ComfyUI-Ethanfel-Prompt-Builder/README.md

# ComfyUI Prompt Builder

Custom ComfyUI node for building prompts from the existing `generate_prompt_batches.py`
generator without writing image batches.

The node is registered as:

- `prompt_builder / SxCP Prompt Builder`
- `prompt_builder / SxCP Seed Control`
- `prompt_builder / SxCP Camera Control`
- `prompt_builder / SxCP Caption Naturalizer`
- `prompt_builder / SxCP Insta/OF Options`
- `prompt_builder / SxCP Insta/OF Prompt Pair`

It outputs:

- `prompt`
- `negative_prompt`
- `caption`
- `metadata_json`
- `category`
- `subcategory`

`SxCP Seed Control` outputs `seed_config`, which can be connected to the prompt
builder's optional `seed_config` input.

`SxCP Camera Control` outputs `camera_config`, which can be connected to the
prompt builder or the Insta/OF pair node. It makes camera/framing first-class
instead of relying on a weak phrase inside the prompt.

Camera controls:

- `camera_mode`: `standard`, `handheld_selfie`, `mirror_selfie`,
  `phone_tripod`, `creator_pov`, `bed_selfie`, `bathroom_mirror`,
  `phone_flash`, or `action_cam`.
- `shot_size`: `auto`, `full_body`, `three_quarter`, `waist_up`, `close_up`,
  or `extreme_close_up`.
- `angle`: `auto`, `eye_level`, `high_angle`, `low_angle`, `overhead`,
  `side_profile`, `rear_view`, or `mirror_reflection`.
- `lens`: `auto`, `smartphone_wide`, `ultra_wide`, `portrait_lens`,
  `telephoto`, or `macro_detail`.
- `distance`: `auto`, `arm_length`, `near_body`, `bedside`, or `room_corner`.
- `orientation`: `auto`, `vertical_story`, `square_feed`, or `horizontal`.
- `phone_visibility`: `auto`, `phone_visible`, `phone_hidden`,
  `screen_reflection`, or `ring_light_visible`.
- `priority`: `soft_hint`, `strong`, or `locked`.

`SxCP Caption Naturalizer` rewrites tag-like captions or labeled prompts into
more natural language. Connect the prompt builder's `metadata_json` output to
`source_text` for the cleanest result. You can also connect `caption` or
`prompt`; in that case the node falls back to prompt-label parsing or comma-tag
cleanup.

Naturalizer controls:

- `input_hint`: `auto`, `metadata_json`, or `caption_or_prompt`.
- `detail_level`: `concise`, `balanced`, or `dense`.
- `style_policy`: `drop_style_tail` removes old fixed style tails; `keep_style_terms`
  keeps style descriptions in the rewritten text.
- `trigger`: defaults to `sxcppnl7`.
- `include_trigger`: prepends the trigger as its own sentence.

It outputs:

- `natural_caption`
- `method`

`SxCP Insta/OF Prompt Pair` is a special paired-output mode. It creates one
shared primary creator descriptor, then returns both a softcore prompt and a
hardcore prompt from that same descriptor. This is useful when you want the same
person/look/scene continuity but need two different prompt strengths.

It outputs:

- `softcore_prompt`
- `hardcore_prompt`
- `softcore_negative_prompt`
- `hardcore_negative_prompt`
- `softcore_caption`
- `hardcore_caption`
- `shared_descriptor`
- `metadata_json`

`SxCP Insta/OF Options` outputs `options_json`, which can be connected to the
pair node. Defaults are set so the softcore prompt is solo while the hardcore
prompt can include partners. It also defaults the camera to handheld selfie
framing. For stronger camera control, connect `SxCP Camera Control` to the pair
node's optional `camera_config` input.

Options:

- `softcore_cast`: `solo` or `same_as_hardcore`.
- `hardcore_cast`: `use_counts`, `couple`, `threesome`, or `group`.
- `hardcore_women_count` and `hardcore_men_count`: used when `hardcore_cast` is
  `use_counts`. The pair mode always keeps at least one adult woman as the
  primary creator so the shared descriptor remains valid.
- `softcore_level`: `social_tease`, `lingerie_tease`, `implied_nude`, or
  `explicit_tease`.
- `hardcore_level`: `explicit` or `hardcore`.
- `platform_style`: `hybrid`, `instagram`, or `onlyfans`.
- `continuity`: `same_creator_same_room` keeps the scene/composition aligned;
  `same_creator_new_scene` keeps the same creator descriptor but lets the
  hardcore scene use its own setting.
- `softcore_camera_mode`: base camera mode for the softcore output.
- `hardcore_camera_mode`: `same_as_softcore` or a separate base camera mode for
  the hardcore output.

## Built-In Categories

The node keeps the original generator controls:

- `category`: `auto_weighted`, `woman`, `man`, `couple`, `group_or_layout`, or a custom JSON category.
- `clothing`: `full` or `minimal`.
- `minimal_clothing_ratio`: `-1` disables mixing; `0.0` to `1.0` mixes minimal/full clothing.
- `ethnicity`: `any`, `asian`, `white_asian`.
- `poses`: `standard` or `evocative`.
- `standard_pose_ratio`: `-1` disables mixing; `0.0` to `1.0` mixes standard/evocative poses.
- `backside_bias`: `0.0` to `1.0`, applies to evocative single-subject poses.
- `figure`: `curvy`, `balanced`, `bombshell`.
- `no_plus_women`: excludes plus-size women.
- `no_black`: excludes Black/African-coded women from women-focused pools.
- Optional `camera_config`: connect `SxCP Camera Control` to force selfie,
  phone, lens, angle, distance, crop, and camera-priority behavior. This applies
  to custom categories too, including `Hardcore sexual poses`.

`auto_weighted` uses the original batch mix: mostly women, then men, couples, and
group/layout rows. Direct categories generate only that selected category.

## Custom Categories

Add or edit JSON files in `categories/*.json`. Each file can define new main
categories, subcategories, and optional extensions to the built-in pools. Restart
or reload ComfyUI after changing JSON so dropdown choices are rebuilt.

Included JSON categories:

- `Casual clothes`
- `Provocative erotic clothes`
- `Hardcore sexual poses`

Example:

```json
{
  "version": 1,
  "categories": [
    {
      "name": "Casual clothes",
      "slug": "casual_clothes",
      "subject_type": "woman",
      "item_label": "Clothing",
      "style": "tasteful adult fashion-editorial coloured-pencil comic illustration",
      "subcategories": [
        {
          "name": "Streetwear",
          "slug": "streetwear",
          "items": [
            "oversized hoodie with slim jeans and clean sneakers",
            "cropped bomber jacket with cargo pants and chunky trainers"
          ],
          "scenes": [
            {
              "slug": "city_crosswalk",
              "prompt": "sunlit city crosswalk with storefront reflections"
            }
          ],
          "poses": [
            "standing with one hand in a pocket",
            "walking forward with a casual runway stride"
          ]
        }
      ]
    }
  ]
}
```

Custom categories do not need a Python generator. If no `prompt_template` is
provided, the node uses a generic composer that selects subject appearance,
scene, pose, expression, composition, and a random item from the selected
subcategory.

For large categories, prefer `item_templates` plus `item_axes` instead of writing
every final item by hand:

```json
{
  "name": "Example clothes",
  "subject_type": "woman",
  "subcategories": [
    {
      "name": "Lingerie",
      "item_templates": [
        "{color} {fabric} {top} with {bottom} and {stockings}"
      ],
      "item_axes": {
        "color": ["black", "red", "ivory"],
        "fabric": ["lace", "satin", "transparent mesh"],
        "top": ["balconette bra", "open-cup bra"],
        "bottom": ["matching thong", "high-cut g-string"],
        "stockings": ["thigh-high stockings", "fishnet stockings"]
      }
    }
  ]
}
```

The node chooses one template, fills each placeholder from the matching axis,
then records the selected axis values in `metadata_json`.

Supported `subject_type` values:

- `single_any`
- `woman`
- `man`
- `couple`
- `group`
- `layout`
- `scene`
- `configured_cast`

`configured_cast` uses the node's `women_count` and `men_count` inputs. It adds
template fields for `{women_count}`, `{men_count}`, `{person_count}`,
`{cast_summary}`, `{scene_kind}`, and `{role_graph}`. This is intended for
categories where the same prompt generator should support couples, threesomes,
and larger groups.

`{role_graph}` is a generated choreography sentence that assigns roles to the
cast, such as who penetrates, who receives oral, and who joins from the side.
It is currently most useful for `Hardcore sexual poses`.

Subcategories, templates, and axis values can declare cast constraints:

```json
{
  "name": "Threesomes",
  "min_people": 3,
  "item_axes": {
    "act": [
      {
        "text": "strap-on penetration and cunnilingus",
        "cast": "women_only"
      },
      {
        "text": "male/male oral and anal contact",
        "cast": "men_only"
      },
      {
        "text": "front-and-back penetration",
        "cast": "mixed"
      }
    ]
  }
}
```

Supported constraints:

- `min_women`, `max_women`
- `min_men`, `max_men`
- `min_people`, `max_people`
- `cast` or `requires`: `women_only`, `men_only`, `mixed`, `has_women`,
  `has_men`, `solo`, `couple`, `threesome`, `group`

If an exact subcategory is not compatible with `women_count` and `men_count`,
the node raises a clear error instead of generating an impossible prompt.

Use the `subcategory` dropdown to select either `random` or an exact
`Main category / Subcategory` path. Exact paths override the `category` dropdown,
which is useful because ComfyUI does not provide dependent dropdowns from Python
alone.

## Seed Control

The main `seed` input is still the default master seed. Connect `SxCP Seed
Control` to `seed_config` when you want to lock or vary specific axes.

Seed values:

- `-1`: follow the main seed.
- `0` or higher: override only that axis.

Axes:

- `category_seed`: custom category selection when `custom_random` is used.
- `subcategory_seed`: random subcategory selection.
- `content_seed`: generated item content, such as outfit wording.
- `person_seed`: appearance/person selection.
- `scene_seed`: scene/environment selection.
- `pose_seed`: body pose selection. For `Hardcore sexual poses`, this also drives the generated sexual pose content.
- `role_seed`: participant choreography for `{role_graph}`. If left at `-1`, it follows `pose_seed`.
- `expression_seed`: facial expression selection.
- `composition_seed`: camera/composition selection.

Example workflow: if the person and scene are right but the pose is wrong, keep
`person_seed` and `scene_seed` fixed, then change `pose_seed`. If the cast roles
are wrong but the act wording is good, change `role_seed`. If the clothing or
sexual act wording is wrong, change `content_seed`; for pose-driven categories,
change `pose_seed`.

## Pool Extensions

Use `pool_extensions` to add new entries to built-in pools without editing
Python:

```json
{
  "pool_extensions": {
    "women_clothes": ["relaxed high-waist jeans with a fitted ribbed tank top"],
    "men_clothes": ["clean white T-shirt with relaxed jeans and canvas sneakers"],
    "poses": ["standing with a relaxed hand-on-hip pose"],
    "expressions": ["easygoing half-smile"],
    "scenes": [
      {
        "slug": "city_cafe",
        "prompt": "quiet city cafe terrace with morning light and small round tables"
      }
    ]
  }
}
```

Known extension pools:

`women_clothes`, `women_clothes_minimal`, `men_clothes`, `men_clothes_minimal`,
`couple_outfits`, `couple_outfits_minimal`, `poses`, `evocative_poses`,
`backside_poses`, `expressions`, `compositions`, `props`, `figure_curvy`,
`figure_athletic`, `figure_bombshell`, `scenes`, `group_scenes`,
`layouts_full`, `layouts_minimal`, `group_compositions`, `group_ages`.

## Templates

A category, subcategory, or individual item can provide `prompt_template` and
`caption_template`. Templates can use these fields:

`{trigger}`, `{main_category}`, `{subcategory}`, `{item}`, `{item_label}`,
`{subject}`, `{subject_phrase}`, `{age}`, `{body}`, `{body_phrase}`, `{skin}`,
`{hair}`, `{eyes}`, `{figure}`, `{scene}`, `{pose}`, `{expression}`,
`{composition}`, `{style}`, `{positive_suffix}`, `{negative_prompt}`,
`{women_count}`, `{men_count}`, `{person_count}`, `{cast_summary}`,
`{scene_kind}`, `{role_graph}`.

If `prepend_trigger_to_prompt` is enabled, the node prepends the trigger to the
positive prompt. Disable it for output closer to the original script's `prompt`
field.