopSequences: list[str] | None = Field(None)
|
||||
temperature: float | None = Field(None, ge=0.0, le=2.0)
|
||||
topK: int | None = Field(None, ge=1)
|
||||
topP: float | None = Field(None, ge=0.0, le=1.0)
|
||||
thinkingConfig: GeminiThinkingConfig | None = Field(None)
|
||||
|
||||
|
||||
class GeminiImageOutputOptions(BaseModel):
|
||||
@ -134,6 +128,11 @@ class GeminiImageConfig(BaseModel):
|
||||
imageOutputOptions: GeminiImageOutputOptions = Field(default_factory=GeminiImageOutputOptions)
|
||||
|
||||
|
||||
class GeminiThinkingConfig(BaseModel):
|
||||
includeThoughts: bool | None = Field(None)
|
||||
thinkingLevel: str = Field(...)
|
||||
|
||||
|
||||
class GeminiImageGenerationConfig(GeminiGenerationConfig):
|
||||
responseModalities: list[str] | None = Field(None)
|
||||
imageConfig: GeminiImageConfig | None = Field(None)
|
||||
|
||||
@ -8,7 +8,7 @@ import os
|
||||
from enum import Enum
|
||||
from fnmatch import fnmatch
|
||||
from io import BytesIO
|
||||
from typing import Any, Literal
|
||||
from typing import Literal
|
||||
|
||||
import torch
|
||||
from typing_extensions import override
|
||||
@ -19,7 +19,6 @@ from comfy_api_nodes.apis.gemini import (
|
||||
GeminiContent,
|
||||
GeminiFileData,
|
||||
GeminiGenerateContentRequest,
|
||||
GeminiGenerationConfig,
|
||||
GeminiGenerateContentResponse,
|
||||
GeminiImageConfig,
|
||||
GeminiImageGenerateContentRequest,
|
||||
@ -41,18 +40,13 @@ from comfy_api_nodes.util import (
|
||||
get_number_of_images,
|
||||
sync_op,
|
||||
tensor_to_base64_string,
|
||||
upload_audio_to_comfyapi,
|
||||
upload_image_to_comfyapi,
|
||||
upload_images_to_comfyapi,
|
||||
upload_video_to_comfyapi,
|
||||
validate_string,
|
||||
video_to_base64_string,
|
||||
)
|
||||
|
||||
GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini"
|
||||
GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024 # 20 MB
|
||||
GEMINI_URL_INPUT_BUDGET = 10
|
||||
GEMINI_MAX_INLINE_BYTES = 18 * 1024 * 1024
|
||||
GEMINI_IMAGE_SYS_PROMPT = (
|
||||
"You are an expert image-generation engine. You must ALWAYS produce an image.\n"
|
||||
"Interpret all user input—regardless of "
|
||||
@ -291,140 +285,6 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
|
||||
return final_price / 1_000_000.0
|
||||
|
||||
|
||||
def create_video_parts(video_input: Input.Video) -> list[GeminiPart]:
|
||||
"""Convert a single video input to Gemini API compatible parts (inline MP4/H.264)."""
|
||||
base_64_string = video_to_base64_string(
|
||||
video_input, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
|
||||
)
|
||||
return [
|
||||
GeminiPart(
|
||||
inlineData=GeminiInlineData(
|
||||
mimeType=GeminiMimeType.video_mp4,
|
||||
data=base_64_string,
|
||||
)
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
def create_audio_parts(audio_input: Input.Audio) -> list[GeminiPart]:
|
||||
"""Convert an audio input to Gemini API compatible parts (one inline MP3 part per batch item)."""
|
||||
audio_parts: list[GeminiPart] = []
|
||||
for batch_index in range(audio_input["waveform"].shape[0]):
|
||||
# Recreate an IO.AUDIO object for the given batch dimension index
|
||||
audio_at_index = Input.Audio(
|
||||
waveform=audio_input["waveform"][batch_index].unsqueeze(0),
|
||||
sample_rate=audio_input["sample_rate"],
|
||||
)
|
||||
# Convert to MP3 format for compatibility with Gemini API
|
||||
audio_bytes = audio_to_base64_string(
|
||||
audio_at_index,
|
||||
container_format="mp3",
|
||||
codec_name="libmp3lame",
|
||||
)
|
||||
audio_parts.append(
|
||||
GeminiPart(
|
||||
inlineData=GeminiInlineData(
|
||||
mimeType=GeminiMimeType.audio_mp3,
|
||||
data=audio_bytes,
|
||||
)
|
||||
)
|
||||
)
|
||||
return audio_parts
|
||||
|
||||
|
||||
def _flatten_images(images: list[Input.Image]) -> list[torch.Tensor]:
|
||||
"""Expand any batched image tensors into individual (H, W, C) frames, preserving order."""
|
||||
frames: list[torch.Tensor] = []
|
||||
for img in images:
|
||||
if len(img.shape) == 4:
|
||||
frames.extend(img[i] for i in range(img.shape[0]))
|
||||
else:
|
||||
frames.append(img)
|
||||
return frames
|
||||
|
||||
|
||||
def _flatten_audio(audios: list[Input.Audio]) -> list[Input.Audio]:
|
||||
"""Expand any batched audio inputs into individual single-clip audio inputs, preserving order."""
|
||||
clips: list[Input.Audio] = []
|
||||
for audio in audios:
|
||||
waveform = audio["waveform"]
|
||||
for i in range(waveform.shape[0]):
|
||||
clips.append(Input.Audio(waveform=waveform[i].unsqueeze(0), sample_rate=audio["sample_rate"]))
|
||||
return clips
|
||||
|
||||
|
||||
async def _media_url_part(cls: type[IO.ComfyNode], kind: str, payload: Any) -> GeminiPart:
|
||||
"""Upload a single media unit to ComfyAPI storage and return a fileData (URL) part."""
|
||||
if kind == "image":
|
||||
url = await upload_image_to_comfyapi(cls, payload, mime_type="image/png", wait_label="Uploading image")
|
||||
return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.image_png, fileUri=url))
|
||||
if kind == "audio":
|
||||
url = await upload_audio_to_comfyapi(
|
||||
cls, payload, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mp3"
|
||||
)
|
||||
return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.audio_mp3, fileUri=url))
|
||||
url = await upload_video_to_comfyapi(cls, payload, wait_label="Uploading video")
|
||||
return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.video_mp4, fileUri=url))
|
||||
|
||||
|
||||
def _media_inline_part(kind: str, payload: Any) -> tuple[GeminiPart, int]:
|
||||
"""Encode a single media unit as an inline base64 part; returns (part, base64_length)."""
|
||||
if kind == "image":
|
||||
data = tensor_to_base64_string(payload, mime_type="image/webp")
|
||||
mime = GeminiMimeType.image_webp
|
||||
elif kind == "audio":
|
||||
data = audio_to_base64_string(payload, container_format="mp3", codec_name="libmp3lame")
|
||||
mime = GeminiMimeType.audio_mp3
|
||||
else:
|
||||
data = video_to_base64_string(
|
||||
payload, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
|
||||
)
|
||||
mime = GeminiMimeType.video_mp4
|
||||
return GeminiPart(inlineData=GeminiInlineData(mimeType=mime, data=data)), len(data)
|
||||
|
||||
|
||||
async def build_gemini_media_parts(
|
||||
cls: type[IO.ComfyNode],
|
||||
images: list[Input.Image],
|
||||
audios: list[Input.Audio],
|
||||
videos: list[Input.Video],
|
||||
*,
|
||||
url_budget: int = GEMINI_URL_INPUT_BUDGET,
|
||||
max_inline_bytes: int = GEMINI_MAX_INLINE_BYTES,
|
||||
) -> list[GeminiPart]:
|
||||
"""Build Gemini parts for multimodal inputs (images, audio, video).
|
||||
|
||||
fileData URLs are preferred for every media type: the upload is fetched directly by the
|
||||
model, keeping the request body tiny regardless of media size. The URL budget is shared
|
||||
across all media and assigned largest-first (video, then audio, then images), so that if it
|
||||
is ever exhausted the inline-base64 overflow is limited to the smallest items. Total inline
|
||||
payload is capped by `max_inline_bytes`.
|
||||
"""
|
||||
units: list[tuple[str, Any]] = (
|
||||
[("video", v) for v in videos]
|
||||
+ [("audio", a) for a in _flatten_audio(audios)]
|
||||
+ [("image", f) for f in _flatten_images(images)]
|
||||
)
|
||||
|
||||
parts: list[GeminiPart] = []
|
||||
url_used = 0
|
||||
inline_bytes = 0
|
||||
for kind, payload in units:
|
||||
if url_used < url_budget:
|
||||
parts.append(await _media_url_part(cls, kind, payload))
|
||||
url_used += 1
|
||||
continue
|
||||
part, nbytes = _media_inline_part(kind, payload)
|
||||
inline_bytes += nbytes
|
||||
if inline_bytes > max_inline_bytes:
|
||||
raise ValueError(
|
||||
f"Too much media to send inline (over {max_inline_bytes // (1024 * 1024)}MB after the first "
|
||||
f"{url_budget} inputs are uploaded as URLs). Reduce the number or size of attached media."
|
||||
)
|
||||
parts.append(part)
|
||||
return parts
|
||||
|
||||
|
||||
class GeminiNode(IO.ComfyNode):
|
||||
"""
|
||||
Node to generate text responses from a Gemini model.
|
||||
@ -547,9 +407,58 @@ class GeminiNode(IO.ComfyNode):
|
||||
)
|
||||
""",
|
||||
),
|
||||
is_deprecated=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def create_video_parts(cls, video_input: Input.Video) -> list[GeminiPart]:
|
||||
"""Convert video input to Gemini API compatible parts."""
|
||||
|
||||
base_64_string = video_to_base64_string(
|
||||
video_input, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264
|
||||
)
|
||||
return [
|
||||
GeminiPart(
|
||||
inlineData=GeminiInlineData(
|
||||
mimeType=GeminiMimeType.video_mp4,
|
||||
data=base_64_string,
|
||||
)
|
||||
)
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def create_audio_parts(cls, audio_input: Input.Audio) -> list[GeminiPart]:
|
||||
"""
|
||||
Convert audio input to Gemini API compatible parts.
|
||||
|
||||
Args:
|
||||
audio_input: Audio input from ComfyUI, containing waveform tensor and sample rate.
|
||||
|
||||
Returns:
|
||||
List of GeminiPart objects containing the encoded audio.
|
||||
"""
|
||||
audio_parts: list[GeminiPart] = []
|
||||
for batch_index in range(audio_input["waveform"].shape[0]):
|
||||
# Recreate an IO.AUDIO object for the given batch dimension index
|
||||
audio_at_index = Input.Audio(
|
||||
waveform=audio_input["waveform"][batch_index].unsqueeze(0),
|
||||
sample_rate=audio_input["sample_rate"],
|
||||
)
|
||||
# Convert to MP3 format for compatibility with Gemini API
|
||||
audio_bytes = audio_to_base64_string(
|
||||
audio_at_index,
|
||||
container_format="mp3",
|
||||
codec_name="libmp3lame",
|
||||
)
|
||||
audio_parts.append(
|
||||
GeminiPart(
|
||||
inlineData=GeminiInlineData(
|
||||
mimeType=GeminiMimeType.audio_mp3,
|
||||
data=audio_bytes,
|
||||
)
|
||||
)
|
||||
)
|
||||
return audio_parts
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
@ -573,9 +482,9 @@ class GeminiNode(IO.ComfyNode):
|
||||
if images is not None:
|
||||
parts.extend(await create_image_parts(cls, images))
|
||||
if audio is not None:
|
||||
parts.extend(create_audio_parts(audio))
|
||||
parts.extend(cls.create_audio_parts(audio))
|
||||
if video is not None:
|
||||
parts.extend(create_video_parts(video))
|
||||
parts.extend(cls.create_video_parts(video))
|
||||
if files is not None:
|
||||
parts.extend(files)
|
||||
|
||||
@ -603,210 +512,6 @@ class GeminiNode(IO.ComfyNode):
|
||||
return IO.NodeOutput(output_text or "Empty response from Gemini model...")
|
||||
|
||||
|
||||
GEMINI_V2_MODELS: dict[str, str] = {
|
||||
"Gemini 3.1 Pro": "gemini-3.1-pro-preview",
|
||||
"Gemini 3.1 Flash-Lite": "gemini-3.1-flash-lite-preview",
|
||||
}
|
||||
|
||||
|
||||
def _gemini_text_model_inputs(thinking_default: str) -> list[Input]:
|
||||
"""Per-model inputs revealed by the model DynamicCombo (shared media + sampling controls)."""
|
||||
return [
|
||||
IO.Autogrow.Input(
|
||||
"images",
|
||||
template=IO.Autogrow.TemplateNames(
|
||||
IO.Image.Input("image"),
|
||||
names=[f"image_{i}" for i in range(1, 17)],
|
||||
min=0,
|
||||
),
|
||||
tooltip="Optional image(s) to use as context for the model. Up to 16 images.",
|
||||
),
|
||||
IO.Autogrow.Input(
|
||||
"audio",
|
||||
template=IO.Autogrow.TemplateNames(
|
||||
IO.Audio.Input("audio"),
|
||||
names=["audio_1"],
|
||||
min=0,
|
||||
),
|
||||
tooltip="Optional audio clip to use as context for the model.",
|
||||
),
|
||||
IO.Autogrow.Input(
|
||||
"video",
|
||||
template=IO.Autogrow.TemplateNames(
|
||||
IO.Video.Input("video"),
|
||||
names=["video_1"],
|
||||
min=0,
|
||||
),
|
||||
tooltip="Optional video clip to use as context for the model.",
|
||||
),
|
||||
IO.Custom("GEMINI_INPUT_FILES").Input(
|
||||
"files",
|
||||
optional=True,
|
||||
tooltip="Optional file(s) to use as context for the model. "
|
||||
"Accepts inputs from the Gemini Input Files node.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"thinking_level",
|
||||
options=["LOW", "HIGH"],
|
||||
default=thinking_default,
|
||||
tooltip="How hard the model reasons internally before answering. "
|
||||
"HIGH improves quality on difficult tasks but costs more (thinking) tokens and is slower.",
|
||||
),
|
||||
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 creative.",
|
||||
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,
|
||||
),
|
||||
IO.Int.Input(
|
||||
"max_output_tokens",
|
||||
default=32768,
|
||||
min=16,
|
||||
max=65536,
|
||||
tooltip="Maximum tokens to generate, including the model's internal thinking. "
|
||||
"With thinking_level HIGH, a low value can leave no room for the answer; raise this if "
|
||||
"responses come back empty or truncated. The model stops early when finished, so a higher "
|
||||
"cap costs nothing extra for short replies.",
|
||||
advanced=True,
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
class GeminiNodeV2(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="GeminiNodeV2",
|
||||
display_name="Google Gemini",
|
||||
category="partner/text/Gemini",
|
||||
essentials_category="Text Generation",
|
||||
description="Generate text responses with Google's Gemini models. Provide a text prompt and, "
|
||||
"optionally, one or more images, audio clips, videos, or files as multimodal context.",
|
||||
inputs=[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="Text input to the model. Include detailed instructions, questions, or context.",
|
||||
),
|
||||
IO.DynamicCombo.Input(
|
||||
"model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option("Gemini 3.1 Pro", _gemini_text_model_inputs("HIGH")),
|
||||
IO.DynamicCombo.Option("Gemini 3.1 Flash-Lite", _gemini_text_model_inputs("LOW")),
|
||||
],
|
||||
tooltip="The Gemini model used to generate the response.",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=42,
|
||||
min=0,
|
||||
max=2147483647,
|
||||
control_after_generate=True,
|
||||
tooltip="Seed for sampling. Set to 0 for a random seed. Deterministic output isn't guaranteed.",
|
||||
),
|
||||
IO.String.Input(
|
||||
"system_prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
optional=True,
|
||||
advanced=True,
|
||||
tooltip="Foundational instructions that dictate the model's behavior.",
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
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(
|
||||
depends_on=IO.PriceBadgeDepends(widgets=["model"]),
|
||||
expr="""
|
||||
(
|
||||
$m := widgets.model;
|
||||
$contains($m, "lite") ? {
|
||||
"type": "list_usd",
|
||||
"usd": [0.00025, 0.0015],
|
||||
"format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
|
||||
} : {
|
||||
"type": "list_usd",
|
||||
"usd": [0.002, 0.012],
|
||||
"format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
|
||||
}
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
prompt: str,
|
||||
model: dict,
|
||||
seed: int,
|
||||
system_prompt: str = "",
|
||||
) -> IO.NodeOutput:
|
||||
validate_string(prompt, strip_whitespace=True, min_length=1)
|
||||
model_id = GEMINI_V2_MODELS[model["model"]]
|
||||
|
||||
parts: list[GeminiPart] = [GeminiPart(text=prompt)]
|
||||
images = [t for t in (model.get("images") or {}).values() if t is not None]
|
||||
audios = [a for a in (model.get("audio") or {}).values() if a is not None]
|
||||
videos = [v for v in (model.get("video") or {}).values() if v is not None]
|
||||
if images or audios or videos:
|
||||
parts.extend(await build_gemini_media_parts(cls, images, audios, videos))
|
||||
files = model.get("files")
|
||||
if files is not None:
|
||||
parts.extend(files)
|
||||
|
||||
gemini_system_prompt = None
|
||||
if system_prompt:
|
||||
gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
|
||||
|
||||
response = await sync_op(
|
||||
cls,
|
||||
endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model_id}", method="POST"),
|
||||
data=GeminiGenerateContentRequest(
|
||||
contents=[
|
||||
GeminiContent(
|
||||
role=GeminiRole.user,
|
||||
parts=parts,
|
||||
)
|
||||
],
|
||||
generationConfig=GeminiGenerationConfig(
|
||||
temperature=model["temperature"],
|
||||
topP=model["top_p"],
|
||||
maxOutputTokens=model["max_output_tokens"],
|
||||
seed=seed if seed > 0 else None,
|
||||
thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]),
|
||||
),
|
||||
systemInstruction=gemini_system_prompt,
|
||||
),
|
||||
response_model=GeminiGenerateContentResponse,
|
||||
price_extractor=calculate_tokens_price,
|
||||
)
|
||||
|
||||
output_text = get_text_from_response(response)
|
||||
return IO.NodeOutput(output_text or "Empty response from Gemini model...")
|
||||
|
||||
|
||||
class GeminiInputFiles(IO.ComfyNode):
|
||||
"""
|
||||
Loads and formats input files for use with the Gemini API.
|
||||
@ -1424,26 +1129,6 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
|
||||
tooltip="Foundational instructions that dictate an AI's behavior.",
|
||||
advanced=True,
|
||||
),
|
||||
IO.Float.Input(
|
||||
"temperature",
|
||||
default=1.0,
|
||||
min=0.0,
|
||||
max=2.0,
|
||||
step=0.01,
|
||||
optional=True,
|
||||
tooltip="Controls randomness in generation. Lower is more focused/deterministic.",
|
||||
advanced=True,
|
||||
),
|
||||
IO.Float.Input(
|
||||
"top_p",
|
||||
default=0.95,
|
||||
min=0.0,
|
||||
max=1.0,
|
||||
step=0.01,
|
||||
optional=True,
|
||||
tooltip="Nucleus sampling threshold. Lower is more focused, higher more diverse.",
|
||||
advanced=True,
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Image.Output(),
|
||||
@ -1480,8 +1165,6 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
|
||||
seed: int,
|
||||
response_modalities: str,
|
||||
system_prompt: str = "",
|
||||
temperature: float = 1.0,
|
||||
top_p: float = 0.95,
|
||||
) -> IO.NodeOutput:
|
||||
validate_string(prompt, strip_whitespace=True, min_length=1)
|
||||
model_choice = model["model"]
|
||||
@ -1521,8 +1204,6 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
|
||||
responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
|
||||
imageConfig=image_config,
|
||||
thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]),
|
||||
temperature=temperature,
|
||||
topP=top_p,
|
||||
),
|
||||
systemInstruction=gemini_system_prompt,
|
||||
),
|
||||
@ -1541,7 +1222,6 @@ class GeminiExtension(ComfyExtension):
|
||||
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
|
||||
return [
|
||||
GeminiNode,
|
||||
GeminiNodeV2,
|
||||
GeminiImage,
|
||||
GeminiImage2,
|
||||
GeminiNanoBanana2,
|
||||
|
||||
@ -933,10 +933,9 @@ class Guider_DualModel(comfy.samplers.CFGGuider):
|
||||
|
||||
def predict_noise(self, x, timestep, model_options={}, seed=None):
|
||||
positive = self.conds.get("positive", None)
|
||||
if self.uncond_inner is None: # cfg == 1 or no negative -> single model, cond only
|
||||
return comfy.samplers.calc_cond_batch(self.inner_model, [positive], x, timestep, model_options)[0]
|
||||
cond = comfy.samplers.calc_cond_batch(self.inner_model, [positive], x, timestep, model_options)[0]
|
||||
# uncond model not loaded (base cfg==1/no negative), or cfg driven to 1.0 this step -> single model, cond only
|
||||
if self.uncond_inner is None or (math.isclose(self.cfg, 1.0) and not model_options.get("disable_cfg1_optimization", False)):
|
||||
return cond
|
||||
|
||||
uncond_model_options = model_options
|
||||
if "multigpu_clones" in model_options: # TODO: support multigpu instead of just running uncond on a single GPU
|
||||
@ -1141,7 +1140,7 @@ class CFGOverride(io.ComfyNode):
|
||||
return io.Schema(
|
||||
node_id="CFGOverride",
|
||||
display_name="CFG Override",
|
||||
description="Override cfg to a fixed value over a [start, end] percent (sigma) range. "
|
||||
description="Override cfg to a fixed value over a [start, end] percent slice of the steps. "
|
||||
"With multiple overrides, the one nearest the sampler wins on overlap.",
|
||||
category="sampling/custom_sampling",
|
||||
inputs=[
|
||||
|
||||
@ -488,7 +488,7 @@ class SplatToFile3D(IO.ComfyNode):
|
||||
"spz: Niantic gzip-compressed (~10x smaller), base color only "
|
||||
),
|
||||
],
|
||||
outputs=[IO.File3DSplatAny.Output(display_name="model_3d")],
|
||||
outputs=[IO.File3DAny.Output(display_name="model_3d")],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
@ -516,7 +516,7 @@ class File3DToSplat(IO.ComfyNode):
|
||||
inputs=[
|
||||
IO.MultiType.Input(
|
||||
IO.File3DAny.Input("model_3d"),
|
||||
types=[IO.File3DSplatAny, IO.File3DPLY, IO.File3DSPLAT, IO.File3DKSPLAT, IO.File3DSPZ],
|
||||
types=[IO.File3DPLY, IO.File3DSPLAT, IO.File3DKSPLAT, IO.File3DSPZ],
|
||||
tooltip="A gaussian splat 3D file",
|
||||
),
|
||||
],
|
||||
|
||||
@ -51,14 +51,6 @@ class Load3D(IO.ComfyNode):
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def validate_inputs(cls, model_file, **kwargs) -> bool | str:
|
||||
if not model_file or model_file == "none":
|
||||
return True
|
||||
if not folder_paths.exists_annotated_filepath(model_file):
|
||||
return f"Invalid 3D model file: {model_file}"
|
||||
return True
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model_file, image, **kwargs) -> IO.NodeOutput:
|
||||
image_path = folder_paths.get_annotated_filepath(image['image'])
|
||||
@ -144,7 +136,7 @@ class Preview3DAdvanced(IO.ComfyNode):
|
||||
is_output_node=True,
|
||||
inputs=[
|
||||
IO.MultiType.Input(
|
||||
"model_3d",
|
||||
"model_file",
|
||||
types=[
|
||||
IO.File3DGLB,
|
||||
IO.File3DGLTF,
|
||||
@ -156,236 +148,47 @@ class Preview3DAdvanced(IO.ComfyNode):
|
||||
],
|
||||
tooltip="3D model file from an upstream 3D node.",
|
||||
),
|
||||
IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
|
||||
IO.Load3D.Input("viewport_state"),
|
||||
IO.Load3D.Input("image"),
|
||||
IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
|
||||
IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
|
||||
IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
|
||||
IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
|
||||
],
|
||||
outputs=[
|
||||
IO.File3DAny.Output(display_name="model_3d"),
|
||||
IO.Load3DModelInfo.Output(display_name="model_3d_info"),
|
||||
IO.File3DAny.Output(display_name="model_file"),
|
||||
IO.Load3DCamera.Output(display_name="camera_info"),
|
||||
IO.Load3DModelInfo.Output(display_name="model_3d_info"),
|
||||
IO.Int.Output(display_name="width"),
|
||||
IO.Int.Output(display_name="height"),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
|
||||
filename = f"preview3d_advanced_{uuid.uuid4().hex}.{model_3d.format}"
|
||||
model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
|
||||
def execute(cls, model_file: Types.File3D, image, width: int, height: int, **kwargs) -> IO.NodeOutput:
|
||||
filename = f"preview3d_advanced_{uuid.uuid4().hex}.{model_file.format}"
|
||||
model_file.save_to(os.path.join(folder_paths.get_output_directory(), filename))
|
||||
|
||||
camera_info_input = kwargs.get("camera_info", None)
|
||||
camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info']
|
||||
camera_info = camera_info_input if camera_info_input is not None else image['camera_info']
|
||||
model_3d_info_input = kwargs.get("model_3d_info", None)
|
||||
model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
|
||||
model_3d_info = model_3d_info_input if model_3d_info_input is not None else image.get('model_3d_info', [])
|
||||
return IO.NodeOutput(
|
||||
model_3d,
|
||||
model_3d_info,
|
||||
model_file,
|
||||
camera_info,
|
||||
model_3d_info,
|
||||
width,
|
||||
height,
|
||||
ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info),
|
||||
)
|
||||
|
||||
|
||||
class PreviewGaussianSplat(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="PreviewGaussianSplat",
|
||||
display_name="Preview Splat",
|
||||
category="3d",
|
||||
is_experimental=True,
|
||||
is_output_node=True,
|
||||
search_aliases=[
|
||||
"view splat",
|
||||
"view gaussian",
|
||||
"view gaussian splat",
|
||||
"preview gaussian",
|
||||
"preview gaussian splat",
|
||||
"view 3dgs",
|
||||
"preview 3dgs",
|
||||
"preview ply",
|
||||
"preview spz",
|
||||
"preview splat",
|
||||
"preview ksplat",
|
||||
],
|
||||
inputs=[
|
||||
IO.MultiType.Input(
|
||||
"model_3d",
|
||||
types=[
|
||||
IO.File3DSplatAny,
|
||||
IO.File3DPLY,
|
||||
IO.File3DSPLAT,
|
||||
IO.File3DSPZ,
|
||||
IO.File3DKSPLAT,
|
||||
],
|
||||
tooltip="A gaussian splat 3D file.",
|
||||
),
|
||||
IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
|
||||
IO.Load3D.Input("viewport_state"),
|
||||
IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
|
||||
IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
|
||||
IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
|
||||
],
|
||||
outputs=[
|
||||
IO.File3DSplatAny.Output(display_name="model_3d"),
|
||||
IO.Load3DModelInfo.Output(display_name="model_3d_info"),
|
||||
IO.Load3DCamera.Output(display_name="camera_info"),
|
||||
IO.Int.Output(display_name="width"),
|
||||
IO.Int.Output(display_name="height"),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
|
||||
filename = f"preview_splat_{uuid.uuid4().hex}.{model_3d.format}"
|
||||
model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
|
||||
|
||||
camera_info_input = kwargs.get("camera_info", None)
|
||||
camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info']
|
||||
model_3d_info_input = kwargs.get("model_3d_info", None)
|
||||
model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
|
||||
return IO.NodeOutput(
|
||||
model_3d,
|
||||
model_3d_info,
|
||||
camera_info,
|
||||
width,
|
||||
height,
|
||||
ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info),
|
||||
)
|
||||
|
||||
|
||||
class PreviewPointCloud(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="PreviewPointCloud",
|
||||
display_name="Preview Point Cloud",
|
||||
category="3d",
|
||||
is_experimental=True,
|
||||
is_output_node=True,
|
||||
search_aliases=[
|
||||
"view point cloud",
|
||||
"view pointcloud",
|
||||
"preview point cloud",
|
||||
"preview pointcloud",
|
||||
"preview ply",
|
||||
],
|
||||
inputs=[
|
||||
IO.MultiType.Input(
|
||||
"model_3d",
|
||||
types=[
|
||||
IO.File3DPointCloudAny,
|
||||
IO.File3DPLY,
|
||||
],
|
||||
tooltip="Point cloud file (.ply)",
|
||||
),
|
||||
IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
|
||||
IO.Load3D.Input("viewport_state"),
|
||||
IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
|
||||
IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
|
||||
IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
|
||||
],
|
||||
outputs=[
|
||||
IO.File3DPointCloudAny.Output(display_name="model_3d"),
|
||||
IO.Load3DModelInfo.Output(display_name="model_3d_info"),
|
||||
IO.Load3DCamera.Output(display_name="camera_info"),
|
||||
IO.Int.Output(display_name="width"),
|
||||
IO.Int.Output(display_name="height"),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
|
||||
filename = f"preview_pointcloud_{uuid.uuid4().hex}.{model_3d.format}"
|
||||
model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
|
||||
|
||||
camera_info_input = kwargs.get("camera_info", None)
|
||||
camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info']
|
||||
model_3d_info_input = kwargs.get("model_3d_info", None)
|
||||
model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
|
||||
return IO.NodeOutput(
|
||||
model_3d,
|
||||
model_3d_info,
|
||||
camera_info,
|
||||
width,
|
||||
height,
|
||||
ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info),
|
||||
)
|
||||
|
||||
|
||||
MESH_EXTENSIONS = {'.gltf', '.glb', '.obj', '.fbx', '.stl'}
|
||||
|
||||
|
||||
class Load3DAdvanced(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
input_dir = folder_paths.get_input_directory()
|
||||
os.makedirs(input_dir, exist_ok=True)
|
||||
|
||||
files = [
|
||||
f for f in os.listdir(input_dir)
|
||||
if os.path.isfile(os.path.join(input_dir, f))
|
||||
and os.path.splitext(f)[1].lower() in MESH_EXTENSIONS
|
||||
]
|
||||
return IO.Schema(
|
||||
node_id="Load3DAdvanced",
|
||||
display_name="Load 3D (Advanced)",
|
||||
category="3d",
|
||||
search_aliases=[
|
||||
"load mesh",
|
||||
"load gltf",
|
||||
"load glb",
|
||||
"load obj",
|
||||
"load fbx",
|
||||
"load stl",
|
||||
],
|
||||
is_experimental=True,
|
||||
inputs=[
|
||||
IO.Combo.Input("model_file", options=["none"] + sorted(files), upload=IO.UploadType.model),
|
||||
IO.Load3D.Input("viewport_state"),
|
||||
IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
|
||||
IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
|
||||
],
|
||||
outputs=[
|
||||
IO.File3DAny.Output(display_name="model_3d"),
|
||||
IO.Load3DModelInfo.Output(display_name="model_3d_info"),
|
||||
IO.Load3DCamera.Output(display_name="camera_info"),
|
||||
IO.Int.Output(display_name="width"),
|
||||
IO.Int.Output(display_name="height"),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def validate_inputs(cls, model_file, **kwargs) -> bool | str:
|
||||
if not model_file or model_file == "none":
|
||||
return True
|
||||
if not folder_paths.exists_annotated_filepath(model_file):
|
||||
return f"Invalid 3D model file: {model_file}"
|
||||
return True
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model_file, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput:
|
||||
file_3d = None
|
||||
if model_file and model_file != "none":
|
||||
file_3d = Types.File3D(folder_paths.get_annotated_filepath(model_file))
|
||||
model_3d_info = viewport_state.get('model_3d_info', [])
|
||||
return IO.NodeOutput(file_3d, model_3d_info, viewport_state['camera_info'], width, height)
|
||||
|
||||
|
||||
class Load3DExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
|
||||
return [
|
||||
Load3D,
|
||||
Load3DAdvanced,
|
||||
Preview3D,
|
||||
Preview3DAdvanced,
|
||||
PreviewGaussianSplat,
|
||||
PreviewPointCloud,
|
||||
]
|
||||
|
||||
|
||||
|
||||
@ -337,12 +337,6 @@ class SaveGLB(IO.ComfyNode):
|
||||
IO.File3DFBX,
|
||||
IO.File3DSTL,
|
||||
IO.File3DUSDZ,
|
||||
IO.File3DPLY,
|
||||
IO.File3DSPLAT,
|
||||
IO.File3DSPZ,
|
||||
IO.File3DKSPLAT,
|
||||
IO.File3DSplatAny,
|
||||
IO.File3DPointCloudAny,
|
||||
IO.File3DAny,
|
||||
],
|
||||
tooltip="Mesh or 3D file to save",
|
||||
|
||||
160
openapi.yaml
160
openapi.yaml
@ -3,11 +3,6 @@ components:
|
||||
Asset:
|
||||
description: Represents a user-owned asset (image, video, or other generated output).
|
||||
properties:
|
||||
asset_hash:
|
||||
deprecated: true
|
||||
description: 'Deprecated: use hash instead. Blake3 hash of the asset content.'
|
||||
pattern: ^blake3:[a-f0-9]{64}$
|
||||
type: string
|
||||
created_at:
|
||||
description: Timestamp when the asset was created
|
||||
format: date-time
|
||||
@ -17,7 +12,7 @@ components:
|
||||
nullable: true
|
||||
type: string
|
||||
hash:
|
||||
description: Blake3 hash of the asset content. Preferred over asset_hash.
|
||||
description: Blake3 hash of the asset content.
|
||||
pattern: ^blake3:[a-f0-9]{64}$
|
||||
type: string
|
||||
id:
|
||||
@ -139,17 +134,12 @@ components:
|
||||
AssetUpdated:
|
||||
description: Response returned when an existing asset is successfully updated.
|
||||
properties:
|
||||
asset_hash:
|
||||
deprecated: true
|
||||
description: 'Deprecated: use hash instead. Blake3 hash of the asset content.'
|
||||
pattern: ^blake3:[a-f0-9]{64}$
|
||||
type: string
|
||||
display_name:
|
||||
description: Display name of the asset. Mirrors name for backwards compatibility.
|
||||
nullable: true
|
||||
type: string
|
||||
hash:
|
||||
description: Blake3 hash of the asset content. Preferred over asset_hash.
|
||||
description: Blake3 hash of the asset content.
|
||||
pattern: ^blake3:[a-f0-9]{64}$
|
||||
type: string
|
||||
id:
|
||||
@ -1518,17 +1508,11 @@ paths:
|
||||
schema:
|
||||
default: true
|
||||
type: boolean
|
||||
- description: Filter assets by exact content hash. Preferred over asset_hash.
|
||||
- description: Filter assets by exact content hash.
|
||||
in: query
|
||||
name: hash
|
||||
schema:
|
||||
type: string
|
||||
- deprecated: true
|
||||
description: 'Deprecated: use hash instead. Filter assets by exact content hash.'
|
||||
in: query
|
||||
name: asset_hash
|
||||
schema:
|
||||
type: string
|
||||
- description: |
|
||||
Opaque cursor for keyset pagination. Pass the `next_cursor` value
|
||||
from the previous response to fetch the next page. When provided,
|
||||
@ -1571,42 +1555,12 @@ paths:
|
||||
- file
|
||||
post:
|
||||
description: |
|
||||
Uploads a new asset to the system with associated metadata.
|
||||
Supports two upload methods:
|
||||
1. Direct file upload (multipart/form-data)
|
||||
2. URL-based upload (application/json with source: "url")
|
||||
Creates a new asset from a direct file upload (multipart/form-data) with associated metadata.
|
||||
|
||||
If an asset with the same hash already exists, returns the existing asset.
|
||||
operationId: uploadAsset
|
||||
operationId: createAsset
|
||||
requestBody:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
properties:
|
||||
name:
|
||||
description: Display name for the asset (used to determine file extension)
|
||||
type: string
|
||||
preview_id:
|
||||
description: Optional preview asset ID
|
||||
format: uuid
|
||||
type: string
|
||||
tags:
|
||||
description: Freeform tags for the asset. Common types include "models", "input", "output", and "temp", but any tag can be used in any order.
|
||||
items:
|
||||
type: string
|
||||
type: array
|
||||
url:
|
||||
description: HTTP/HTTPS URL to download the asset from
|
||||
format: uri
|
||||
type: string
|
||||
user_metadata:
|
||||
additionalProperties: true
|
||||
description: Custom metadata to store with the asset
|
||||
type: object
|
||||
required:
|
||||
- url
|
||||
- name
|
||||
type: object
|
||||
multipart/form-data:
|
||||
schema:
|
||||
properties:
|
||||
@ -1614,6 +1568,10 @@ paths:
|
||||
description: The asset file to upload
|
||||
format: binary
|
||||
type: string
|
||||
hash:
|
||||
description: Content hash of the file.
|
||||
pattern: ^(blake3|sha256):[a-f0-9]{64}$
|
||||
type: string
|
||||
id:
|
||||
description: Optional asset ID for idempotent creation. If provided and asset exists, returns existing asset.
|
||||
format: uuid
|
||||
@ -1629,10 +1587,8 @@ paths:
|
||||
format: uuid
|
||||
type: string
|
||||
tags:
|
||||
description: Freeform tags for the asset. Common types include "models", "input", "output", and "temp", but any tag can be used in any order.
|
||||
items:
|
||||
type: string
|
||||
type: array
|
||||
description: JSON-encoded array of freeform tag strings, e.g. '["models","checkpoint"]'. Common types include "models", "input", "output", and "temp", but any tag can be used in any order.
|
||||
type: string
|
||||
user_metadata:
|
||||
description: Custom JSON metadata as a string
|
||||
type: string
|
||||
@ -1652,25 +1608,13 @@ paths:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Invalid request (bad file, invalid URL, invalid content type, etc.)
|
||||
description: Invalid request (bad file, invalid content type, etc.)
|
||||
"401":
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Unauthorized
|
||||
"403":
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Source URL requires authentication or access denied
|
||||
"404":
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Source URL not found
|
||||
"413":
|
||||
content:
|
||||
application/json:
|
||||
@ -1683,19 +1627,13 @@ paths:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Unsupported media type
|
||||
"422":
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Download failed due to network error or timeout
|
||||
"500":
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Internal server error
|
||||
summary: Upload a new asset
|
||||
summary: Create a new asset
|
||||
tags:
|
||||
- file
|
||||
/api/assets/{id}:
|
||||
@ -1783,7 +1721,7 @@ paths:
|
||||
description: |
|
||||
Updates an asset's metadata. At least one field must be provided.
|
||||
Only name, mime_type, preview_id, and user_metadata can be updated.
|
||||
For tag management, use the dedicated PUT /api/assets/{id}/tags endpoint.
|
||||
For tag management, use POST (add) and DELETE (remove) /api/assets/{id}/tags.
|
||||
operationId: updateAsset
|
||||
parameters:
|
||||
- description: Asset ID
|
||||
@ -1982,76 +1920,6 @@ paths:
|
||||
summary: Add tags to asset
|
||||
tags:
|
||||
- file
|
||||
put:
|
||||
description: Adds and removes tags from an asset in a single operation
|
||||
operationId: updateAssetTags
|
||||
parameters:
|
||||
- description: Asset ID
|
||||
in: path
|
||||
name: id
|
||||
required: true
|
||||
schema:
|
||||
format: uuid
|
||||
type: string
|
||||
requestBody:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
description: At least one of add or remove must contain items. Empty arrays are allowed when the other array has items.
|
||||
minProperties: 1
|
||||
properties:
|
||||
add:
|
||||
description: Tags to add to the asset. Can be empty if remove has items.
|
||||
items:
|
||||
type: string
|
||||
type: array
|
||||
remove:
|
||||
description: Tags to remove from the asset. Can be empty if add has items.
|
||||
items:
|
||||
type: string
|
||||
type: array
|
||||
type: object
|
||||
required: true
|
||||
responses:
|
||||
"200":
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/TagsModificationResponse'
|
||||
description: Tags updated successfully
|
||||
"400":
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Invalid request
|
||||
"401":
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Unauthorized
|
||||
"404":
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Asset not found
|
||||
"422":
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Reserved tag validation error
|
||||
"500":
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/ErrorResponse'
|
||||
description: Internal server error
|
||||
summary: Update asset tags
|
||||
tags:
|
||||
- file
|
||||
/api/assets/from-hash:
|
||||
post:
|
||||
description: |
|
||||
|
||||
@ -23,7 +23,7 @@ SQLAlchemy>=2.0.0
|
||||
filelock
|
||||
av>=16.0.0
|
||||
comfy-kitchen==0.2.10
|
||||
comfy-aimdo==0.4.9
|
||||
comfy-aimdo==0.4.8
|
||||
requests
|
||||
simpleeval>=1.0.0
|
||||
blake3
|
||||
|
||||
Reference in New Issue
Block a user