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:

{
  "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:

{
  "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:

{
  "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:

{
  "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.