Eric HissandClaude Fable 5.1
4459af9ea6
Every node id, input name and existing widget position is unchanged, so saved
workflows load; new inputs are appended only.
Wildcards
- core/wildcards: Dynamic Prompts / Impact Pack grammar, tag queries, variables,
conditionals, concept / verbatim / loose bindings, reproducible seeds.
Wired into every enhancer node; reads ComfyUI/models/wildcards.
- New nodes: Wildcard Processor, Semantic Variation (direction axes),
Concept Survival Check. Starter lists in wildcards/.
- Random choices use a private generator; global random state is untouched.
Platform profiles
- core/profiles + profiles/*.yaml: one file per model (format, length,
negative prompt or none, quality tokens, text and dialogue syntax, vendor
rewriter prompt fetched once and cached). profiles_user/ overrides by id.
- New node: Platform Prompt Rewriter (prose, tags, edit instructions, JSON,
MiniMax H3 fields, Hailuo brackets), with validators for each format.
- Text-to-Image and Image-to-Image read profiles through platforms.py;
corrected facts for FLUX, Kontext, Chroma, Wan, AuraFlow, Kolors, NoobAI,
Qwen-Image, HunyuanImage. Negative prompt is empty for models without one.
- Hosted / API models: Seedream 5, Nano Banana, GPT Image, Midjourney V8,
FLUX.2 pro, Ideogram 3, Recraft V4, Seedance 2.0 / 2.5, Kling 3.0, Veo 3.1,
Gemini Omni Flash, Runway, Wan 3.0, Luma, hosted H3, Grok, Vidu, PixVerse.
Reports say how to switch the vendor's own prompt rewriting off.
- Illustrious: spaced tags, quality tags last, 25-80 words (OnomaAI paper).
Video nodes
- target_model and duration_seconds appended to the three video expanders.
Default target is MiniMax H3; wan22_video is the original path, unchanged.
A workflow saved earlier loads with the default until the input is set.
Also included: the Alice, Ideogram Magic Prompt and prompt-database nodes that
were already registered in __init__.py but had not been committed.
Fixes: v1 {animal} / {label:a|b} wildcards were inert in the video nodes;
Image-to-Video save_to_file raised NameError; T2I padded short-prompt models.
.gitattributes added (text=auto) so CRLF working files stop showing as
whole-file changes. prompt_database.db and the cached Ideogram prompt are
git-ignored.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019VpCi6nw5UECMdZ7ayYJvN
222 lines
12 KiB
Python
222 lines
12 KiB
Python
"""
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Platform Prompt Rewriter.
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One idea in, one prompt out, written the way the chosen model wants to read it. Everything the
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node knows about a model comes from a YAML profile in profiles/ (see core/profiles/schema.py):
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format (prose, tags, edit instruction, JSON, MiniMax H3 fields, Hailuo bracket commands), target
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length and hard limits, whether a negative prompt exists, whether quality tokens help or hurt,
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how rendered text and dialogue are written, and - where the vendor publishes one - the vendor's
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own rewriter prompt, downloaded once and cached.
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The node is thin on purpose: resolve wildcards, build the instructions from the profile, call
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the LLM through the package's LLMBackend, check the answer, report. If the LLM cannot be reached
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the idea passes through unchanged and the report says why.
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Author: Eric Hiss (GitHub: EricRollei)
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License: Dual License (Non-Commercial and Commercial Use). See LICENSE.
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"""
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from .core.profiles import RunFacts
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from .profile_support import (
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OFFICIAL_PROMPT_CHOICES,
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TASK_CHOICES,
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caption_image_tensor,
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get_profile,
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profile_ids,
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rewrite_for_platform,
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)
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from .wildcard_support import (
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enforce_verbatim,
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is_changed_token,
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llm_block,
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normalise_seed,
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report_lines,
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resolve_wildcards,
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wildcard_optional_inputs,
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)
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DEFAULT_PLATFORM = "krea2"
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def _platform_choices():
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choices = profile_ids()
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return choices or [DEFAULT_PLATFORM]
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class PlatformPromptRewriter:
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CATEGORY = "Eric Prompt Enhancers"
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FUNCTION = "rewrite"
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RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING")
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RETURN_NAMES = ("prompt", "negative_prompt", "report", "system_prompt")
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@classmethod
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def INPUT_TYPES(cls):
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choices = _platform_choices()
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default_platform = DEFAULT_PLATFORM if DEFAULT_PLATFORM in choices else choices[0]
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return {
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"required": {
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"idea": ("STRING", {
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"multiline": True, "default": "",
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"tooltip": "What you want, in your own words. Wildcards work: __name__, {a|b|c}, __objects[metal AND handheld]__.",
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}),
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"platform": (choices, {
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"default": default_platform,
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"tooltip": (
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"Target model. Listed as image models, then edit models, then video models.\n"
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"Each entry is a YAML file in the package's profiles/ folder; put your own or edited\n"
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"copies in profiles_user/ (same id = replaces the shipped one)."
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),
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}),
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"task": (TASK_CHOICES, {
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"default": "auto",
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"tooltip": (
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"auto = the model's main task.\n"
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"t2i text-to-image, edit = edit instruction, t2v text-to-video,\n"
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"i2v image-to-video, flf2v first+last frame, ref2v reference-to-video.\n"
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"A task the model does not have falls back to its main one (the report says so)."
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),
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}),
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"backend": (["lm_studio", "ollama", "qwen3_vl"], {"default": "lm_studio"}),
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"endpoint": ("STRING", {
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"default": "http://localhost:1234/v1",
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"tooltip": "LM Studio / Ollama base URL. For qwen3_vl, optionally a local model path.",
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}),
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"model": ("STRING", {"default": "", "tooltip": "Blank = whatever model is loaded."}),
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"temperature": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 2.0, "step": 0.05}),
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"max_tokens": ("INT", {
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"default": 4096, "min": 256, "max": 32768,
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"tooltip": "Room for the LLM's answer. Reasoning models and MiniMax H3 prompts need the most.",
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}),
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"official_prompt": (OFFICIAL_PROMPT_CHOICES, {
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"default": "auto",
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"tooltip": (
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"Several labs publish the system prompt of their own prompt rewriter.\n"
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"auto = use it when the profile has one and it is not huge; downloaded once, then cached\n"
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"on = use it whatever its size (Qwen-Image's is ~35,000 characters)\n"
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"off = always use the rules written in the profile (works offline)\n"
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"refresh = download it again, then behave like on"
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),
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}),
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},
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"optional": {
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"image_description": ("STRING", {
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"forceInput": True,
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"tooltip": "For i2v / edit: what the start or source image shows, from your own captioner. Used instead of captioning `image`.",
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}),
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"end_image_description": ("STRING", {
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"forceInput": True,
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"tooltip": "For flf2v: what the last frame shows. Used instead of captioning `end_image`.",
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}),
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"dialogue": ("STRING", {
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"multiline": True, "default": "",
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"tooltip": "Exact spoken lines for models with sound, one per line, e.g. Woman: I get off at the next station.\nUsed word for word. Empty = the rewriter invents speech only if your idea involves someone speaking.",
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}),
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"duration_seconds": ("FLOAT", {
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"default": 0.0, "min": 0.0, "max": 60.0, "step": 0.5,
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"tooltip": "Clip length for video models. 0 = not stated. The rewriter fits the action to it.",
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}),
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"aspect_ratio": ("STRING", {"default": "", "tooltip": "Optional, e.g. 16:9. The rewriter composes for it."}),
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"audio": (["auto", "off"], {
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"default": "auto",
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"tooltip": "auto = write sound, music and dialogue when the model generates audio. off = no sound design.",
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}),
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"language": (["auto", "en", "zh"], {
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"default": "auto",
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"tooltip": "Language of the prompt. auto = the model's first language (English for every shipped profile).",
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}),
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"extra_instructions": ("STRING", {
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"multiline": True, "default": "",
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"tooltip": "Direction for the rewriter for this run, e.g. 'keep it under 60 words', 'night exterior, handheld'.",
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}),
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"negative_keywords": ("STRING", {
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"default": "",
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"tooltip": "Comma-separated additions to the negative prompt. Passed through even for models that ignore negatives.",
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}),
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**wildcard_optional_inputs(),
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# Appended later. Sockets only (no widgets), but still added at the end: the rule is append-only.
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"image": ("IMAGE", {
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"tooltip": (
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"For i2v / edit / flf2v: the start or source image. It is captioned through the backend above,\n"
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"which therefore needs a vision model loaded. A connected image_description wins over this."
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),
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}),
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"end_image": ("IMAGE", {"tooltip": "For flf2v: the last frame. Captioned like `image`."}),
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},
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}
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@classmethod
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def IS_CHANGED(cls, wildcard_seed=-1, **kwargs):
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"""wildcard_seed = -1 means a fresh roll each run, which must defeat ComfyUI's cache."""
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return is_changed_token(wildcard_seed)
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def rewrite(self, idea, platform, task, backend, endpoint, model, temperature, max_tokens, official_prompt,
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image=None, end_image=None, image_description="", end_image_description="", dialogue="",
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duration_seconds=0.0, aspect_ratio="", audio="auto", language="auto", extra_instructions="",
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negative_keywords="", wildcard_seed=-1, wildcard_llm_block=True, wildcard_instructions=""):
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resolution = resolve_wildcards(idea or "", seed=normalise_seed(wildcard_seed), legacy=True,
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upstream_instructions=wildcard_instructions)
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resolved_idea = resolution.text
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if not resolved_idea.strip():
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return ("", "", "Empty idea - nothing to rewrite.", "")
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# Images: a description wired in by the user wins; otherwise caption through the same backend.
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profile_for_task = get_profile(platform)
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caption_notes = []
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has_start = image is not None or bool((image_description or "").strip())
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has_end = end_image is not None or bool((end_image_description or "").strip())
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if task == "auto" and profile_for_task is not None and has_start:
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# An image on the input says what the job is: first+last frame, image-to-video, or an edit.
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for candidate, wanted in (("flf2v", has_end), ("i2v", True), ("edit", True)):
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if wanted and candidate in profile_for_task.tasks:
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task = candidate
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caption_notes.append(f"task auto -> {candidate}, because an image is connected")
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break
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resolved_task = profile_for_task.resolve_task(None if task == "auto" else task) if profile_for_task else task
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if image is not None and not (image_description or "").strip():
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purpose = "edit" if resolved_task == "edit" else "start"
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image_description, note = caption_image_tensor(image, purpose, backend, endpoint, model)
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caption_notes.append(note)
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if end_image is not None and not (end_image_description or "").strip():
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end_image_description, note = caption_image_tensor(end_image, "end", backend, endpoint, model)
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caption_notes.append(note)
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facts = RunFacts(
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duration_seconds=float(duration_seconds or 0.0),
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aspect_ratio=(aspect_ratio or "").strip(),
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image_description=image_description or "",
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end_image_description=end_image_description or "",
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dialogue=dialogue or "",
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audio=audio,
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language="" if language == "auto" else language,
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extra_instructions=extra_instructions or "",
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)
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custom_negatives = [part.strip() for part in (negative_keywords or "").split(",") if part.strip()]
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result = rewrite_for_platform(
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resolved_idea, platform, task=task, backend=backend, endpoint=endpoint, model=model,
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temperature=temperature, max_tokens=max_tokens, facts=facts, official_mode=official_prompt,
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extra_system=llm_block(resolution, wildcard_llm_block), custom_negatives=custom_negatives,
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)
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result.notes.extend(caption_notes)
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profile = profile_for_task
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prompt = result.prompt
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if result.ok:
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structured = profile is not None and profile.caption_style in ("json", "fields")
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repaired, missing = enforce_verbatim(prompt, resolution)
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if missing and structured:
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# Appending text to JSON or to named fields would break the format, so only report it.
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result.warnings.append("verbatim wildcard picks missing from the answer (not added, to keep the format valid): " + ", ".join(missing))
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elif missing:
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prompt = repaired
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result.notes.append("verbatim wildcard picks the LLM dropped were added back: " + ", ".join(missing))
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report = result.report(profile)
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wildcard_report = report_lines(resolution)
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if wildcard_report:
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report = f"{report}\n{wildcard_report}"
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print(f"[Platform Prompt Rewriter] {platform}: {'ok' if result.ok else 'FAILED'} ({result.base or 'no plan'})")
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return (prompt, result.negative, report, result.system_prompt)
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