316 lines
10 KiB
Markdown
316 lines
10 KiB
Markdown
# ComfyUI Prompt Builder
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Custom ComfyUI node for building prompts from the existing `generate_prompt_batches.py`
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generator without writing image batches.
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The node is registered as:
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- `prompt_builder / SxCP Prompt Builder`
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- `prompt_builder / SxCP Seed Control`
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- `prompt_builder / SxCP Caption Naturalizer`
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- `prompt_builder / SxCP Insta/OF Options`
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- `prompt_builder / SxCP Insta/OF Prompt Pair`
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It outputs:
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- `prompt`
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- `negative_prompt`
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- `caption`
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- `metadata_json`
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- `category`
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- `subcategory`
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`SxCP Seed Control` outputs `seed_config`, which can be connected to the prompt
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builder's optional `seed_config` input.
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`SxCP Caption Naturalizer` rewrites tag-like captions or labeled prompts into
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more natural language. Connect the prompt builder's `metadata_json` output to
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`source_text` for the cleanest result. You can also connect `caption` or
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`prompt`; in that case the node falls back to prompt-label parsing or comma-tag
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cleanup.
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Naturalizer controls:
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- `input_hint`: `auto`, `metadata_json`, or `caption_or_prompt`.
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- `detail_level`: `concise`, `balanced`, or `dense`.
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- `style_policy`: `drop_style_tail` removes old fixed style tails; `keep_style_terms`
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keeps style descriptions in the rewritten text.
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- `trigger`: defaults to `sxcppnl7`.
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- `include_trigger`: prepends the trigger as its own sentence.
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It outputs:
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- `natural_caption`
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- `method`
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`SxCP Insta/OF Prompt Pair` is a special paired-output mode. It creates one
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shared primary creator descriptor, then returns both a softcore prompt and a
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hardcore prompt from that same descriptor. This is useful when you want the same
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person/look/scene continuity but need two different prompt strengths.
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It outputs:
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- `softcore_prompt`
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- `hardcore_prompt`
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- `softcore_negative_prompt`
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- `hardcore_negative_prompt`
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- `softcore_caption`
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- `hardcore_caption`
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- `shared_descriptor`
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- `metadata_json`
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`SxCP Insta/OF Options` outputs `options_json`, which can be connected to the
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pair node. Defaults are set so the softcore prompt is solo while the hardcore
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prompt can include partners.
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Options:
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- `softcore_cast`: `solo` or `same_as_hardcore`.
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- `hardcore_cast`: `use_counts`, `couple`, `threesome`, or `group`.
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- `hardcore_women_count` and `hardcore_men_count`: used when `hardcore_cast` is
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`use_counts`. The pair mode always keeps at least one adult woman as the
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primary creator so the shared descriptor remains valid.
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- `softcore_level`: `social_tease`, `lingerie_tease`, `implied_nude`, or
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`explicit_tease`.
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- `hardcore_level`: `explicit` or `hardcore`.
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- `platform_style`: `hybrid`, `instagram`, or `onlyfans`.
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- `continuity`: `same_creator_same_room` keeps the scene/composition aligned;
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`same_creator_new_scene` keeps the same creator descriptor but lets the
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hardcore scene use its own setting.
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## Built-In Categories
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The node keeps the original generator controls:
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- `category`: `auto_weighted`, `woman`, `man`, `couple`, `group_or_layout`, or a custom JSON category.
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- `clothing`: `full` or `minimal`.
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- `minimal_clothing_ratio`: `-1` disables mixing; `0.0` to `1.0` mixes minimal/full clothing.
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- `ethnicity`: `any`, `asian`, `white_asian`.
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- `poses`: `standard` or `evocative`.
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- `standard_pose_ratio`: `-1` disables mixing; `0.0` to `1.0` mixes standard/evocative poses.
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- `backside_bias`: `0.0` to `1.0`, applies to evocative single-subject poses.
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- `figure`: `curvy`, `balanced`, `bombshell`.
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- `no_plus_women`: excludes plus-size women.
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- `no_black`: excludes Black/African-coded women from women-focused pools.
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`auto_weighted` uses the original batch mix: mostly women, then men, couples, and
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group/layout rows. Direct categories generate only that selected category.
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## Custom Categories
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Add or edit JSON files in `categories/*.json`. Each file can define new main
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categories, subcategories, and optional extensions to the built-in pools. Restart
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or reload ComfyUI after changing JSON so dropdown choices are rebuilt.
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Included JSON categories:
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- `Casual clothes`
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- `Provocative erotic clothes`
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- `Hardcore sexual poses`
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Example:
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```json
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{
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"version": 1,
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"categories": [
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{
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"name": "Casual clothes",
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"slug": "casual_clothes",
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"subject_type": "woman",
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"item_label": "Clothing",
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"style": "tasteful adult fashion-editorial coloured-pencil comic illustration",
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"subcategories": [
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{
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"name": "Streetwear",
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"slug": "streetwear",
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"items": [
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"oversized hoodie with slim jeans and clean sneakers",
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"cropped bomber jacket with cargo pants and chunky trainers"
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],
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"scenes": [
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{
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"slug": "city_crosswalk",
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"prompt": "sunlit city crosswalk with storefront reflections"
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}
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],
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"poses": [
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"standing with one hand in a pocket",
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"walking forward with a casual runway stride"
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]
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}
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]
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}
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]
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}
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```
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Custom categories do not need a Python generator. If no `prompt_template` is
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provided, the node uses a generic composer that selects subject appearance,
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scene, pose, expression, composition, and a random item from the selected
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subcategory.
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For large categories, prefer `item_templates` plus `item_axes` instead of writing
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every final item by hand:
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```json
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{
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"name": "Example clothes",
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"subject_type": "woman",
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"subcategories": [
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{
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"name": "Lingerie",
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"item_templates": [
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"{color} {fabric} {top} with {bottom} and {stockings}"
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],
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"item_axes": {
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"color": ["black", "red", "ivory"],
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"fabric": ["lace", "satin", "transparent mesh"],
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"top": ["balconette bra", "open-cup bra"],
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"bottom": ["matching thong", "high-cut g-string"],
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"stockings": ["thigh-high stockings", "fishnet stockings"]
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}
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}
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]
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}
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```
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The node chooses one template, fills each placeholder from the matching axis,
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then records the selected axis values in `metadata_json`.
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Supported `subject_type` values:
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- `single_any`
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- `woman`
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- `man`
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- `couple`
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- `group`
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- `layout`
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- `scene`
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- `configured_cast`
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`configured_cast` uses the node's `women_count` and `men_count` inputs. It adds
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template fields for `{women_count}`, `{men_count}`, `{person_count}`,
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`{cast_summary}`, `{scene_kind}`, and `{role_graph}`. This is intended for
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categories where the same prompt generator should support couples, threesomes,
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and larger groups.
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`{role_graph}` is a generated choreography sentence that assigns roles to the
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cast, such as who penetrates, who receives oral, and who joins from the side.
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It is currently most useful for `Hardcore sexual poses`.
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Subcategories, templates, and axis values can declare cast constraints:
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```json
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{
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"name": "Threesomes",
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"min_people": 3,
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"item_axes": {
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"act": [
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{
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"text": "strap-on penetration and cunnilingus",
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"cast": "women_only"
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},
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{
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"text": "male/male oral and anal contact",
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"cast": "men_only"
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},
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{
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"text": "front-and-back penetration",
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"cast": "mixed"
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}
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]
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}
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}
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```
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Supported constraints:
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- `min_women`, `max_women`
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- `min_men`, `max_men`
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- `min_people`, `max_people`
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- `cast` or `requires`: `women_only`, `men_only`, `mixed`, `has_women`,
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`has_men`, `solo`, `couple`, `threesome`, `group`
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If an exact subcategory is not compatible with `women_count` and `men_count`,
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the node raises a clear error instead of generating an impossible prompt.
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Use the `subcategory` dropdown to select either `random` or an exact
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`Main category / Subcategory` path. Exact paths override the `category` dropdown,
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which is useful because ComfyUI does not provide dependent dropdowns from Python
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alone.
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## Seed Control
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The main `seed` input is still the default master seed. Connect `SxCP Seed
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Control` to `seed_config` when you want to lock or vary specific axes.
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Seed values:
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- `-1`: follow the main seed.
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- `0` or higher: override only that axis.
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Axes:
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- `category_seed`: custom category selection when `custom_random` is used.
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- `subcategory_seed`: random subcategory selection.
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- `content_seed`: generated item content, such as outfit wording.
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- `person_seed`: appearance/person selection.
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- `scene_seed`: scene/environment selection.
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- `pose_seed`: body pose selection. For `Hardcore sexual poses`, this also drives the generated sexual pose content.
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- `role_seed`: participant choreography for `{role_graph}`. If left at `-1`, it follows `pose_seed`.
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- `expression_seed`: facial expression selection.
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- `composition_seed`: camera/composition selection.
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Example workflow: if the person and scene are right but the pose is wrong, keep
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`person_seed` and `scene_seed` fixed, then change `pose_seed`. If the cast roles
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are wrong but the act wording is good, change `role_seed`. If the clothing or
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sexual act wording is wrong, change `content_seed`; for pose-driven categories,
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change `pose_seed`.
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## Pool Extensions
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Use `pool_extensions` to add new entries to built-in pools without editing
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Python:
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```json
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{
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"pool_extensions": {
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"women_clothes": ["relaxed high-waist jeans with a fitted ribbed tank top"],
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"men_clothes": ["clean white T-shirt with relaxed jeans and canvas sneakers"],
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"poses": ["standing with a relaxed hand-on-hip pose"],
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"expressions": ["easygoing half-smile"],
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"scenes": [
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{
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"slug": "city_cafe",
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"prompt": "quiet city cafe terrace with morning light and small round tables"
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}
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]
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}
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}
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```
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Known extension pools:
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`women_clothes`, `women_clothes_minimal`, `men_clothes`, `men_clothes_minimal`,
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`couple_outfits`, `couple_outfits_minimal`, `poses`, `evocative_poses`,
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`backside_poses`, `expressions`, `compositions`, `props`, `figure_curvy`,
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`figure_athletic`, `figure_bombshell`, `scenes`, `group_scenes`,
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`layouts_full`, `layouts_minimal`, `group_compositions`, `group_ages`.
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## Templates
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A category, subcategory, or individual item can provide `prompt_template` and
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`caption_template`. Templates can use these fields:
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`{trigger}`, `{main_category}`, `{subcategory}`, `{item}`, `{item_label}`,
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`{subject}`, `{subject_phrase}`, `{age}`, `{body}`, `{body_phrase}`, `{skin}`,
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`{hair}`, `{eyes}`, `{figure}`, `{scene}`, `{pose}`, `{expression}`,
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`{composition}`, `{style}`, `{positive_suffix}`, `{negative_prompt}`,
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`{women_count}`, `{men_count}`, `{person_count}`, `{cast_summary}`,
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`{scene_kind}`, `{role_graph}`.
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If `prepend_trigger_to_prompt` is enabled, the node prepends the trigger to the
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positive prompt. Disable it for output closer to the original script's `prompt`
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field.
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