mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 17:24:32 +02:00
refactor(caption): extract caption API into standalone module
Move all caption/interrogate/tagger/VQA API code out of the monolithic endpoints.py and models.py into a new self-contained modules/api/caption.py, following the loras.py / nudenet.py self-registering pattern. - Move 15 Pydantic models (ReqCaption, ResCaption, ReqVQA, ResVQA, ReqTagger, ResTagger, dispatch union types, etc.) from models.py - Move 11 handler functions from endpoints.py - Deduplicate ~150 lines via shared _do_openclip, _do_tagger, _do_vqa core functions called by both direct and dispatch endpoints - Add register_api() that registers all 8 caption routes - Add promptgen field to ResVLMPrompts (bug fix: handler returned it but response model silently dropped it) - Improve all endpoint docstrings and Field descriptions for API docs
This commit is contained in:
CalamitousFelicitousness
@@ -1,5 +1,4 @@
|
||||
from typing import Optional
|
||||
from fastapi.exceptions import HTTPException
|
||||
from modules import shared
|
||||
from modules.api import models, helpers
|
||||
|
||||
@@ -91,23 +90,6 @@ def get_extra_networks(page: Optional[str] = None, name: Optional[str] = None, f
|
||||
})
|
||||
return res
|
||||
|
||||
def get_caption():
|
||||
"""
|
||||
List available OpenCLIP caption models.
|
||||
|
||||
Returns model identifiers for use with POST /sdapi/v1/openclip or POST /sdapi/v1/caption (with backend="openclip").
|
||||
|
||||
**Model Types:**
|
||||
- OpenCLIP models: Format `architecture/pretrained_dataset` (e.g., `ViT-L-14/openai`)
|
||||
|
||||
**Example Response:**
|
||||
```json
|
||||
["ViT-L-14/openai", "ViT-H-14/laion2b_s32b_b79k"]
|
||||
```
|
||||
"""
|
||||
from modules.caption.openclip import refresh_clip_models
|
||||
return refresh_clip_models()
|
||||
|
||||
def get_schedulers():
|
||||
from modules.sd_samplers import list_samplers
|
||||
all_schedulers = list_samplers()
|
||||
@@ -115,510 +97,6 @@ def get_schedulers():
|
||||
shared.log.critical(s)
|
||||
return all_schedulers
|
||||
|
||||
def post_caption(req: models.ReqCaption):
|
||||
"""
|
||||
Caption an image using OpenCLIP/BLIP (direct endpoint).
|
||||
|
||||
This is the direct endpoint for OpenCLIP captioning. For a unified interface
|
||||
that can dispatch to OpenCLIP, Tagger, or VLM, use POST /sdapi/v1/caption instead.
|
||||
|
||||
Analyze image using CLIP model via OpenCLIP to generate Stable Diffusion prompts.
|
||||
|
||||
- Uses CLIP for image-text matching, BLIP for captioning
|
||||
- **Modes:**
|
||||
- `best`: Highest quality, combines multiple techniques
|
||||
- `fast`: Quick results with fewer iterations
|
||||
- `classic`: Traditional CLIP captioner style
|
||||
- `caption`: BLIP caption only
|
||||
- `negative`: Generate negative prompt suggestions
|
||||
- Set `analyze=True` for detailed breakdown (medium, artist, movement, trending, flavor)
|
||||
|
||||
**Error Codes:**
|
||||
- 404: Image not provided or model not found
|
||||
"""
|
||||
if req.image is None or len(req.image) < 64:
|
||||
raise HTTPException(status_code=404, detail="Image not found")
|
||||
image = helpers.decode_base64_to_image(req.image)
|
||||
image = image.convert('RGB')
|
||||
from modules.caption.openclip import caption_image, analyze_image, refresh_clip_models
|
||||
if req.model not in refresh_clip_models():
|
||||
raise HTTPException(status_code=404, detail="Model not found")
|
||||
# Build clip overrides from request (only include non-None values)
|
||||
clip_overrides = {}
|
||||
if req.max_length is not None:
|
||||
clip_overrides['max_length'] = req.max_length
|
||||
if req.chunk_size is not None:
|
||||
clip_overrides['chunk_size'] = req.chunk_size
|
||||
if req.min_flavors is not None:
|
||||
clip_overrides['min_flavors'] = req.min_flavors
|
||||
if req.max_flavors is not None:
|
||||
clip_overrides['max_flavors'] = req.max_flavors
|
||||
if req.flavor_count is not None:
|
||||
clip_overrides['flavor_count'] = req.flavor_count
|
||||
if req.num_beams is not None:
|
||||
clip_overrides['num_beams'] = req.num_beams
|
||||
try:
|
||||
caption = caption_image(image, clip_model=req.clip_model, blip_model=req.blip_model, mode=req.mode, overrides=clip_overrides if clip_overrides else None)
|
||||
except Exception as e:
|
||||
caption = str(e)
|
||||
if not req.analyze:
|
||||
return models.ResCaption(caption=caption)
|
||||
analyze_results = analyze_image(image, clip_model=req.clip_model, blip_model=req.blip_model)
|
||||
# Extract top-ranked item from each Gradio update dict
|
||||
def get_top_item(result):
|
||||
if isinstance(result, dict) and 'value' in result:
|
||||
value = result['value']
|
||||
if isinstance(value, dict) and value:
|
||||
return next(iter(value.keys())) # First key = top ranked
|
||||
if isinstance(value, str):
|
||||
return value
|
||||
return None
|
||||
medium = get_top_item(analyze_results[0])
|
||||
artist = get_top_item(analyze_results[1])
|
||||
movement = get_top_item(analyze_results[2])
|
||||
trending = get_top_item(analyze_results[3])
|
||||
flavor = get_top_item(analyze_results[4])
|
||||
return models.ResCaption(caption=caption, medium=medium, artist=artist, movement=movement, trending=trending, flavor=flavor)
|
||||
|
||||
|
||||
def post_vqa(req: models.ReqVQA):
|
||||
"""
|
||||
Caption an image using Vision-Language Models (VLM).
|
||||
|
||||
Analyze image using vision language model for flexible image understanding.
|
||||
Supports 60+ models including Google Gemma, Alibaba Qwen, Microsoft Florence, Moondream.
|
||||
|
||||
**Common Tasks:**
|
||||
|
||||
1. **General Captioning**
|
||||
- `question="Short Caption"` or `"Normal Caption"` or `"Long Caption"`
|
||||
|
||||
2. **Object Detection** (Florence-2):
|
||||
- `question="<OD>"` - Returns bounding boxes
|
||||
- `question="<DENSE_REGION_CAPTION>"` - Captions for regions
|
||||
|
||||
3. **OCR/Text Recognition** (Florence-2):
|
||||
- `question="<OCR>"` - Extract text from image
|
||||
|
||||
4. **Point/Detect** (Moondream):
|
||||
- `question="Point at the cat"` - Returns coordinates
|
||||
- `question="Detect all faces"` - Returns all instances
|
||||
|
||||
**Annotated Images:**
|
||||
Set `include_annotated=True` to receive an annotated image with detection results.
|
||||
Returns Base64 PNG with bounding boxes and points drawn for:
|
||||
- Florence-2: Object detection, phrase grounding, region proposals
|
||||
- Moondream 2/3: Point detection, object detection, gaze detection
|
||||
|
||||
**Model Selection:**
|
||||
- Small/Fast: Florence 2 Base, SmolVLM 0.5B, FastVLM 0.5B
|
||||
- Balanced: Qwen 2.5 VL 3B, Florence 2 Large, Gemma 3 4B
|
||||
- High Quality: Qwen 3 VL 8B, JoyCaption Beta, Moondream 3
|
||||
|
||||
Use GET /sdapi/v1/vqa/models for complete model list.
|
||||
"""
|
||||
if req.image is None or len(req.image) < 64:
|
||||
raise HTTPException(status_code=404, detail="Image not found")
|
||||
image = helpers.decode_base64_to_image(req.image)
|
||||
image = image.convert('RGB')
|
||||
|
||||
# Build generation kwargs from request parameters (None values are ignored)
|
||||
generation_kwargs = {}
|
||||
if req.max_tokens is not None:
|
||||
generation_kwargs['max_tokens'] = req.max_tokens
|
||||
if req.temperature is not None:
|
||||
generation_kwargs['temperature'] = req.temperature
|
||||
if req.top_k is not None:
|
||||
generation_kwargs['top_k'] = req.top_k
|
||||
if req.top_p is not None:
|
||||
generation_kwargs['top_p'] = req.top_p
|
||||
if req.num_beams is not None:
|
||||
generation_kwargs['num_beams'] = req.num_beams
|
||||
if req.do_sample is not None:
|
||||
generation_kwargs['do_sample'] = req.do_sample
|
||||
if req.keep_thinking is not None:
|
||||
generation_kwargs['keep_thinking'] = req.keep_thinking
|
||||
if req.keep_prefill is not None:
|
||||
generation_kwargs['keep_prefill'] = req.keep_prefill
|
||||
|
||||
from modules.caption import vqa
|
||||
answer = vqa.caption(
|
||||
question=req.question,
|
||||
system_prompt=req.system,
|
||||
prompt=req.prompt or '',
|
||||
image=image,
|
||||
model_name=req.model,
|
||||
prefill=req.prefill,
|
||||
thinking_mode=req.thinking_mode,
|
||||
generation_kwargs=generation_kwargs if generation_kwargs else None
|
||||
)
|
||||
if isinstance(answer, str) and answer.startswith('Error:'):
|
||||
raise HTTPException(status_code=422, detail=answer)
|
||||
# Return annotated image if requested and available
|
||||
annotated_b64 = None
|
||||
if req.include_annotated:
|
||||
annotated_img = vqa.get_last_annotated_image()
|
||||
if annotated_img is not None:
|
||||
annotated_b64 = helpers.encode_pil_to_base64(annotated_img)
|
||||
return models.ResVQA(answer=answer, annotated_image=annotated_b64)
|
||||
|
||||
|
||||
def post_caption_dispatch(req: models.ReqCaptionDispatch):
|
||||
"""
|
||||
Unified caption endpoint - dispatches to OpenCLIP, Tagger, or VLM backends.
|
||||
|
||||
This endpoint provides a single entry point for all captioning needs. Select the backend
|
||||
using the `backend` field, then provide backend-specific parameters.
|
||||
|
||||
**Backends:**
|
||||
|
||||
1. **OpenCLIP** (`backend: "openclip"`):
|
||||
- CLIP/BLIP-based captioning for Stable Diffusion prompts
|
||||
- Modes: best, fast, classic, caption, negative
|
||||
- Set `analyze=True` for style breakdown
|
||||
|
||||
2. **Tagger** (`backend: "tagger"`):
|
||||
- WaifuDiffusion or DeepBooru anime/illustration tagging
|
||||
- Returns comma-separated booru-style tags
|
||||
- Configurable thresholds for general and character tags
|
||||
|
||||
3. **VLM** (`backend: "vlm"`):
|
||||
- Vision-Language Models (Qwen, Gemma, Florence, Moondream, etc.)
|
||||
- Flexible tasks: captioning, Q&A, object detection, OCR
|
||||
- Supports thinking mode for reasoning models
|
||||
|
||||
**Direct Endpoints:**
|
||||
For simpler requests, you can also use the direct endpoints:
|
||||
- POST /sdapi/v1/openclip - OpenCLIP only
|
||||
- POST /sdapi/v1/tagger - Tagger only
|
||||
- POST /sdapi/v1/vqa - VLM only
|
||||
"""
|
||||
if req.backend == "openclip":
|
||||
return _dispatch_openclip(req)
|
||||
elif req.backend == "tagger":
|
||||
return _dispatch_tagger(req)
|
||||
elif req.backend == "vlm":
|
||||
return _dispatch_vlm(req)
|
||||
else:
|
||||
raise HTTPException(status_code=400, detail=f"Unknown backend: {req.backend}")
|
||||
|
||||
|
||||
def _dispatch_openclip(req: models.ReqCaptionOpenCLIP) -> models.ResCaptionDispatch:
|
||||
"""Handle OpenCLIP dispatch."""
|
||||
if req.image is None or len(req.image) < 64:
|
||||
raise HTTPException(status_code=404, detail="Image not found")
|
||||
image = helpers.decode_base64_to_image(req.image)
|
||||
image = image.convert('RGB')
|
||||
from modules.caption.openclip import caption_image, analyze_image, refresh_clip_models
|
||||
if req.model not in refresh_clip_models():
|
||||
raise HTTPException(status_code=404, detail="Model not found")
|
||||
# Build clip overrides from request
|
||||
clip_overrides = {}
|
||||
if req.max_length is not None:
|
||||
clip_overrides['max_length'] = req.max_length
|
||||
if req.chunk_size is not None:
|
||||
clip_overrides['chunk_size'] = req.chunk_size
|
||||
if req.min_flavors is not None:
|
||||
clip_overrides['min_flavors'] = req.min_flavors
|
||||
if req.max_flavors is not None:
|
||||
clip_overrides['max_flavors'] = req.max_flavors
|
||||
if req.flavor_count is not None:
|
||||
clip_overrides['flavor_count'] = req.flavor_count
|
||||
if req.num_beams is not None:
|
||||
clip_overrides['num_beams'] = req.num_beams
|
||||
try:
|
||||
caption = caption_image(image, clip_model=req.clip_model, blip_model=req.blip_model, mode=req.mode, overrides=clip_overrides if clip_overrides else None)
|
||||
except Exception as e:
|
||||
caption = str(e)
|
||||
if not req.analyze:
|
||||
return models.ResCaptionDispatch(backend="openclip", caption=caption)
|
||||
analyze_results = analyze_image(image, clip_model=req.clip_model, blip_model=req.blip_model)
|
||||
# Extract top-ranked item from each Gradio update dict
|
||||
def get_top_item(result):
|
||||
if isinstance(result, dict) and 'value' in result:
|
||||
value = result['value']
|
||||
if isinstance(value, dict) and value:
|
||||
return next(iter(value.keys()))
|
||||
if isinstance(value, str):
|
||||
return value
|
||||
return None
|
||||
medium = get_top_item(analyze_results[0])
|
||||
artist = get_top_item(analyze_results[1])
|
||||
movement = get_top_item(analyze_results[2])
|
||||
trending = get_top_item(analyze_results[3])
|
||||
flavor = get_top_item(analyze_results[4])
|
||||
return models.ResCaptionDispatch(backend="openclip", caption=caption, medium=medium, artist=artist, movement=movement, trending=trending, flavor=flavor)
|
||||
|
||||
|
||||
def _dispatch_tagger(req: models.ReqCaptionTagger) -> models.ResCaptionDispatch:
|
||||
"""Handle Tagger dispatch."""
|
||||
if req.image is None or len(req.image) < 64:
|
||||
raise HTTPException(status_code=404, detail="Image not found")
|
||||
image = helpers.decode_base64_to_image(req.image)
|
||||
image = image.convert('RGB')
|
||||
from modules.caption import tagger
|
||||
is_deepbooru = req.model.lower() in ('deepbooru', 'deepdanbooru')
|
||||
# Store original settings
|
||||
original_opts = {
|
||||
'tagger_threshold': shared.opts.tagger_threshold,
|
||||
'tagger_max_tags': shared.opts.tagger_max_tags,
|
||||
'tagger_include_rating': shared.opts.tagger_include_rating,
|
||||
'tagger_sort_alpha': shared.opts.tagger_sort_alpha,
|
||||
'tagger_use_spaces': shared.opts.tagger_use_spaces,
|
||||
'tagger_escape_brackets': shared.opts.tagger_escape_brackets,
|
||||
'tagger_exclude_tags': shared.opts.tagger_exclude_tags,
|
||||
'tagger_show_scores': shared.opts.tagger_show_scores,
|
||||
}
|
||||
if not is_deepbooru:
|
||||
original_opts['waifudiffusion_character_threshold'] = shared.opts.waifudiffusion_character_threshold
|
||||
original_opts['waifudiffusion_model'] = shared.opts.waifudiffusion_model
|
||||
try:
|
||||
shared.opts.tagger_threshold = req.threshold
|
||||
shared.opts.tagger_max_tags = req.max_tags
|
||||
shared.opts.tagger_include_rating = req.include_rating
|
||||
shared.opts.tagger_sort_alpha = req.sort_alpha
|
||||
shared.opts.tagger_use_spaces = req.use_spaces
|
||||
shared.opts.tagger_escape_brackets = req.escape_brackets
|
||||
shared.opts.tagger_exclude_tags = req.exclude_tags
|
||||
shared.opts.tagger_show_scores = req.show_scores
|
||||
if not is_deepbooru:
|
||||
shared.opts.waifudiffusion_character_threshold = req.character_threshold
|
||||
shared.opts.waifudiffusion_model = req.model
|
||||
tags = tagger.tag(image, model_name='DeepBooru' if is_deepbooru else None)
|
||||
# Parse scores if requested
|
||||
scores = None
|
||||
if req.show_scores:
|
||||
scores = {}
|
||||
for item in tags.split(', '):
|
||||
item = item.strip()
|
||||
if item.startswith('(') and item.endswith(')') and ':' in item:
|
||||
inner = item[1:-1]
|
||||
tag, score_str = inner.rsplit(':', 1)
|
||||
try:
|
||||
scores[tag.strip()] = float(score_str.strip())
|
||||
except ValueError:
|
||||
pass
|
||||
elif ':' in item:
|
||||
tag, score_str = item.rsplit(':', 1)
|
||||
try:
|
||||
scores[tag.strip()] = float(score_str.strip())
|
||||
except ValueError:
|
||||
pass
|
||||
if not scores:
|
||||
scores = None
|
||||
return models.ResCaptionDispatch(backend="tagger", tags=tags, scores=scores)
|
||||
finally:
|
||||
for key, value in original_opts.items():
|
||||
setattr(shared.opts, key, value)
|
||||
|
||||
|
||||
def _dispatch_vlm(req: models.ReqCaptionVLM) -> models.ResCaptionDispatch:
|
||||
"""Handle VLM dispatch."""
|
||||
if req.image is None or len(req.image) < 64:
|
||||
raise HTTPException(status_code=404, detail="Image not found")
|
||||
image = helpers.decode_base64_to_image(req.image)
|
||||
image = image.convert('RGB')
|
||||
# Build generation kwargs
|
||||
generation_kwargs = {}
|
||||
if req.max_tokens is not None:
|
||||
generation_kwargs['max_tokens'] = req.max_tokens
|
||||
if req.temperature is not None:
|
||||
generation_kwargs['temperature'] = req.temperature
|
||||
if req.top_k is not None:
|
||||
generation_kwargs['top_k'] = req.top_k
|
||||
if req.top_p is not None:
|
||||
generation_kwargs['top_p'] = req.top_p
|
||||
if req.num_beams is not None:
|
||||
generation_kwargs['num_beams'] = req.num_beams
|
||||
if req.do_sample is not None:
|
||||
generation_kwargs['do_sample'] = req.do_sample
|
||||
if req.keep_thinking is not None:
|
||||
generation_kwargs['keep_thinking'] = req.keep_thinking
|
||||
if req.keep_prefill is not None:
|
||||
generation_kwargs['keep_prefill'] = req.keep_prefill
|
||||
from modules.caption import vqa
|
||||
answer = vqa.caption(
|
||||
question=req.question,
|
||||
system_prompt=req.system,
|
||||
prompt=req.prompt or '',
|
||||
image=image,
|
||||
model_name=req.model,
|
||||
prefill=req.prefill,
|
||||
thinking_mode=req.thinking_mode,
|
||||
generation_kwargs=generation_kwargs if generation_kwargs else None
|
||||
)
|
||||
if isinstance(answer, str) and answer.startswith('Error:'):
|
||||
raise HTTPException(status_code=422, detail=answer)
|
||||
annotated_b64 = None
|
||||
if req.include_annotated:
|
||||
annotated_img = vqa.get_last_annotated_image()
|
||||
if annotated_img is not None:
|
||||
annotated_b64 = helpers.encode_pil_to_base64(annotated_img)
|
||||
return models.ResCaptionDispatch(backend="vlm", answer=answer, annotated_image=annotated_b64)
|
||||
|
||||
|
||||
def get_vqa_models():
|
||||
"""
|
||||
List available VLM models for captioning.
|
||||
|
||||
Returns all Vision-Language Models available for POST /sdapi/v1/vqa.
|
||||
|
||||
**Response includes:**
|
||||
- `name`: Display name
|
||||
- `repo`: HuggingFace repository ID
|
||||
- `prompts`: Available prompts/tasks
|
||||
- `capabilities`: Model features (caption, vqa, detection, ocr, thinking)
|
||||
"""
|
||||
from modules.caption import vqa
|
||||
models_list = []
|
||||
for name, repo in vqa.vlm_models.items():
|
||||
prompts = vqa.get_prompts_for_model(name)
|
||||
capabilities = ["caption", "vqa"]
|
||||
# Detect additional capabilities based on model name
|
||||
name_lower = name.lower()
|
||||
if 'florence' in name_lower or 'promptgen' in name_lower:
|
||||
capabilities.extend(["detection", "ocr"])
|
||||
if 'moondream' in name_lower:
|
||||
capabilities.append("detection")
|
||||
if vqa.is_thinking_model(name):
|
||||
capabilities.append("thinking")
|
||||
models_list.append({
|
||||
"name": name,
|
||||
"repo": repo,
|
||||
"prompts": prompts,
|
||||
"capabilities": list(set(capabilities))
|
||||
})
|
||||
return models_list
|
||||
|
||||
def get_vqa_prompts(model: Optional[str] = None):
|
||||
"""
|
||||
List available prompts/tasks for VLM models.
|
||||
|
||||
**Query Parameters:**
|
||||
- `model` (optional): Filter prompts for a specific model
|
||||
|
||||
**Prompt Categories:**
|
||||
- Common: Use Prompt, Short/Normal/Long Caption
|
||||
- Florence: Phrase Grounding, Object Detection, OCR, Dense Region Caption (all Florence models)
|
||||
- PromptGen: Analyze, Generate Tags, Mixed Caption (MiaoshouAI PromptGen fine-tunes only)
|
||||
- Moondream: Point at..., Detect all..., Detect Gaze
|
||||
"""
|
||||
from modules.caption import vqa
|
||||
if model:
|
||||
prompts = vqa.get_prompts_for_model(model)
|
||||
return {"available": prompts}
|
||||
return {
|
||||
"common": vqa.vlm_prompts_common,
|
||||
"florence": vqa.vlm_prompts_florence,
|
||||
"promptgen": vqa.vlm_prompts_promptgen,
|
||||
"moondream": vqa.vlm_prompts_moondream,
|
||||
"moondream2_only": vqa.vlm_prompts_moondream2
|
||||
}
|
||||
|
||||
def get_tagger_models():
|
||||
"""
|
||||
List available tagger models.
|
||||
|
||||
Returns WaifuDiffusion and DeepBooru models for image tagging.
|
||||
|
||||
**WaifuDiffusion Models:**
|
||||
- `wd-eva02-large-tagger-v3` (recommended)
|
||||
- `wd-vit-tagger-v3`, `wd-convnext-tagger-v3`, `wd-swinv2-tagger-v3`
|
||||
|
||||
**DeepBooru:**
|
||||
- Legacy tagger for anime images
|
||||
"""
|
||||
from modules.caption import waifudiffusion
|
||||
models_list = []
|
||||
# Add WaifuDiffusion models
|
||||
for name in waifudiffusion.get_models():
|
||||
models_list.append({"name": name, "type": "waifudiffusion"})
|
||||
# Add DeepBooru
|
||||
models_list.append({"name": "deepbooru", "type": "deepbooru"})
|
||||
return models_list
|
||||
|
||||
def post_tagger(req: models.ReqTagger):
|
||||
"""
|
||||
Tag an image using WaifuDiffusion or DeepBooru.
|
||||
|
||||
Generate anime/illustration tags for images.
|
||||
|
||||
**WaifuDiffusion Models:**
|
||||
- `wd-eva02-large-tagger-v3` (recommended)
|
||||
- `wd-vit-tagger-v3`, `wd-convnext-tagger-v3`, `wd-swinv2-tagger-v3`
|
||||
|
||||
**DeepBooru:**
|
||||
- Legacy tagger, use `deepbooru` or `deepdanbooru`
|
||||
|
||||
**Thresholds:**
|
||||
- `threshold`: General tag confidence (default: 0.5)
|
||||
- `character_threshold`: Character identification (default: 0.85, WaifuDiffusion only)
|
||||
"""
|
||||
if req.image is None or len(req.image) < 64:
|
||||
raise HTTPException(status_code=404, detail="Image not found")
|
||||
image = helpers.decode_base64_to_image(req.image)
|
||||
image = image.convert('RGB')
|
||||
from modules.caption import tagger
|
||||
# Determine if using DeepBooru
|
||||
is_deepbooru = req.model.lower() in ('deepbooru', 'deepdanbooru')
|
||||
# Store original settings and apply request settings
|
||||
original_opts = {
|
||||
'tagger_threshold': shared.opts.tagger_threshold,
|
||||
'tagger_max_tags': shared.opts.tagger_max_tags,
|
||||
'tagger_include_rating': shared.opts.tagger_include_rating,
|
||||
'tagger_sort_alpha': shared.opts.tagger_sort_alpha,
|
||||
'tagger_use_spaces': shared.opts.tagger_use_spaces,
|
||||
'tagger_escape_brackets': shared.opts.tagger_escape_brackets,
|
||||
'tagger_exclude_tags': shared.opts.tagger_exclude_tags,
|
||||
'tagger_show_scores': shared.opts.tagger_show_scores,
|
||||
}
|
||||
# WaifuDiffusion-specific settings (not applicable to DeepBooru)
|
||||
if not is_deepbooru:
|
||||
original_opts['waifudiffusion_character_threshold'] = shared.opts.waifudiffusion_character_threshold
|
||||
original_opts['waifudiffusion_model'] = shared.opts.waifudiffusion_model
|
||||
try:
|
||||
shared.opts.tagger_threshold = req.threshold
|
||||
shared.opts.tagger_max_tags = req.max_tags
|
||||
shared.opts.tagger_include_rating = req.include_rating
|
||||
shared.opts.tagger_sort_alpha = req.sort_alpha
|
||||
shared.opts.tagger_use_spaces = req.use_spaces
|
||||
shared.opts.tagger_escape_brackets = req.escape_brackets
|
||||
shared.opts.tagger_exclude_tags = req.exclude_tags
|
||||
shared.opts.tagger_show_scores = req.show_scores
|
||||
# WaifuDiffusion-specific settings (not applicable to DeepBooru)
|
||||
if not is_deepbooru:
|
||||
shared.opts.waifudiffusion_character_threshold = req.character_threshold
|
||||
shared.opts.waifudiffusion_model = req.model
|
||||
tags = tagger.tag(image, model_name='DeepBooru' if is_deepbooru else None)
|
||||
# Parse scores if requested - format is "(tag:0.95)" from WaifuDiffusion/DeepBooru
|
||||
scores = None
|
||||
if req.show_scores:
|
||||
scores = {}
|
||||
for item in tags.split(', '):
|
||||
item = item.strip()
|
||||
# Format: "(tag:0.95)" - tag and score wrapped in parentheses
|
||||
if item.startswith('(') and item.endswith(')') and ':' in item:
|
||||
inner = item[1:-1] # Remove outer parentheses
|
||||
tag, score_str = inner.rsplit(':', 1)
|
||||
try:
|
||||
scores[tag.strip()] = float(score_str.strip())
|
||||
except ValueError:
|
||||
pass
|
||||
elif ':' in item:
|
||||
# Fallback format: "tag:0.95" without parentheses
|
||||
tag, score_str = item.rsplit(':', 1)
|
||||
try:
|
||||
scores[tag.strip()] = float(score_str.strip())
|
||||
except ValueError:
|
||||
pass
|
||||
if not scores:
|
||||
scores = None
|
||||
return models.ResTagger(tags=tags, scores=scores)
|
||||
finally:
|
||||
# Restore original settings
|
||||
for key, value in original_opts.items():
|
||||
setattr(shared.opts, key, value)
|
||||
|
||||
def post_unload_checkpoint():
|
||||
from modules import sd_models
|
||||
sd_models.unload_model_weights(op='model')
|
||||
|
||||
Reference in New Issue
Block a user