BehzadandClaude Sonnet 5
cca9661dd1
Load, Apply, Facets, Reference Images, and Login nodes for loading StyleRef creative styles into ComfyUI graphs. Zero runtime dependencies. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
731 lines
29 KiB
Python
731 lines
29 KiB
Python
#!/usr/bin/env python3
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"""
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Generate the shipped workflow templates in workflows/.
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Hand-maintaining four ComfyUI workflow files means hand-maintaining node ids,
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link ids, and slot indices across all of them — the kind of bookkeeping that is
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silently wrong until someone drags the file onto a canvas. This builds them from
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a description instead, so the ids are correct by construction.
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Layout policy (plan P2-1): node positions are never guessed. Every node's height
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is estimated from its widget count, and nodes stack per column with a fixed
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vertical gap — so no template can ship with overlapping nodes at default sizes.
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A test asserts that invariant on the committed files.
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Instruction policy (plan P2-2): user-facing instructions live in visible core
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`Note` nodes on the canvas. `extra.styleref.notes` carries only a one-line
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provenance string — ComfyUI never renders it, so it must not carry anything a
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user needs to read.
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Run after changing a node's inputs or outputs:
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python scripts/build_workflows.py
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CI checks the committed files match this script's output.
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"""
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from __future__ import annotations
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import json
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import os
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import sys
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from typing import Any
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_PACK_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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if _PACK_ROOT not in sys.path:
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sys.path.insert(0, _PACK_ROOT)
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# Single-sourced from the node itself (D4: the Facets output list is data-driven
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# and long — duplicating it here would guarantee drift).
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from styleref_nodes.facets import StyleRefFacets # noqa: E402
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WORKFLOW_DIR = os.path.join(_PACK_ROOT, "workflows")
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PROVENANCE = "Generated by scripts/build_workflows.py — edit the script, not this file."
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# Slot layouts, mirroring each node's RETURN_TYPES / INPUT_TYPES order.
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OUTPUTS = {
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"StyleRefLoad": [
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("style", "STYLEREF_STYLE"),
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("search_results", "STRING"),
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],
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"StyleRefApply": [("positive", "STRING"), ("negative", "STRING"), ("attribution", "STRING")],
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"StyleRefFacets": list(zip(StyleRefFacets.RETURN_NAMES, StyleRefFacets.RETURN_TYPES)),
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"StyleRefReferenceImages": [("images", "IMAGE"), ("count", "INT")],
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"StyleRefLogin": [("status", "STRING")],
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"CheckpointLoaderSimple": [("MODEL", "MODEL"), ("CLIP", "CLIP"), ("VAE", "VAE")],
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"UNETLoader": [("MODEL", "MODEL")],
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"CLIPLoader": [("CLIP", "CLIP")],
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"VAELoader": [("VAE", "VAE")],
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"CLIPTextEncode": [("CONDITIONING", "CONDITIONING")],
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"ConditioningZeroOut": [("CONDITIONING", "CONDITIONING")],
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"EmptyLatentImage": [("LATENT", "LATENT")],
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"EmptySD3LatentImage": [("LATENT", "LATENT")],
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"KSampler": [("LATENT", "LATENT")],
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"VAEDecode": [("IMAGE", "IMAGE")],
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"LoadImage": [("IMAGE", "IMAGE"), ("MASK", "MASK")],
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"SaveImage": [],
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"PreviewImage": [],
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"PreviewAny": [],
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"Note": [],
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}
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INPUTS = {
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"StyleRefApply": [("style", "STYLEREF_STYLE")],
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"StyleRefFacets": [("style", "STYLEREF_STYLE")],
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"StyleRefReferenceImages": [("style", "STYLEREF_STYLE")],
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"CLIPTextEncode": [("clip", "CLIP"), ("text", "STRING")],
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"ConditioningZeroOut": [("conditioning", "CONDITIONING")],
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"KSampler": [
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("model", "MODEL"),
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("positive", "CONDITIONING"),
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("negative", "CONDITIONING"),
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("latent_image", "LATENT"),
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],
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"VAEDecode": [("samples", "LATENT"), ("vae", "VAE")],
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"SaveImage": [("images", "IMAGE")],
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"PreviewImage": [("images", "IMAGE")],
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# PreviewAny accepts anything — ComfyUI's wildcard type.
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"PreviewAny": [("source", "*")],
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}
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# ── layout constants (plan P2-1) ─────────────────────────────────────────────
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NODE_WIDTH = 320
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HEADER_H = 60 # title bar
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SLOT_H = 24 # one input/output slot row
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WIDGET_ROW_H = 28 # one single-line widget
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MULTILINE_H = 90 # one multiline text widget
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V_GAP = 48 # vertical gap between stacked nodes
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COL_W = 380 # column pitch; > NODE_WIDTH so columns never touch
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# Widgets that render multiline per node type (by widget index).
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MULTILINE_WIDGETS = {
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"StyleRefApply": {0, 2}, # subject, extra_negative
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"CLIPTextEncode": {0},
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"Note": {0},
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}
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# Node titles longer than this truncate in both frontends (plan P2-2).
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MAX_TITLE = 28
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# Frontend model-download metadata (plan P2-7): a missing checkpoint becomes a
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# guided download instead of a red error.
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# Placeholder ref for the "your own style" templates. Refs are opaque ids now
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# (no name lookup), so the templates ship an obvious placeholder the user swaps
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# for their own style's id — or fills via the node's "Search styles…" picker.
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OWN_STYLE_REF = "<your style id>"
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MODEL_SDXL = {
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"name": "sd_xl_base_1.0.safetensors",
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"url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors",
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"directory": "checkpoints",
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}
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# The sections the Facets templates surface on canvas. Facets has 24 outputs;
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# previewing all of them would be a wall of nodes, so the templates show a
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# representative six and the note points at the rest. Names are looked up in
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# the node's own RETURN_NAMES, so a schema reorder can never mis-slot them.
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FACET_SHOWCASE = [
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"colors",
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"light_shadow",
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"mood_personality",
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"surface_material",
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"shape_language",
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"guardrails",
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]
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# Z-Image Turbo (Tongyi-MAI) — an S3-DiT model with its own loader stack (a
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# UNET, a Qwen-3-4B text encoder, and a Flux-style 16-channel VAE), not a
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# single checkpoint. URLs are the Comfy-Org repacks. It compiles well from the
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# `diffusion` target: qwen handles the tag-weighted output that SDXL/SD1.5 use.
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MODEL_ZIMAGE_UNET = {
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"name": "z_image_turbo_bf16.safetensors",
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"url": "https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/diffusion_models/z_image_turbo_bf16.safetensors",
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"directory": "diffusion_models",
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}
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MODEL_ZIMAGE_CLIP = {
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"name": "qwen_3_4b.safetensors",
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"url": "https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/text_encoders/qwen_3_4b.safetensors",
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"directory": "text_encoders",
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}
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MODEL_ZIMAGE_VAE = {
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"name": "ae.safetensors",
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"url": "https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/vae/ae.safetensors",
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"directory": "vae",
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}
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MODELS_ZIMAGE = [MODEL_ZIMAGE_UNET, MODEL_ZIMAGE_CLIP, MODEL_ZIMAGE_VAE]
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def node_height(node_type: str, widgets: list[Any]) -> int:
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"""
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Estimated on-canvas height (plan P2-1): header + one row per input/output
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slot + one row per widget. The slot term matters for wide-output nodes —
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Facets alone has dozens of outputs.
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"""
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slots = max(len(INPUTS.get(node_type, [])), len(OUTPUTS.get(node_type, [])))
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multiline = MULTILINE_WIDGETS.get(node_type, set())
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rows = sum(1 for i in range(len(widgets)) if i not in multiline)
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tall = sum(1 for i in range(len(widgets)) if i in multiline)
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return HEADER_H + slots * SLOT_H + rows * WIDGET_ROW_H + tall * MULTILINE_H
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class Graph:
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"""
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Workflow builder with column layout.
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`place(node_type, col, …)` stacks nodes top-to-bottom within a column,
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spacing them by each node's estimated height plus V_GAP — overlap-free by
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construction. Columns are indexed (0, 1, 2, …) and spaced COL_W apart.
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"""
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def __init__(self) -> None:
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self.nodes: list[dict[str, Any]] = []
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self.links: list[list[Any]] = []
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self._node_id = 0
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self._link_id = 0
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self._cursor: dict[int, int] = {} # column index → next free y
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def place(
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self,
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node_type: str,
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col: int,
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widgets: list[Any] | None = None,
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title: str | None = None,
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height: int | None = None,
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) -> int:
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if title is not None and len(title) > MAX_TITLE:
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raise ValueError(f"title longer than {MAX_TITLE} chars truncates: {title!r}")
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widgets = widgets or []
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h = height if height is not None else node_height(node_type, widgets)
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x = 40 + col * COL_W
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y = self._cursor.get(col, 60)
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self._cursor[col] = y + h + V_GAP
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self._node_id += 1
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node: dict[str, Any] = {
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"id": self._node_id,
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"type": node_type,
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"pos": [x, y],
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"size": [NODE_WIDTH, h],
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"flags": {},
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"order": len(self.nodes),
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"mode": 0,
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"inputs": [
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{"name": name, "type": kind, "link": None}
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for name, kind in INPUTS.get(node_type, [])
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# `text` on CLIPTextEncode is a widget until something connects
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# to it; declaring it as an input is what lets Apply drive it.
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],
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"outputs": [
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{"name": name, "type": kind, "links": [], "slot_index": index}
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for index, (name, kind) in enumerate(OUTPUTS.get(node_type, []))
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],
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"properties": {"Node name for S&R": node_type},
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"widgets_values": widgets,
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}
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if title:
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node["title"] = title
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self.nodes.append(node)
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return self._node_id
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def note(self, col: int, text: str, title: str = "Read me") -> int:
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"""A visible core Note node — the instruction channel (plan P2-2)."""
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# Height scales with the text so long notes don't clip at default size.
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h = HEADER_H + 30 + 18 * (text.count("\n") + len(text) // 55)
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return self.place("Note", col, [text], title=title, height=h)
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def link(self, src: int, src_slot: int, dst: int, dst_slot: int) -> None:
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src_node = self._node(src)
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dst_node = self._node(dst)
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kind = src_node["outputs"][src_slot]["type"]
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self._link_id += 1
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self.links.append([self._link_id, src, src_slot, dst, dst_slot, kind])
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src_node["outputs"][src_slot]["links"].append(self._link_id)
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dst_node["inputs"][dst_slot]["link"] = self._link_id
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def _node(self, node_id: int) -> dict[str, Any]:
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return next(n for n in self.nodes if n["id"] == node_id)
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def to_json(self, models: list[dict[str, str]] | None = None) -> dict[str, Any]:
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payload: dict[str, Any] = {
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"last_node_id": self._node_id,
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"last_link_id": self._link_id,
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"nodes": self.nodes,
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"links": self.links,
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"groups": [],
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"config": {},
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"extra": {"styleref": {"notes": PROVENANCE}},
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"version": 0.4,
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}
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if models:
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payload["models"] = models
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return payload
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# Shared widget rows (single source for the values the nodes expect, in the
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# INPUT_TYPES order of each node).
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def load_widgets(style_ref: str) -> list[Any]:
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# style_ref, search, category, use_cache, save_to_library
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return [style_ref, "", "any", True, False]
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def apply_widgets(subject: str, target: str) -> list[Any]:
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# subject, target, extra_negative, compact, sections, subject_first
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# Targets mirror the web app's copy list: diffusion drives SDXL/SD1.5
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# checkpoints, flux drives FLUX ones.
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return [subject, target, "", False, "", True]
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def sampler_widgets(cfg: float) -> list[Any]:
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# Seeds randomize (plan P2-4): a fixed seed makes the second queue a silent
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# cache no-op — deadly in a demo.
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return [0, "randomize", 25, cfg, "euler", "normal", 1.0]
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def _sdxl_tail(graph: Graph, apply_node: int, checkpoint: int, col: int) -> None:
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"""Apply → two CLIP encoders → KSampler → VAEDecode → SaveImage."""
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positive = graph.place("CLIPTextEncode", col, [""], title="Positive (from StyleRef)")
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negative = graph.place("CLIPTextEncode", col, [""], title="Negative (from StyleRef)")
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latent = graph.place("EmptyLatentImage", col, [1024, 1024, 1])
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sampler = graph.place("KSampler", col + 1, sampler_widgets(cfg=7.0))
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decode = graph.place("VAEDecode", col + 2)
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save = graph.place("SaveImage", col + 2, ["StyleRef"])
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graph.link(apply_node, 0, positive, 1)
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graph.link(apply_node, 1, negative, 1)
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graph.link(checkpoint, 1, positive, 0)
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graph.link(checkpoint, 1, negative, 0)
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graph.link(checkpoint, 0, sampler, 0)
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graph.link(positive, 0, sampler, 1)
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graph.link(negative, 0, sampler, 2)
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graph.link(latent, 0, sampler, 3)
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graph.link(sampler, 0, decode, 0)
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graph.link(checkpoint, 2, decode, 1)
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graph.link(decode, 0, save, 0)
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def _facet_board(graph: Graph, load: int, col: int) -> int:
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"""
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Load → Facets, with a Preview Any on each showcased section.
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Facets outputs plain STRINGs, so the only thing a template can *show*
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without depending on third-party packs is the text itself — which is the
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point: seeing `colors` as readable lines is what tells a user they can drag
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that output into any text input downstream. Returns the Facets node id so
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callers can wire more sections if they want.
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"""
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facets = graph.place("StyleRefFacets", col, [], title="Facets — every section")
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graph.link(load, 0, facets, 0)
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for section in FACET_SHOWCASE:
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slot = StyleRefFacets.RETURN_NAMES.index(section)
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preview = graph.place(
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"PreviewAny", col + 1, [], title=f"Facet — {section.replace('_', ' ')}"
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)
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graph.link(facets, slot, preview, 0)
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return facets
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def _preview(graph: Graph, apply_node: int, col: int, suffix: str = "") -> None:
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"""
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Compiled prompts made visible (plan P2-3, extended to every template by
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owner decision): Preview Any on Apply's positive and negative kills the
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"empty text box on the encoder looks broken" perception and teaches what
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StyleRef actually produces.
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"""
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pos = graph.place("PreviewAny", col, [], title=f"Compiled positive{suffix}")
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neg = graph.place("PreviewAny", col, [], title=f"Compiled negative{suffix}")
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graph.link(apply_node, 0, pos, 0)
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graph.link(apply_node, 1, neg, 0)
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def zimage_sampler_widgets() -> list[Any]:
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"""
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Z-Image Turbo defaults: ~8 steps, CFG 1.0, euler/simple.
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CFG stays at 1.0 because Z-Image has no negative channel to activate (see
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`_zimage_tail`) — raising it only oversaturates and slows the sample down.
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"""
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return [0, "randomize", 8, 1.0, "euler", "simple", 1.0]
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def _zimage_loaders(graph: Graph) -> tuple[int, int, int]:
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"""Place the Z-Image UNET + CLIP + VAE loaders. Returns their node ids."""
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unet = graph.place(
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"UNETLoader", 0, [MODEL_ZIMAGE_UNET["name"], "default"], title="Z-Image UNET"
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)
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clip = graph.place(
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"CLIPLoader", 0, [MODEL_ZIMAGE_CLIP["name"], "lumina2", "default"], title="Qwen text encoder"
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)
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vae = graph.place("VAELoader", 0, [MODEL_ZIMAGE_VAE["name"]], title="Z-Image VAE")
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return unet, clip, vae
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def _zimage_tail(
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graph: Graph, apply_node: int, unet: int, clip: int, vae: int, col: int
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) -> None:
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"""
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Apply → one CLIP encoder → KSampler → VAEDecode → SaveImage, on the Z-Image
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loader stack.
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**No negative encoder.** Z-Image's official pipeline runs without
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classifier-free guidance — the authors state the model does not use negative
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prompts at all — so a second encoder here would be dead weight wired to a
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sampler input that cannot read it. The sampler's negative gets a zeroed
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conditioning instead, the same shape a FLUX graph uses. Apply's `negative`
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output stays connected to its Preview Any so the compiled exclusions are
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still readable and copyable; anything you need from them belongs in
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`subject` on Apply.
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"""
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positive = graph.place("CLIPTextEncode", col, [""], title="Positive (from StyleRef)")
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zero_out = graph.place("ConditioningZeroOut", col, [], title="Z-Image has no negative")
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latent = graph.place("EmptySD3LatentImage", col, [1024, 1024, 1])
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sampler = graph.place("KSampler", col + 1, zimage_sampler_widgets())
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decode = graph.place("VAEDecode", col + 2)
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save = graph.place("SaveImage", col + 2, ["StyleRef"])
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graph.link(apply_node, 0, positive, 1)
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graph.link(clip, 0, positive, 0)
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graph.link(unet, 0, sampler, 0)
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graph.link(positive, 0, sampler, 1)
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graph.link(positive, 0, zero_out, 0)
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graph.link(zero_out, 0, sampler, 2)
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graph.link(latent, 0, sampler, 3)
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graph.link(sampler, 0, decode, 0)
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graph.link(vae, 0, decode, 1)
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graph.link(decode, 0, save, 0)
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_ZIMAGE_SETUP_NOTE = (
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"Z-Image Turbo (S3-DiT). Three model files download on first use: the UNET "
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"(diffusion_models/), the Qwen text encoder (text_encoders/), and the VAE "
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"(vae/).\n"
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"Sampler defaults are turbo: 8 steps, CFG 1.0, euler/simple.\n"
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"Z-Image takes no negative prompt. The model runs without classifier-free "
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"guidance, so there is no negative encoder on this canvas — the sampler's "
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"negative input gets a zeroed conditioning. StyleRef still compiles the "
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"negative and previews it; put anything you need from it into `subject` on "
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"Apply as a positive phrase (\"fully clothed\", \"no text or watermark\"). "
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"Raising CFG does not bring the negative back.\n"
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"Style prompts use the `diffusion` target — Z-Image reads the same "
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"tag-weighted output SDXL/SD1.5 use."
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)
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def workflow_zimage_01_quickstart() -> dict[str, Any]:
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"""Z-Image quickstart: Load a gallery style → Apply → generate."""
|
|
g = Graph()
|
|
g.note(
|
|
0,
|
|
"Z-Image quickstart — no account needed.\n"
|
|
"1. Queue — the three Z-Image files download on first run.\n"
|
|
"2. Change `subject` on StyleRef Apply and queue again (seeds randomize).\n"
|
|
"Swap `style_ref` on StyleRef Load for any gallery slug.\n\n" + _ZIMAGE_SETUP_NOTE,
|
|
title="Z-Image quickstart",
|
|
)
|
|
load = g.place(
|
|
"StyleRefLoad", 0, load_widgets("9a2adtz6-cd8d77ee2f51"), title="Load — swap this slug"
|
|
)
|
|
unet, clip, vae = _zimage_loaders(g)
|
|
apply_node = g.place(
|
|
"StyleRefApply", 1, apply_widgets("a lighthouse on a rocky shore at dawn", "diffusion")
|
|
)
|
|
g.link(load, 0, apply_node, 0)
|
|
_preview(g, apply_node, 1)
|
|
_zimage_tail(g, apply_node, unet, clip, vae, col=2)
|
|
|
|
return g.to_json(models=MODELS_ZIMAGE)
|
|
|
|
|
|
def workflow_zimage_02_consistency() -> dict[str, Any]:
|
|
"""One style across three subjects on Z-Image (plan P2-5)."""
|
|
g = Graph()
|
|
g.note(
|
|
0,
|
|
"Z-Image consistency grid — no account needed.\n"
|
|
"One style feeds three Apply nodes with three subjects; each renders "
|
|
"through its own Z-Image sampler. Queue once and compare: one style, "
|
|
"many subjects, a coherent set within this model.\n\n" + _ZIMAGE_SETUP_NOTE,
|
|
title="Z-Image consistency",
|
|
)
|
|
load = g.place("StyleRefLoad", 0, load_widgets("9a2adtz6-cd8d77ee2f51"))
|
|
unet, clip, vae = _zimage_loaders(g)
|
|
|
|
subjects = [
|
|
"a lighthouse on a rocky shore at dawn",
|
|
"a quiet street cafe in the rain",
|
|
"a hot air balloon over desert dunes",
|
|
]
|
|
for index, subject in enumerate(subjects):
|
|
apply_node = g.place(
|
|
"StyleRefApply", 1, apply_widgets(subject, "diffusion"), title=f"Subject {index + 1}"
|
|
)
|
|
g.link(load, 0, apply_node, 0)
|
|
_preview(g, apply_node, 1, suffix=f" {index + 1}")
|
|
_zimage_tail(g, apply_node, unet, clip, vae, col=2)
|
|
|
|
return g.to_json(models=MODELS_ZIMAGE)
|
|
|
|
|
|
def workflow_zimage_03_your_own_style() -> dict[str, Any]:
|
|
"""Your own style, made on the web, rendered on Z-Image."""
|
|
g = Graph()
|
|
g.note(
|
|
0,
|
|
"Use your own style on Z-Image.\n"
|
|
"Extract a style from any image at https://styleref.io, then load it "
|
|
"here by its share slug or id.\n"
|
|
"1. Queue the StyleRef Login node alone first (use its Sign in button).\n"
|
|
"2. Put your style's id in `style_ref` on StyleRef Load, or pick it\n"
|
|
" with the Search styles… button.\n"
|
|
"3. Queue.\n\n" + _ZIMAGE_SETUP_NOTE,
|
|
title="Z-Image — your style",
|
|
)
|
|
login = g.place("StyleRefLogin", 0, [], title="Login — check status")
|
|
assert login
|
|
load = g.place(
|
|
"StyleRefLoad", 0, load_widgets(OWN_STYLE_REF), title="Load — your own style"
|
|
)
|
|
unet, clip, vae = _zimage_loaders(g)
|
|
apply_node = g.place(
|
|
"StyleRefApply", 1, apply_widgets("a quiet street cafe in the rain", "diffusion")
|
|
)
|
|
g.link(load, 0, apply_node, 0)
|
|
_preview(g, apply_node, 1)
|
|
_zimage_tail(g, apply_node, unet, clip, vae, col=2)
|
|
|
|
return g.to_json(models=MODELS_ZIMAGE)
|
|
|
|
|
|
def workflow_zimage_04_reference_images() -> dict[str, Any]:
|
|
"""Z-Image render + the style's inspiration images side by side."""
|
|
g = Graph()
|
|
g.note(
|
|
0,
|
|
"Z-Image — style prompt and reference images together.\n"
|
|
"StyleRef Apply drives the Z-Image sampler with the style's prompt; "
|
|
"StyleRef Reference Images pulls the style's inspiration images out as "
|
|
"a batch, previewed alongside. The prompt says how the style reads; the "
|
|
"images show how it looks — wire the images into IPAdapter or a "
|
|
"reference-only ControlNet to combine both.\n"
|
|
"Use a gallery style that has inspiration images.\n\n" + _ZIMAGE_SETUP_NOTE,
|
|
title="Z-Image + references",
|
|
)
|
|
load = g.place(
|
|
"StyleRefLoad", 0, load_widgets("9a2adtz6-cd8d77ee2f51"), title="Load — a gallery slug"
|
|
)
|
|
unet, clip, vae = _zimage_loaders(g)
|
|
|
|
refs = g.place("StyleRefReferenceImages", 1, [12], title="Reference images")
|
|
ref_preview = g.place("PreviewImage", 1, [], title="Inspiration images")
|
|
g.link(load, 0, refs, 0)
|
|
g.link(refs, 0, ref_preview, 0)
|
|
|
|
apply_node = g.place(
|
|
"StyleRefApply", 1, apply_widgets("a lighthouse on a rocky shore at dawn", "diffusion")
|
|
)
|
|
g.link(load, 0, apply_node, 0)
|
|
_preview(g, apply_node, 1)
|
|
_zimage_tail(g, apply_node, unet, clip, vae, col=2)
|
|
|
|
return g.to_json(models=MODELS_ZIMAGE)
|
|
|
|
|
|
def workflow_01_quickstart() -> dict[str, Any]:
|
|
"""Load a gallery style → Apply → generate. The 60-second demo."""
|
|
g = Graph()
|
|
g.note(
|
|
0,
|
|
"Quickstart — no account needed.\n"
|
|
"1. Set your SDXL checkpoint below (a missing model offers a download).\n"
|
|
"2. Queue. Seeds randomize, so every queue is a fresh image.\n"
|
|
"3. Change `subject` on StyleRef Apply and queue again.\n"
|
|
"Swap `style_ref` on StyleRef Load for any slug from "
|
|
"https://styleref.io/gallery — or use the Search styles… button.",
|
|
title="Quickstart",
|
|
)
|
|
load = g.place(
|
|
"StyleRefLoad",
|
|
0,
|
|
load_widgets("9a2adtz6-cd8d77ee2f51"),
|
|
title="Load — swap this slug",
|
|
)
|
|
checkpoint = g.place("CheckpointLoaderSimple", 0, [MODEL_SDXL["name"]])
|
|
apply_node = g.place(
|
|
"StyleRefApply", 1, apply_widgets("a lighthouse on a rocky shore at dawn", "diffusion")
|
|
)
|
|
_preview(g, apply_node, 1)
|
|
g.link(load, 0, apply_node, 0)
|
|
_sdxl_tail(g, apply_node, checkpoint, col=2)
|
|
|
|
return g.to_json(models=[MODEL_SDXL])
|
|
|
|
|
|
def workflow_02_consistency() -> dict[str, Any]:
|
|
"""
|
|
One style across three subjects, side by side (plan P2-5).
|
|
|
|
Consistency is the claim this demonstrates, and it is scoped precisely: the
|
|
same style applied within this one model produces a coherent set. It is not
|
|
a before/after, and not a claim that other tools match this output.
|
|
"""
|
|
g = Graph()
|
|
g.note(
|
|
0,
|
|
"Consistency grid — no account needed.\n"
|
|
"One style feeds three Apply nodes with three different subjects; each "
|
|
"renders through its own sampler. Queue once and compare: one style, "
|
|
"many subjects, a coherent set within this model.\n"
|
|
"Change any subject, or swap the style on StyleRef Load, and queue again.",
|
|
title="Consistency grid",
|
|
)
|
|
load = g.place("StyleRefLoad", 0, load_widgets("9a2adtz6-cd8d77ee2f51"))
|
|
checkpoint = g.place("CheckpointLoaderSimple", 0, [MODEL_SDXL["name"]])
|
|
|
|
subjects = [
|
|
"a lighthouse on a rocky shore at dawn",
|
|
"a quiet street cafe in the rain",
|
|
"a hot air balloon over desert dunes",
|
|
]
|
|
for index, subject in enumerate(subjects):
|
|
apply_node = g.place(
|
|
"StyleRefApply", 1, apply_widgets(subject, "diffusion"), title=f"Subject {index + 1}"
|
|
)
|
|
g.link(load, 0, apply_node, 0)
|
|
_preview(g, apply_node, 1, suffix=f" {index + 1}")
|
|
_sdxl_tail(g, apply_node, checkpoint, col=2)
|
|
|
|
return g.to_json(models=[MODEL_SDXL])
|
|
|
|
|
|
def workflow_03_your_own_style() -> dict[str, Any]:
|
|
"""Your own style, made on the web, loaded here (plan P0-6's template 03)."""
|
|
g = Graph()
|
|
g.note(
|
|
0,
|
|
"Use your own style.\n"
|
|
"Extract a style from any image at https://styleref.io — it appears in "
|
|
"your library — then load it here by its share slug or id.\n"
|
|
"1. Queue the StyleRef Login node alone first (Queue runs every node, "
|
|
"so an empty workflow is the clean way). If status says signed out, "
|
|
"set action to `sign in` and queue again.\n"
|
|
"2. Put your style's id in `style_ref` on StyleRef Load, or pick it\n"
|
|
" with the Search styles… button.\n"
|
|
"3. Set your SDXL checkpoint and queue.",
|
|
title="Your own style",
|
|
)
|
|
login = g.place("StyleRefLogin", 0, [], title="Login — check status")
|
|
assert login # not linked: it is a status helper, not a data node
|
|
load = g.place(
|
|
"StyleRefLoad",
|
|
0,
|
|
load_widgets(OWN_STYLE_REF),
|
|
title="Load — your own style",
|
|
)
|
|
checkpoint = g.place("CheckpointLoaderSimple", 0, [MODEL_SDXL["name"]])
|
|
apply_node = g.place(
|
|
"StyleRefApply", 1, apply_widgets("a quiet street cafe in the rain", "diffusion")
|
|
)
|
|
g.link(load, 0, apply_node, 0)
|
|
_preview(g, apply_node, 1)
|
|
_sdxl_tail(g, apply_node, checkpoint, col=2)
|
|
|
|
return g.to_json(models=[MODEL_SDXL])
|
|
|
|
|
|
_FACETS_NOTE = (
|
|
"Inside the style — the Facets node.\n"
|
|
"Apply gives you one prompt string. Facets gives you the style's sections "
|
|
"one output at a time — 24 of them, exactly the sections you see on the "
|
|
"style board at styleref.io, plus the six custom items.\n"
|
|
"Six are previewed here so you can read them. Every other output is on the "
|
|
"node too; drag any of them into a text input.\n"
|
|
"They are plain STRINGs, so they go anywhere text goes: a second CLIP "
|
|
"encoder for regional conditioning, an IPAdapter or ControlNet prompt, a "
|
|
"LoRA trigger line, or your own nodes. That is the escape hatch — use the "
|
|
"palette without taking the whole prompt.\n"
|
|
"The render alongside is the paved road (Apply), so you can compare the "
|
|
"assembled prompt against the parts it was built from."
|
|
)
|
|
|
|
|
|
def workflow_04_facets() -> dict[str, Any]:
|
|
"""Every section of a style, exposed and readable, next to a normal render."""
|
|
g = Graph()
|
|
g.note(0, _FACETS_NOTE, title="Facets")
|
|
load = g.place(
|
|
"StyleRefLoad",
|
|
0,
|
|
load_widgets("9a2adtz6-cd8d77ee2f51"),
|
|
title="Load — swap this slug",
|
|
)
|
|
checkpoint = g.place("CheckpointLoaderSimple", 0, [MODEL_SDXL["name"]])
|
|
|
|
_facet_board(g, load, col=1)
|
|
|
|
apply_node = g.place(
|
|
"StyleRefApply", 3, apply_widgets("a lighthouse on a rocky shore at dawn", "diffusion")
|
|
)
|
|
g.link(load, 0, apply_node, 0)
|
|
_preview(g, apply_node, 3)
|
|
_sdxl_tail(g, apply_node, checkpoint, col=4)
|
|
|
|
return g.to_json(models=[MODEL_SDXL])
|
|
|
|
|
|
def workflow_zimage_05_facets() -> dict[str, Any]:
|
|
"""The Facets story on the Z-Image loader stack."""
|
|
g = Graph()
|
|
g.note(0, _FACETS_NOTE + "\n\n" + _ZIMAGE_SETUP_NOTE, title="Facets on Z-Image")
|
|
load = g.place(
|
|
"StyleRefLoad",
|
|
0,
|
|
load_widgets("9a2adtz6-cd8d77ee2f51"),
|
|
title="Load — swap this slug",
|
|
)
|
|
unet, clip, vae = _zimage_loaders(g)
|
|
|
|
_facet_board(g, load, col=1)
|
|
|
|
apply_node = g.place(
|
|
"StyleRefApply", 3, apply_widgets("a lighthouse on a rocky shore at dawn", "diffusion")
|
|
)
|
|
g.link(load, 0, apply_node, 0)
|
|
_preview(g, apply_node, 3)
|
|
_zimage_tail(g, apply_node, unet, clip, vae, col=4)
|
|
|
|
return g.to_json(models=MODELS_ZIMAGE)
|
|
|
|
|
|
# Keyed by path relative to workflows/. The SDXL set lives at the root; the
|
|
# Z-Image set lives in workflows/z-image/.
|
|
WORKFLOWS = {
|
|
"01-quickstart.json": workflow_01_quickstart,
|
|
"02-consistency-grid.json": workflow_02_consistency,
|
|
"03-your-own-style.json": workflow_03_your_own_style,
|
|
"04-facets.json": workflow_04_facets,
|
|
"z-image/01-quickstart.json": workflow_zimage_01_quickstart,
|
|
"z-image/02-consistency-grid.json": workflow_zimage_02_consistency,
|
|
"z-image/03-your-own-style.json": workflow_zimage_03_your_own_style,
|
|
"z-image/04-reference-images.json": workflow_zimage_04_reference_images,
|
|
"z-image/05-facets.json": workflow_zimage_05_facets,
|
|
}
|
|
|
|
|
|
def main() -> None:
|
|
for relpath, builder in WORKFLOWS.items():
|
|
path = os.path.join(WORKFLOW_DIR, relpath)
|
|
os.makedirs(os.path.dirname(path), exist_ok=True)
|
|
with open(path, "w", encoding="utf-8") as fh:
|
|
json.dump(builder(), fh, indent=2)
|
|
fh.write("\n")
|
|
print(f"wrote {path}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|