Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-20 01:31:13 +02:00
Code Issues Packages Projects Releases Wiki Activity

modernize typing

Browse Source
This commit is contained in:
Vladimir Mandic
2026-02-19 09:15:37 +01:00
parent 7aded79e8a
commit bfe014f5da
222 changed files with 1538 additions and 1444 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/api/caption.py
+67 -67
View File
@@ -25,7 +25,7 @@ Core processing logic is shared between direct and dispatch handlers via
``do_openclip``, ``do_tagger``, and ``do_vqa`` functions to avoid duplication.
"""
from typing import Optional, List, Union, Literal, Annotated
from typing import Literal, Annotated
from pydantic import BaseModel, Field # pylint: disable=no-name-in-module
from fastapi.exceptions import HTTPException
from modules import shared
@@ -49,21 +49,21 @@ class ReqCaption(BaseModel):
mode: str = Field(default="best", title="Mode", description="Caption mode. 'best': Most thorough analysis, slowest but highest quality. 'fast': Quick caption with minimal flavor terms. 'classic': Standard captioning with balanced quality and speed. 'caption': BLIP caption only, no CLIP flavor matching. 'negative': Generate terms suitable for use as a negative prompt.")
analyze: bool = Field(default=False, title="Analyze", description="If True, returns detailed image analysis breakdown (medium, artist, movement, trending, flavor) in addition to caption.")
# Advanced settings (optional per-request overrides)
max_length: Optional[int] = Field(default=None, title="Max Length", description="Maximum number of tokens in the generated caption.")
chunk_size: Optional[int] = Field(default=None, title="Chunk Size", description="Batch size for processing description candidates (flavors). Higher values speed up captioning but increase VRAM usage.")
min_flavors: Optional[int] = Field(default=None, title="Min Flavors", description="Minimum number of descriptive tags (flavors) to keep in the final prompt.")
max_flavors: Optional[int] = Field(default=None, title="Max Flavors", description="Maximum number of descriptive tags (flavors) to keep in the final prompt.")
flavor_count: Optional[int] = Field(default=None, title="Intermediates", description="Size of the intermediate candidate pool when matching image features to descriptive tags. Higher values may improve quality but are slower.")
num_beams: Optional[int] = Field(default=None, title="Num Beams", description="Number of beams for beam search during caption generation. Higher values search more possibilities but are slower.")
max_length: int | None = Field(default=None, title="Max Length", description="Maximum number of tokens in the generated caption.")
chunk_size: int | None = Field(default=None, title="Chunk Size", description="Batch size for processing description candidates (flavors). Higher values speed up captioning but increase VRAM usage.")
min_flavors: int | None = Field(default=None, title="Min Flavors", description="Minimum number of descriptive tags (flavors) to keep in the final prompt.")
max_flavors: int | None = Field(default=None, title="Max Flavors", description="Maximum number of descriptive tags (flavors) to keep in the final prompt.")
flavor_count: int | None = Field(default=None, title="Intermediates", description="Size of the intermediate candidate pool when matching image features to descriptive tags. Higher values may improve quality but are slower.")
num_beams: int | None = Field(default=None, title="Num Beams", description="Number of beams for beam search during caption generation. Higher values search more possibilities but are slower.")
class ResCaption(BaseModel):
"""Response model for image captioning results."""
caption: Optional[str] = Field(default=None, title="Caption", description="Generated caption/prompt describing the image content and style.")
medium: Optional[str] = Field(default=None, title="Medium", description="Detected artistic medium (e.g., 'oil painting', 'digital art', 'photograph'). Only returned when analyze=True.")
artist: Optional[str] = Field(default=None, title="Artist", description="Detected similar artist style (e.g., 'by greg rutkowski'). Only returned when analyze=True.")
movement: Optional[str] = Field(default=None, title="Movement", description="Detected art movement (e.g., 'art nouveau', 'impressionism'). Only returned when analyze=True.")
trending: Optional[str] = Field(default=None, title="Trending", description="Trending/platform tags (e.g., 'trending on artstation'). Only returned when analyze=True.")
flavor: Optional[str] = Field(default=None, title="Flavor", description="Additional descriptive elements (e.g., 'cinematic lighting', 'highly detailed'). Only returned when analyze=True.")
caption: str | None = Field(default=None, title="Caption", description="Generated caption/prompt describing the image content and style.")
medium: str | None = Field(default=None, title="Medium", description="Detected artistic medium (e.g., 'oil painting', 'digital art', 'photograph'). Only returned when analyze=True.")
artist: str | None = Field(default=None, title="Artist", description="Detected similar artist style (e.g., 'by greg rutkowski'). Only returned when analyze=True.")
movement: str | None = Field(default=None, title="Movement", description="Detected art movement (e.g., 'art nouveau', 'impressionism'). Only returned when analyze=True.")
trending: str | None = Field(default=None, title="Trending", description="Trending/platform tags (e.g., 'trending on artstation'). Only returned when analyze=True.")
flavor: str | None = Field(default=None, title="Flavor", description="Additional descriptive elements (e.g., 'cinematic lighting', 'highly detailed'). Only returned when analyze=True.")
class ReqVQA(BaseModel):
"""Request model for Vision-Language Model (VLM) captioning.
@@ -74,32 +74,32 @@ class ReqVQA(BaseModel):
image: str = Field(default="", title="Image", description="Image to caption. Must be a Base64 encoded string containing the image data.")
model: str = Field(default="Alibaba Qwen 2.5 VL 3B", title="Model", description="Select which model to use for Visual Language tasks. Use GET /sdapi/v1/vqa/models for full list. Models which support thinking mode are indicated in capabilities.")
question: str = Field(default="describe the image", title="Question/Task", description="Task for the model to perform. Common tasks: 'Short Caption', 'Normal Caption', 'Long Caption'. Set to 'Use Prompt' to pass custom text via the prompt field. Florence-2 tasks: 'Object Detection', 'OCR (Read Text)', 'Phrase Grounding', 'Dense Region Caption', 'Region Proposal', 'OCR with Regions'. PromptGen tasks: 'Analyze', 'Generate Tags', 'Mixed Caption'. Moondream tasks: 'Point at...', 'Detect all...', 'Detect Gaze' (Moondream 2 only). Use GET /sdapi/v1/vqa/prompts?model=<name> to list tasks available for a specific model.")
prompt: Optional[str] = Field(default=None, title="Prompt", description="Custom prompt text. Required when question is 'Use Prompt'. For 'Point at...' tasks, specify what to find (e.g., 'the red car'). For 'Detect all...' tasks, specify what to detect (e.g., 'faces').")
prompt: str | None = Field(default=None, title="Prompt", description="Custom prompt text. Required when question is 'Use Prompt'. For 'Point at...' tasks, specify what to find (e.g., 'the red car'). For 'Detect all...' tasks, specify what to detect (e.g., 'faces').")
system: str = Field(default="You are image captioning expert, creative, unbiased and uncensored.", title="System Prompt", description="System prompt controls behavior of the LLM. Processed first and persists throughout conversation. Has highest priority weighting and is always appended at the beginning of the sequence. Use for: Response formatting rules, role definition, style.")
include_annotated: bool = Field(default=False, title="Include Annotated Image", description="If True and the task produces detection results (object detection, point detection, gaze), returns annotated image with bounding boxes/points drawn. Only applicable for detection tasks on models like Florence-2 and Moondream.")
# LLM generation parameters (optional overrides)
max_tokens: Optional[int] = Field(default=None, title="Max Tokens", description="Maximum number of tokens the model can generate in its response. The model is not aware of this limit during generation; it simply sets the hard limit for the length and will forcefully cut off the response when reached.")
temperature: Optional[float] = Field(default=None, title="Temperature", description="Controls randomness in token selection. Lower values (e.g., 0.1) make outputs more focused and deterministic, always choosing high-probability tokens. Higher values (e.g., 0.9) increase creativity and diversity by allowing less probable tokens. Set to 0 for fully deterministic output.")
top_k: Optional[int] = Field(default=None, title="Top-K", description="Limits token selection to the K most likely candidates at each step. Lower values (e.g., 40) make outputs more focused and predictable, while higher values allow more diverse choices. Set to 0 to disable.")
top_p: Optional[float] = Field(default=None, title="Top-P", description="Selects tokens from the smallest set whose cumulative probability exceeds P (e.g., 0.9). Dynamically adapts the number of candidates based on model confidence; fewer options when certain, more when uncertain. Set to 1 to disable.")
num_beams: Optional[int] = Field(default=None, title="Num Beams", description="Maintains multiple candidate paths simultaneously and selects the overall best sequence. More thorough but much slower and less creative than random sampling. Generally not recommended; most modern VLMs perform better with sampling methods. Set to 1 to disable.")
do_sample: Optional[bool] = Field(default=None, title="Use Samplers", description="Enable to use sampling (randomly selecting tokens based on sampling methods like Top-K or Top-P) or disable to use greedy decoding (selecting the most probable token at each step). Enabling makes outputs more diverse and creative but less deterministic.")
thinking_mode: Optional[bool] = Field(default=None, title="Thinking Mode", description="Enables thinking/reasoning, allowing the model to take more time to generate responses. Can lead to more thoughtful and detailed answers but increases response time. Only works with models that support this feature.")
prefill: Optional[str] = Field(default=None, title="Prefill Text", description="Pre-fills the start of the model's response to guide its output format or content by forcing it to continue the prefill text. Prefill is filtered out and does not appear in the final response unless keep_prefill is True. Leave empty to let the model generate from scratch.")
keep_thinking: Optional[bool] = Field(default=None, title="Keep Thinking Trace", description="Include the model's reasoning process in the final output. Useful for understanding how the model arrived at its answer. Only works with models that support thinking mode.")
keep_prefill: Optional[bool] = Field(default=None, title="Keep Prefill", description="Include the prefill text at the beginning of the final output. If disabled, the prefill text used to guide the model is removed from the result.")
max_tokens: int | None = Field(default=None, title="Max Tokens", description="Maximum number of tokens the model can generate in its response. The model is not aware of this limit during generation; it simply sets the hard limit for the length and will forcefully cut off the response when reached.")
temperature: float | None = Field(default=None, title="Temperature", description="Controls randomness in token selection. Lower values (e.g., 0.1) make outputs more focused and deterministic, always choosing high-probability tokens. Higher values (e.g., 0.9) increase creativity and diversity by allowing less probable tokens. Set to 0 for fully deterministic output.")
top_k: int | None = Field(default=None, title="Top-K", description="Limits token selection to the K most likely candidates at each step. Lower values (e.g., 40) make outputs more focused and predictable, while higher values allow more diverse choices. Set to 0 to disable.")
top_p: float | None = Field(default=None, title="Top-P", description="Selects tokens from the smallest set whose cumulative probability exceeds P (e.g., 0.9). Dynamically adapts the number of candidates based on model confidence; fewer options when certain, more when uncertain. Set to 1 to disable.")
num_beams: int | None = Field(default=None, title="Num Beams", description="Maintains multiple candidate paths simultaneously and selects the overall best sequence. More thorough but much slower and less creative than random sampling. Generally not recommended; most modern VLMs perform better with sampling methods. Set to 1 to disable.")
do_sample: bool | None = Field(default=None, title="Use Samplers", description="Enable to use sampling (randomly selecting tokens based on sampling methods like Top-K or Top-P) or disable to use greedy decoding (selecting the most probable token at each step). Enabling makes outputs more diverse and creative but less deterministic.")
thinking_mode: bool | None = Field(default=None, title="Thinking Mode", description="Enables thinking/reasoning, allowing the model to take more time to generate responses. Can lead to more thoughtful and detailed answers but increases response time. Only works with models that support this feature.")
prefill: str | None = Field(default=None, title="Prefill Text", description="Pre-fills the start of the model's response to guide its output format or content by forcing it to continue the prefill text. Prefill is filtered out and does not appear in the final response unless keep_prefill is True. Leave empty to let the model generate from scratch.")
keep_thinking: bool | None = Field(default=None, title="Keep Thinking Trace", description="Include the model's reasoning process in the final output. Useful for understanding how the model arrived at its answer. Only works with models that support thinking mode.")
keep_prefill: bool | None = Field(default=None, title="Keep Prefill", description="Include the prefill text at the beginning of the final output. If disabled, the prefill text used to guide the model is removed from the result.")
class ResVQA(BaseModel):
"""Response model for VLM captioning results."""
answer: Optional[str] = Field(default=None, title="Answer", description="Generated caption, answer, or analysis from the VLM. Format depends on the question/task type.")
annotated_image: Optional[str] = Field(default=None, title="Annotated Image", description="Base64 encoded PNG image with detection results drawn (bounding boxes, points). Only returned when include_annotated=True and the task produces detection results.")
answer: str | None = Field(default=None, title="Answer", description="Generated caption, answer, or analysis from the VLM. Format depends on the question/task type.")
annotated_image: str | None = Field(default=None, title="Annotated Image", description="Base64 encoded PNG image with detection results drawn (bounding boxes, points). Only returned when include_annotated=True and the task produces detection results.")
class ItemVLMModel(BaseModel):
"""VLM model information."""
name: str = Field(title="Name", description="Display name of the model")
repo: str = Field(title="Repository", description="HuggingFace repository ID")
prompts: List[str] = Field(title="Prompts", description="Available prompts/tasks for this model")
capabilities: List[str] = Field(title="Capabilities", description="Model capabilities. Possible values: 'caption' (image captioning), 'vqa' (visual question answering), 'detection' (object/point detection), 'ocr' (text recognition), 'thinking' (reasoning mode support).")
prompts: list[str] = Field(title="Prompts", description="Available prompts/tasks for this model")
capabilities: list[str] = Field(title="Capabilities", description="Model capabilities. Possible values: 'caption' (image captioning), 'vqa' (visual question answering), 'detection' (object/point detection), 'ocr' (text recognition), 'thinking' (reasoning mode support).")
class ResVLMPrompts(BaseModel):
"""Available VLM prompts grouped by category.
@@ -107,12 +107,12 @@ class ResVLMPrompts(BaseModel):
When called without ``model`` parameter, returns all prompt categories.
When called with ``model``, returns only the ``available`` field with prompts for that model.
"""
common: Optional[List[str]] = Field(default=None, title="Common", description="Prompts available for all models: Use Prompt, Short/Normal/Long Caption.")
florence: Optional[List[str]] = Field(default=None, title="Florence", description="Florence-2 base model tasks: Phrase Grounding, Object Detection, Dense Region Caption, Region Proposal, OCR (Read Text), OCR with Regions.")
promptgen: Optional[List[str]] = Field(default=None, title="PromptGen", description="MiaoshouAI PromptGen fine-tune tasks: Analyze, Generate Tags, Mixed Caption, Mixed Caption+. Only available on PromptGen models.")
moondream: Optional[List[str]] = Field(default=None, title="Moondream", description="Moondream 2 and 3 tasks: Point at..., Detect all...")
moondream2_only: Optional[List[str]] = Field(default=None, title="Moondream 2 Only", description="Moondream 2 exclusive tasks: Detect Gaze. Not available in Moondream 3.")
available: Optional[List[str]] = Field(default=None, title="Available", description="Populated only when filtering by model. Contains the combined list of prompts available for the specified model.")
common: list[str] | None = Field(default=None, title="Common", description="Prompts available for all models: Use Prompt, Short/Normal/Long Caption.")
florence: list[str] | None = Field(default=None, title="Florence", description="Florence-2 base model tasks: Phrase Grounding, Object Detection, Dense Region Caption, Region Proposal, OCR (Read Text), OCR with Regions.")
promptgen: list[str] | None = Field(default=None, title="PromptGen", description="MiaoshouAI PromptGen fine-tune tasks: Analyze, Generate Tags, Mixed Caption, Mixed Caption+. Only available on PromptGen models.")
moondream: list[str] | None = Field(default=None, title="Moondream", description="Moondream 2 and 3 tasks: Point at..., Detect all...")
moondream2_only: list[str] | None = Field(default=None, title="Moondream 2 Only", description="Moondream 2 exclusive tasks: Detect Gaze. Not available in Moondream 3.")
available: list[str] | None = Field(default=None, title="Available", description="Populated only when filtering by model. Contains the combined list of prompts available for the specified model.")
class ItemTaggerModel(BaseModel):
"""Tagger model information."""
@@ -136,7 +136,7 @@ class ReqTagger(BaseModel):
class ResTagger(BaseModel):
"""Response model for image tagging results."""
tags: str = Field(title="Tags", description="Comma-separated list of detected tags")
scores: Optional[dict] = Field(default=None, title="Scores", description="Tag confidence scores (when show_scores=True)")
scores: dict | None = Field(default=None, title="Scores", description="Tag confidence scores (when show_scores=True)")
# =============================================================================
@@ -158,12 +158,12 @@ class ReqCaptionOpenCLIP(BaseModel):
blip_model: str = Field(default="blip-large", title="Caption Model", description="BLIP model used to generate the initial image caption.")
mode: str = Field(default="best", title="Mode", description="Caption mode: 'best' (highest quality, slowest), 'fast' (quick, fewer flavors), 'classic' (balanced), 'caption' (BLIP only, no CLIP matching), 'negative' (for negative prompts).")
analyze: bool = Field(default=False, title="Analyze", description="If True, returns detailed breakdown (medium, artist, movement, trending, flavor).")
max_length: Optional[int] = Field(default=None, title="Max Length", description="Maximum tokens in generated caption.")
chunk_size: Optional[int] = Field(default=None, title="Chunk Size", description="Batch size for processing flavors.")
min_flavors: Optional[int] = Field(default=None, title="Min Flavors", description="Minimum descriptive tags to keep.")
max_flavors: Optional[int] = Field(default=None, title="Max Flavors", description="Maximum descriptive tags to keep.")
flavor_count: Optional[int] = Field(default=None, title="Intermediates", description="Size of intermediate candidate pool.")
num_beams: Optional[int] = Field(default=None, title="Num Beams", description="Beams for beam search during caption generation.")
max_length: int | None = Field(default=None, title="Max Length", description="Maximum tokens in generated caption.")
chunk_size: int | None = Field(default=None, title="Chunk Size", description="Batch size for processing flavors.")
min_flavors: int | None = Field(default=None, title="Min Flavors", description="Minimum descriptive tags to keep.")
max_flavors: int | None = Field(default=None, title="Max Flavors", description="Maximum descriptive tags to keep.")
flavor_count: int | None = Field(default=None, title="Intermediates", description="Size of intermediate candidate pool.")
num_beams: int | None = Field(default=None, title="Num Beams", description="Beams for beam search during caption generation.")
class ReqCaptionTagger(BaseModel):
@@ -196,24 +196,24 @@ class ReqCaptionVLM(BaseModel):
image: str = Field(default="", title="Image", description="Image to caption. Must be a Base64 encoded string.")
model: str = Field(default="Alibaba Qwen 2.5 VL 3B", title="Model", description="VLM model to use. See GET /sdapi/v1/vqa/models for full list.")
question: str = Field(default="describe the image", title="Question/Task", description="Task to perform: 'Short Caption', 'Normal Caption', 'Long Caption', 'Use Prompt' (custom text via prompt field). Model-specific tasks available via GET /sdapi/v1/vqa/prompts.")
prompt: Optional[str] = Field(default=None, title="Prompt", description="Custom prompt text when question is 'Use Prompt'.")
prompt: str | None = Field(default=None, title="Prompt", description="Custom prompt text when question is 'Use Prompt'.")
system: str = Field(default="You are image captioning expert, creative, unbiased and uncensored.", title="System Prompt", description="System prompt for LLM behavior.")
include_annotated: bool = Field(default=False, title="Include Annotated Image", description="Return annotated image for detection tasks.")
max_tokens: Optional[int] = Field(default=None, title="Max Tokens", description="Maximum tokens in response.")
temperature: Optional[float] = Field(default=None, title="Temperature", description="Randomness in token selection (0=deterministic, 0.9=creative).")
top_k: Optional[int] = Field(default=None, title="Top-K", description="Limit to K most likely tokens per step.")
top_p: Optional[float] = Field(default=None, title="Top-P", description="Nucleus sampling threshold.")
num_beams: Optional[int] = Field(default=None, title="Num Beams", description="Beam search width (1=disabled).")
do_sample: Optional[bool] = Field(default=None, title="Use Samplers", description="Enable sampling vs greedy decoding.")
thinking_mode: Optional[bool] = Field(default=None, title="Thinking Mode", description="Enable reasoning mode (supported models only).")
prefill: Optional[str] = Field(default=None, title="Prefill Text", description="Pre-fill response start to guide output.")
keep_thinking: Optional[bool] = Field(default=None, title="Keep Thinking Trace", description="Include reasoning in output.")
keep_prefill: Optional[bool] = Field(default=None, title="Keep Prefill", description="Keep prefill text in final output.")
max_tokens: int | None = Field(default=None, title="Max Tokens", description="Maximum tokens in response.")
temperature: float | None = Field(default=None, title="Temperature", description="Randomness in token selection (0=deterministic, 0.9=creative).")
top_k: int | None = Field(default=None, title="Top-K", description="Limit to K most likely tokens per step.")
top_p: float | None = Field(default=None, title="Top-P", description="Nucleus sampling threshold.")
num_beams: int | None = Field(default=None, title="Num Beams", description="Beam search width (1=disabled).")
do_sample: bool | None = Field(default=None, title="Use Samplers", description="Enable sampling vs greedy decoding.")
thinking_mode: bool | None = Field(default=None, title="Thinking Mode", description="Enable reasoning mode (supported models only).")
prefill: str | None = Field(default=None, title="Prefill Text", description="Pre-fill response start to guide output.")
keep_thinking: bool | None = Field(default=None, title="Keep Thinking Trace", description="Include reasoning in output.")
keep_prefill: bool | None = Field(default=None, title="Keep Prefill", description="Keep prefill text in final output.")
# Discriminated union for the dispatch endpoint
ReqCaptionDispatch = Annotated[
Union[ReqCaptionOpenCLIP, ReqCaptionTagger, ReqCaptionVLM],
ReqCaptionOpenCLIP | ReqCaptionTagger | ReqCaptionVLM,
Field(discriminator="backend")
]
@@ -226,18 +226,18 @@ class ResCaptionDispatch(BaseModel):
# Common
backend: str = Field(title="Backend", description="The backend that processed the request: 'openclip', 'tagger', or 'vlm'.")
# OpenCLIP fields
caption: Optional[str] = Field(default=None, title="Caption", description="Generated caption (OpenCLIP backend).")
medium: Optional[str] = Field(default=None, title="Medium", description="Detected artistic medium (OpenCLIP with analyze=True).")
artist: Optional[str] = Field(default=None, title="Artist", description="Detected artist style (OpenCLIP with analyze=True).")
movement: Optional[str] = Field(default=None, title="Movement", description="Detected art movement (OpenCLIP with analyze=True).")
trending: Optional[str] = Field(default=None, title="Trending", description="Trending tags (OpenCLIP with analyze=True).")
flavor: Optional[str] = Field(default=None, title="Flavor", description="Flavor descriptors (OpenCLIP with analyze=True).")
caption: str | None = Field(default=None, title="Caption", description="Generated caption (OpenCLIP backend).")
medium: str | None = Field(default=None, title="Medium", description="Detected artistic medium (OpenCLIP with analyze=True).")
artist: str | None = Field(default=None, title="Artist", description="Detected artist style (OpenCLIP with analyze=True).")
movement: str | None = Field(default=None, title="Movement", description="Detected art movement (OpenCLIP with analyze=True).")
trending: str | None = Field(default=None, title="Trending", description="Trending tags (OpenCLIP with analyze=True).")
flavor: str | None = Field(default=None, title="Flavor", description="Flavor descriptors (OpenCLIP with analyze=True).")
# Tagger fields
tags: Optional[str] = Field(default=None, title="Tags", description="Comma-separated tags (Tagger backend).")
scores: Optional[dict] = Field(default=None, title="Scores", description="Tag confidence scores (Tagger with show_scores=True).")
tags: str | None = Field(default=None, title="Tags", description="Comma-separated tags (Tagger backend).")
scores: dict | None = Field(default=None, title="Scores", description="Tag confidence scores (Tagger with show_scores=True).")
# VLM fields
answer: Optional[str] = Field(default=None, title="Answer", description="VLM response (VLM backend).")
annotated_image: Optional[str] = Field(default=None, title="Annotated Image", description="Base64 annotated image (VLM with include_annotated=True).")
answer: str | None = Field(default=None, title="Answer", description="VLM response (VLM backend).")
annotated_image: str | None = Field(default=None, title="Annotated Image", description="Base64 annotated image (VLM with include_annotated=True).")
# =============================================================================
@@ -596,7 +596,7 @@ def get_vqa_models():
return models_list
def get_vqa_prompts(model: Optional[str] = None):
def get_vqa_prompts(model: str | None = None):
"""
List available prompts/tasks for VLM models.
@@ -653,11 +653,11 @@ def get_tagger_models():
def register_api():
from modules.shared import api
api.add_api_route("/sdapi/v1/openclip", get_caption, methods=["GET"], response_model=List[str], tags=["Caption"])
api.add_api_route("/sdapi/v1/openclip", get_caption, methods=["GET"], response_model=list[str], tags=["Caption"])
api.add_api_route("/sdapi/v1/caption", post_caption_dispatch, methods=["POST"], response_model=ResCaptionDispatch, tags=["Caption"])
api.add_api_route("/sdapi/v1/openclip", post_caption, methods=["POST"], response_model=ResCaption, tags=["Caption"])
api.add_api_route("/sdapi/v1/vqa", post_vqa, methods=["POST"], response_model=ResVQA, tags=["Caption"])
api.add_api_route("/sdapi/v1/vqa/models", get_vqa_models, methods=["GET"], response_model=List[ItemVLMModel], tags=["Caption"])
api.add_api_route("/sdapi/v1/vqa/models", get_vqa_models, methods=["GET"], response_model=list[ItemVLMModel], tags=["Caption"])
api.add_api_route("/sdapi/v1/vqa/prompts", get_vqa_prompts, methods=["GET"], response_model=ResVLMPrompts, tags=["Caption"])
api.add_api_route("/sdapi/v1/tagger", post_tagger, methods=["POST"], response_model=ResTagger, tags=["Caption"])
api.add_api_route("/sdapi/v1/tagger/models", get_tagger_models, methods=["GET"], response_model=List[ItemTaggerModel], tags=["Caption"])
api.add_api_route("/sdapi/v1/tagger/models", get_tagger_models, methods=["GET"], response_model=list[ItemTaggerModel], tags=["Caption"])