character-browser/CLAUDE.md

GAZE — Character Browser: LLM Development Guide

What This Project Is

GAZE is a Flask web app for managing AI image generation assets and generating images via ComfyUI. It is a personal creative tool for organizing characters, outfits, actions, styles, scenes, and detailers — all of which map to Stable Diffusion LoRAs and prompt fragments — and generating images by wiring those assets into a ComfyUI workflow at runtime.

The app is deployed locally, connects to a local ComfyUI instance at http://127.0.0.1:8188, and uses SQLite for persistence. LoRA and model files live on /mnt/alexander/AITools/Image Models/.

---

Architecture

File Structure

app.py                          # ~186 lines: Flask init, config, logging, route registration, startup/migrations
models.py                       # SQLAlchemy models only
comfy_workflow.json              # ComfyUI workflow template with placeholder strings
utils.py                        # Pure constants + helpers (no Flask/DB deps)
services/
  __init__.py
  comfyui.py                    # ComfyUI HTTP client (queue_prompt, get_history, get_image)
  workflow.py                   # Workflow building (_prepare_workflow, _apply_checkpoint_settings)
  prompts.py                    # Prompt building + dedup (build_prompt, build_extras_prompt)
  llm.py                        # LLM integration + MCP tool calls (call_llm, load_prompt)
  mcp.py                        # MCP/Docker server lifecycle (ensure_mcp_server_running)
  sync.py                       # All sync_*() functions + preset resolution helpers
  job_queue.py                  # Background job queue (_enqueue_job, _make_finalize, worker thread)
  file_io.py                    # LoRA/checkpoint scanning, file helpers
routes/
  __init__.py                   # register_routes(app) — imports and calls all route modules
  characters.py                 # Character CRUD + generation + outfit management
  outfits.py                    # Outfit routes
  actions.py                    # Action routes
  styles.py                     # Style routes
  scenes.py                     # Scene routes
  detailers.py                  # Detailer routes
  checkpoints.py                # Checkpoint routes
  looks.py                      # Look routes
  presets.py                    # Preset routes
  generator.py                  # Generator mix-and-match page
  gallery.py                    # Gallery browsing + image/resource deletion
  settings.py                   # Settings page + status APIs + context processors
  strengths.py                  # Strengths gallery system
  transfer.py                   # Resource transfer system
  queue_api.py                  # /api/queue/* endpoints

Dependency Graph

app.py
  ├── models.py (unchanged)
  ├── utils.py (no deps except stdlib)
  ├── services/
  │   ├── comfyui.py        ← utils (for config)
  │   ├── prompts.py        ← utils, models
  │   ├── workflow.py       ← prompts, utils, models
  │   ├── llm.py            ← mcp (for tool calls)
  │   ├── mcp.py            ← (stdlib only: subprocess, os)
  │   ├── sync.py           ← models, utils
  │   ├── job_queue.py      ← comfyui, models
  │   └── file_io.py        ← models, utils
  └── routes/
      ├── All route modules  ← services/*, utils, models
      └── (routes never import from other routes)

No circular imports: routes → services → utils/models. Services never import routes. Utils never imports services.

Route Registration Pattern

Routes use a register_routes(app) closure pattern — each route module defines a function that receives the Flask app object and registers routes via @app.route() closures. This preserves all existing url_for() endpoint names without requiring Blueprint prefixes. Helper functions used only by routes in that module are defined inside register_routes() before the routes that reference them.

Database

SQLite at instance/database.db, managed by Flask-SQLAlchemy. The DB is a cache of the JSON files on disk — the JSON files are the source of truth.

Models: Character, Look, Outfit, Action, Style, Scene, Detailer, Checkpoint, Settings

All category models (except Settings and Checkpoint) share this pattern:

• {entity}_id — canonical ID (from JSON, often matches filename without extension)
• slug — URL-safe version of the ID (alphanumeric + underscores only, via re.sub(r'[^a-zA-Z0-9_]', '', id))
• name — display name
• filename — original JSON filename
• data — full JSON blob (SQLAlchemy JSON column)
• default_fields — list of section::key strings saved as the user's preferred prompt fields
• image_path — relative path under static/uploads/

Data Flow: JSON → DB → Prompt → ComfyUI

1. JSON files in data/{characters,clothing,actions,styles,scenes,detailers,looks}/ are loaded by sync_*() functions into SQLite.
2. At generation time, build_prompt(data, selected_fields, default_fields, active_outfit) converts the character JSON blob into {"main": ..., "face": ..., "hand": ...} prompt strings.
3. _prepare_workflow(workflow, character, prompts, ...) wires prompts and LoRAs into the loaded comfy_workflow.json.
4. queue_prompt(workflow, client_id) POSTs the workflow to ComfyUI's /prompt endpoint.
5. The app polls get_history(prompt_id) and retrieves the image via get_image(filename, subfolder, type).

---

ComfyUI Workflow Node Map

The workflow (comfy_workflow.json) uses string node IDs. These are the critical nodes:

Node	Role
3	Main KSampler
4	Checkpoint loader
5	Empty latent (width/height)
6	Positive prompt — contains {{POSITIVE_PROMPT}} placeholder
7	Negative prompt
8	VAE decode
9	Save image
11	Face ADetailer
13	Hand ADetailer
14	Face detailer prompt — contains {{FACE_PROMPT}} placeholder
15	Hand detailer prompt — contains {{HAND_PROMPT}} placeholder
16	Character LoRA (or Look LoRA when a Look is active)
17	Outfit LoRA
18	Action LoRA
19	Style / Detailer / Scene LoRA (priority: style > detailer > scene)

LoRA nodes chain: 4 → 16 → 17 → 18 → 19. Unused LoRA nodes are bypassed by pointing model_source/clip_source directly to the prior node. All model/clip consumers (nodes 3, 6, 7, 11, 13, 14, 15) are wired to the final model_source/clip_source at the end of _prepare_workflow.

---

Key Functions by Module

utils.py — Constants and Pure Helpers

• _IDENTITY_KEYS / _WARDROBE_KEYS — Lists of canonical field names for the identity and wardrobe sections. Used by _ensure_character_fields().
• ALLOWED_EXTENSIONS — Permitted upload file extensions.
• _LORA_DEFAULTS — Default LoRA directory paths per category.
• parse_orientation(orientation_str) — Converts orientation codes (1F, 2F, 1M1F, etc.) into Danbooru tags.
• _resolve_lora_weight(lora_data) — Extracts and validates LoRA weight from a lora data dict.
• allowed_file(filename) — Checks file extension against ALLOWED_EXTENSIONS.

services/prompts.py — Prompt Building

• build_prompt(data, selected_fields, default_fields, active_outfit) — Converts a character (or combined) data dict into {"main", "face", "hand"} prompt strings. Field selection priority: selected_fields → default_fields → select all (fallback). Fields are addressed as "section::key" strings (e.g. "identity::hair", "wardrobe::top"). Characters support a nested wardrobe format where wardrobe is a dict of outfit names → outfit dicts.
• build_extras_prompt(actions, outfits, scenes, styles, detailers) — Used by the Generator page. Combines prompt text from all checked items across categories into a single string.
• _cross_dedup_prompts(positive, negative) — Cross-deduplicates tags between positive and negative prompt strings. Equal counts cancel completely; excess on one side is retained.
• _resolve_character(character_slug) — Returns a Character ORM object for a given slug string. Handles "__random__" sentinel.
• _ensure_character_fields(character, selected_fields, ...) — Mutates selected_fields in place, appending populated identity/wardrobe keys. Called in every secondary-category generate route after _resolve_character().
• _append_background(prompts, character=None) — Appends "<primary_color> simple background" tag to prompts['main'].

services/workflow.py — Workflow Wiring

• _prepare_workflow(workflow, character, prompts, ...) — Core workflow wiring function. Replaces prompt placeholders, chains LoRA nodes dynamically, randomises seeds, applies checkpoint settings, runs cross-dedup as the final step.
• _apply_checkpoint_settings(workflow, ckpt_data) — Applies checkpoint-specific sampler/prompt/VAE settings.
• _get_default_checkpoint() — Returns (checkpoint_path, checkpoint_data) from session, database Settings, or workflow file fallback.
• _log_workflow_prompts(label, workflow) — Logs the fully assembled workflow prompts in a readable block.

services/job_queue.py — Background Job Queue

• _enqueue_job(label, workflow, finalize_fn) — Adds a generation job to the queue.
• _make_finalize(category, slug, db_model_class=None, action=None) — Factory returning a callback that retrieves the generated image from ComfyUI, saves it, and optionally updates the DB cover image.
• _prune_job_history(max_age_seconds=3600) — Removes old terminal-state jobs from memory.
• init_queue_worker(flask_app) — Stores the app reference and starts the worker thread.

services/comfyui.py — ComfyUI HTTP Client

• queue_prompt(prompt_workflow, client_id) — POSTs workflow to ComfyUI's /prompt endpoint.
• get_history(prompt_id) — Polls ComfyUI for job completion.
• get_image(filename, subfolder, folder_type) — Retrieves generated image bytes.
• _ensure_checkpoint_loaded(checkpoint_path) — Forces ComfyUI to load a specific checkpoint.

services/llm.py — LLM Integration

• call_llm(prompt, system_prompt) — OpenAI-compatible chat completion supporting OpenRouter (cloud) and Ollama/LMStudio (local). Implements a tool-calling loop (up to 10 turns) using DANBOORU_TOOLS via MCP Docker container.
• load_prompt(filename) — Loads system prompt text from data/prompts/.
• call_mcp_tool() — Synchronous wrapper for MCP tool calls.

services/sync.py — Data Synchronization

• sync_characters(), sync_outfits(), sync_actions(), etc. — Load JSON files from data/ directories into SQLite. One function per category.
• _resolve_preset_entity(type, id) / _resolve_preset_fields(preset_data) — Preset resolution helpers.

services/file_io.py — File & DB Helpers

• get_available_loras(category) — Scans filesystem for available LoRA files in a category.
• get_available_checkpoints() — Scans checkpoint directories.
• _count_look_assignments() / _count_outfit_lora_assignments() — DB aggregate queries.

services/mcp.py — MCP/Docker Lifecycle

• ensure_mcp_server_running() — Ensures the danbooru-mcp Docker container is running.
• ensure_character_mcp_server_running() — Ensures the character-mcp Docker container is running.

Route-local Helpers

Some helpers are defined inside a route module's register_routes() since they're only used by routes in that file:

• routes/scenes.py: _queue_scene_generation() — scene-specific workflow builder
• routes/detailers.py: _queue_detailer_generation() — detailer-specific generation helper
• routes/styles.py: _build_style_workflow() — style-specific workflow builder
• routes/checkpoints.py: _build_checkpoint_workflow() — checkpoint-specific workflow builder
• routes/strengths.py: _build_strengths_prompts(), _prepare_strengths_workflow() — strengths gallery helpers
• routes/transfer.py: _create_minimal_template() — transfer template builder
• routes/gallery.py: _scan_gallery_images(), _enrich_with_names(), _parse_comfy_png_metadata()

---

JSON Data Schemas

Character (data/characters/*.json)

{
  "character_id": "tifa_lockhart",
  "character_name": "Tifa Lockhart",
  "identity": { "base_specs": "", "hair": "", "eyes": "", "hands": "", "arms": "", "torso": "", "pelvis": "", "legs": "", "feet": "", "extra": "" },
  "defaults": { "expression": "", "pose": "", "scene": "" },
  "wardrobe": {
    "default": { "full_body": "", "headwear": "", "top": "", "bottom": "", "legwear": "", "footwear": "", "hands": "", "gloves": "", "accessories": "" }
  },
  "styles": { "aesthetic": "", "primary_color": "", "secondary_color": "", "tertiary_color": "" },
  "lora": { "lora_name": "Illustrious/Looks/tifa.safetensors", "lora_weight": 0.8, "lora_triggers": "" },
  "tags": [],
  "participants": { "orientation": "1F", "solo_focus": "true" }
}

participants is optional; when absent, (solo:1.2) is injected. orientation is parsed by parse_orientation() into Danbooru tags (1girl, hetero, etc.).

Outfit (data/clothing/*.json)

{
  "outfit_id": "french_maid_01",
  "outfit_name": "French Maid",
  "wardrobe": { "full_body": "", "headwear": "", "top": "", "bottom": "", "legwear": "", "footwear": "", "hands": "", "accessories": "" },
  "lora": { "lora_name": "Illustrious/Clothing/maid.safetensors", "lora_weight": 0.8, "lora_triggers": "" },
  "tags": []
}

Action (data/actions/*.json)

{
  "action_id": "sitting",
  "action_name": "Sitting",
  "action": { "full_body": "", "additional": "", "head": "", "eyes": "", "arms": "", "hands": "" },
  "lora": { "lora_name": "", "lora_weight": 1.0, "lora_triggers": "" },
  "tags": []
}

Scene (data/scenes/*.json)

{
  "scene_id": "beach",
  "scene_name": "Beach",
  "scene": { "background": "", "foreground": "", "furniture": "", "colors": "", "lighting": "", "theme": "" },
  "lora": { "lora_name": "", "lora_weight": 1.0, "lora_triggers": "" },
  "tags": []
}

Style (data/styles/*.json)

{
  "style_id": "watercolor",
  "style_name": "Watercolor",
  "style": { "artist_name": "", "artistic_style": "" },
  "lora": { "lora_name": "", "lora_weight": 1.0, "lora_triggers": "" }
}

Detailer (data/detailers/*.json)

{
  "detailer_id": "detailed_skin",
  "detailer_name": "Detailed Skin",
  "prompt": ["detailed skin", "pores"],
  "focus": { "face": true, "hands": true },
  "lora": { "lora_name": "", "lora_weight": 1.0, "lora_triggers": "" }
}

Look (data/looks/*.json)

{
  "look_id": "tifa_casual",
  "look_name": "Tifa Casual",
  "character_id": "tifa_lockhart",
  "positive": "casual clothes, jeans",
  "negative": "revealing",
  "lora": { "lora_name": "Illustrious/Looks/tifa_casual.safetensors", "lora_weight": 0.85, "lora_triggers": "" },
  "tags": []
}

Looks occupy LoRA node 16, overriding the character's own LoRA. The Look's negative is prepended to the workflow's negative prompt.

Checkpoint (data/checkpoints/*.json)

{
  "checkpoint_path": "Illustrious/model.safetensors",
  "checkpoint_name": "Model Display Name",
  "base_positive": "anime",
  "base_negative": "text, logo",
  "steps": 25,
  "cfg": 5,
  "sampler_name": "euler_ancestral",
  "scheduler": "normal",
  "vae": "integrated"
}

Checkpoint JSONs are keyed by checkpoint_path. If no JSON exists for a discovered model file, _default_checkpoint_data() provides defaults.

---

URL Routes

Characters

• GET / — character gallery (index)
• GET /character/<slug> — character detail with generation UI
• POST /character/<slug>/generate — queue generation (AJAX or form); returns {"job_id": ...}
• POST /character/<slug>/replace_cover_from_preview — promote preview to cover
• GET/POST /character/<slug>/edit — edit character data
• POST /character/<slug>/upload — upload cover image
• POST /character/<slug>/save_defaults — save default field selection
• POST /character/<slug>/outfit/switch|add|delete|rename — manage per-character wardrobe outfits
• GET/POST /create — create new character (blank or LLM-generated)
• POST /rescan — sync DB from JSON files

Category Pattern (Outfits, Actions, Styles, Scenes, Detailers)

Each category follows the same URL pattern:

• GET /<category>/ — gallery
• GET /<category>/<slug> — detail + generation UI
• POST /<category>/<slug>/generate — queue generation; returns {"job_id": ...}
• POST /<category>/<slug>/replace_cover_from_preview
• GET/POST /<category>/<slug>/edit
• POST /<category>/<slug>/upload
• POST /<category>/<slug>/save_defaults
• POST /<category>/<slug>/clone — duplicate entry
• POST /<category>/<slug>/save_json — save raw JSON (from modal editor)
• POST /<category>/rescan
• POST /<category>/bulk_create — LLM-generate entries from LoRA files on disk

Looks

• GET /looks — gallery
• GET /look/<slug> — detail
• GET/POST /look/<slug>/edit
• POST /look/<slug>/generate — queue generation; returns {"job_id": ...}
• POST /look/<slug>/replace_cover_from_preview
• GET/POST /look/create
• POST /looks/rescan

Generator (Mix & Match)

• GET/POST /generator — freeform generator with multi-select accordion UI
• POST /generator/preview_prompt — AJAX: preview composed prompt without generating

Checkpoints

• GET /checkpoints — gallery
• GET /checkpoint/<slug> — detail + generation settings editor
• POST /checkpoint/<slug>/save_json
• POST /checkpoints/rescan

Job Queue API

All generation routes use the background job queue. Frontend polls:

• GET /api/queue/<job_id>/status — returns {"status": "pending"|"running"|"done"|"failed", "result": {...}}

Image retrieval is handled server-side by the _make_finalize() callback; there are no separate client-facing finalize routes.

Utilities

• POST /set_default_checkpoint — save default checkpoint to session and persist to comfy_workflow.json
• GET /get_missing_{characters,outfits,actions,scenes,styles,detailers,looks,checkpoints} — AJAX: list items without cover images (sorted by display name)
• POST /generate_missing — batch generate covers for all characters missing one (uses job queue)
• POST /clear_all_covers / clear_all_{outfit,action,scene,style,detailer,look,checkpoint}_covers
• GET /gallery — global image gallery browsing static/uploads/
• GET/POST /settings — LLM provider configuration
• POST /resource/<category>/<slug>/delete — soft (JSON only) or hard (JSON + safetensors) delete

---

Frontend

• Bootstrap 5.3 (CDN). Custom styles in static/style.css.
• All templates extend templates/layout.html. The base layout provides:
  • {% block content %} — main page content
  • {% block scripts %} — additional JS at end of body
  • Navbar with links to all sections
  • Global default checkpoint selector (saves to session via AJAX)
  • Resource delete modal (soft/hard) shared across gallery pages
  • initJsonEditor(saveUrl) — shared JSON editor modal (simple form + raw textarea tabs)
• Context processors inject all_checkpoints, default_checkpoint_path, and COMFYUI_WS_URL into every template.
• No {% block head %} exists in layout.html — do not try to use it.
• Generation is async: JS submits the form via AJAX (X-Requested-With: XMLHttpRequest), receives a {"job_id": ...} response, then polls /api/queue/<job_id>/status every ~1.5 seconds until status == "done". The server-side worker handles all ComfyUI polling and image saving via the _make_finalize() callback. There are no client-facing finalize HTTP routes.
• Batch generation (library pages): Uses a two-phase pattern:
  1. Queue phase: All jobs are submitted upfront via sequential fetch calls, collecting job IDs
  2. Poll phase: All jobs are polled concurrently via Promise.all(), updating UI as each completes
  3. Progress tracking: Displays currently processing items in real-time using a Set to track active jobs
  4. Sorting: All batch operations sort items by display name (not filename) for better UX

---

LLM Integration

System Prompts

Text files in data/prompts/ define JSON output schemas for LLM-generated entries:

• character_system.txt — character JSON schema
• outfit_system.txt — outfit JSON schema
• action_system.txt, scene_system.txt, style_system.txt, detailer_system.txt, look_system.txt, checkpoint_system.txt

Used by: character/outfit/action/scene/style create forms, and bulk_create routes.

Danbooru MCP Tools

The LLM loop in call_llm() provides three tools via a Docker-based MCP server (danbooru-mcp:latest):

• search_tags(query, limit, category) — prefix search
• validate_tags(tags) — exact-match validation
• suggest_tags(partial, limit, category) — autocomplete

The LLM uses these to verify and discover correct Danbooru-compatible tags for prompts.

All system prompts (character_system.txt, outfit_system.txt, action_system.txt, scene_system.txt, style_system.txt, detailer_system.txt, look_system.txt, checkpoint_system.txt) instruct the LLM to use these tools before finalising any tag values. checkpoint_system.txt applies them specifically to the base_positive and base_negative fields.

---

LoRA File Paths

LoRA filenames in JSON are stored as paths relative to ComfyUI's models/lora/ root:

Category	Path prefix	Example
Character / Look	Illustrious/Looks/	Illustrious/Looks/tifa_v2.safetensors
Outfit	Illustrious/Clothing/	Illustrious/Clothing/maid.safetensors
Action	Illustrious/Poses/	Illustrious/Poses/sitting.safetensors
Style	Illustrious/Styles/	Illustrious/Styles/watercolor.safetensors
Detailer	Illustrious/Detailers/	Illustrious/Detailers/skin.safetensors
Scene	Illustrious/Backgrounds/	Illustrious/Backgrounds/beach.safetensors

Checkpoint paths: Illustrious/<filename>.safetensors or Noob/<filename>.safetensors.

Absolute paths on disk:

• Checkpoints: /mnt/alexander/AITools/Image Models/Stable-diffusion/{Illustrious,Noob}/
• LoRAs: /mnt/alexander/AITools/Image Models/lora/Illustrious/{Looks,Clothing,Poses,Styles,Detailers,Backgrounds}/

---

Adding a New Category

To add a new content category (e.g. "Poses" as a separate concept from Actions), the pattern is:

1. Model (models.py): Add a new SQLAlchemy model with the standard fields.
2. Sync function (services/sync.py): Add sync_newcategory() following the pattern of sync_outfits().
3. Data directory (app.py): Add app.config['NEWCATEGORY_DIR'] = 'data/newcategory'.
4. Routes (routes/newcategory.py): Create a new route module with a register_routes(app) function. Implement index, detail, edit, generate, replace_cover_from_preview, upload, save_defaults, clone, rescan routes. Follow routes/outfits.py or routes/scenes.py exactly.
5. Route registration (routes/__init__.py): Import and call newcategory.register_routes(app).
6. Templates: Create templates/newcategory/{index,detail,edit,create}.html extending layout.html.
7. Nav: Add link to navbar in templates/layout.html.
8. Startup (app.py): Import and call sync_newcategory() in the with app.app_context() block.
9. Generator page: Add to routes/generator.py, services/prompts.py build_extras_prompt(), and templates/generator.html accordion.

---

Session Keys

The Flask filesystem session stores:

• default_checkpoint — checkpoint_path string for the global default
• prefs_{slug} — selected_fields list for character detail page
• preview_{slug} — relative image path of last character preview
• prefs_outfit_{slug}, preview_outfit_{slug}, char_outfit_{slug} — outfit detail state
• prefs_action_{slug}, preview_action_{slug}, char_action_{slug} — action detail state
• prefs_scene_{slug}, preview_scene_{slug}, char_scene_{slug} — scene detail state
• prefs_detailer_{slug}, preview_detailer_{slug}, char_detailer_{slug}, action_detailer_{slug}, extra_pos_detailer_{slug}, extra_neg_detailer_{slug} — detailer detail state (selected fields, preview image, character, action LoRA, extra positive prompt, extra negative prompt)
• prefs_style_{slug}, preview_style_{slug}, char_style_{slug} — style detail state
• prefs_look_{slug}, preview_look_{slug} — look detail state

---

Running the App

Directly (development)

cd /mnt/alexander/Projects/character-browser
source venv/bin/activate
python app.py

The app runs in debug mode on port 5000 by default. ComfyUI must be running at http://127.0.0.1:8188.

The DB is initialised and all sync functions are called inside with app.app_context(): at the bottom of app.py before app.run().

Docker

docker compose up -d

The compose file (docker-compose.yml) runs two services:

• danbooru-mcp — built from https://git.liveaodh.com/aodhan/danbooru-mcp.git; the MCP tag-search container used by call_llm().
• app — the Flask app, exposed on host port 5782 → container port 5000.

Key environment variables set by compose:

• COMFYUI_URL=http://10.0.0.200:8188 — points at ComfyUI on the Docker host network.
• SKIP_MCP_AUTOSTART=true — disables the app's built-in danbooru-mcp launch logic (compose manages it).

Volumes mounted into the app container:

• ./data, ./static/uploads, ./instance, ./flask_session — persistent app data.
• /Volumes/ImageModels:/ImageModels:ro — model files for checkpoint/LoRA scanning (requires Docker Desktop file sharing enabled for /Volumes/ImageModels).
• /var/run/docker.sock — Docker socket so the app can exec danbooru-mcp tool containers.

---

Common Pitfalls

• SQLAlchemy JSON mutation: After modifying a JSON column dict in place, always call flag_modified(obj, "data") or the change won't be detected.
• Dual write: Every edit route writes back to both the DB (db.session.commit()) and the JSON file on disk. Both must be kept in sync.
• Slug generation: re.sub(r'[^a-zA-Z0-9_]', '', id) — note this removes hyphens and dots, not just replaces them. Character IDs like yuna_(ff10) become slug yunaffx10. This is intentional.
• Checkpoint slugs use underscore replacement: re.sub(r'[^a-zA-Z0-9_]', '_', ...) (replaces with _, not removes) to preserve readability in paths.
• LoRA chaining: If a LoRA node has no LoRA (name is empty/None), the node is skipped and model_source/clip_source pass through unchanged. Do not set the node inputs for skipped nodes.
• AJAX detection: request.headers.get('X-Requested-With') == 'XMLHttpRequest' determines whether to return JSON or redirect.
• Session must be marked modified for JSON responses: After setting session values in AJAX-responding routes, set session.modified = True.
• Detailer prompt is a list: The prompt field in detailer JSON is stored as a list of strings (e.g. ["detailed skin", "pores"]), not a plain string. When merging into tags for build_prompt, use extend for lists and append for strings — never append the list object itself or ", ".join() will fail on the nested list item.
• _make_finalize action semantics: Pass action=None when the route should always update the DB cover (e.g. batch generate, checkpoint generate). Pass action=request.form.get('action') for routes that support both "preview" (no DB update) and "replace" (update DB). The factory skips the DB write when action is truthy and not "replace".