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>
128 lines
4.6 KiB
Python
128 lines
4.6 KiB
Python
"""
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StyleRef Reference Images — the style's inspiration images as an IMAGE batch.
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Wire `style` from StyleRef Load, and every inspiration image stored with the
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style comes out as one IMAGE output — straight into IPAdapter, ControlNet
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reference, or a Preview Image node. This composes with ComfyUI's
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image-conditioning ecosystem instead of competing with it: the prompt from
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Apply says what the style *reads* like, the images here show what it *looks*
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like.
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The images are deliberately NOT in Apply's positive prompt — a sampler cannot
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fetch a URL out of prompt text, so there they would only be junk tokens.
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PIL, numpy and torch are imported lazily inside the function: every ComfyUI
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install ships all three, but the pack's import (and the rest of the nodes)
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must not depend on them.
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"""
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from __future__ import annotations
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import io
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from typing import Any
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import styleref_api as api
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from styleref_api import StyleRefError
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from styleref_style import inspiration_image_urls, is_style
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CATEGORY = "StyleRef"
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# Downloaded image bytes for the session, keyed by URL — inspiration images are
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# immutable uploads, so there is no staleness to manage.
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_IMAGE_CACHE: dict[str, bytes] = {}
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MAX_IMAGES = 12
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def _download(url: str) -> bytes:
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if url in _IMAGE_CACHE:
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return _IMAGE_CACHE[url]
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# anonymous=True: these are public storage URLs, and a bearer token must
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# never be sent to a host we don't control.
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raw, _headers = api.request_raw(url, anonymous=True, timeout=60)
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_IMAGE_CACHE[url] = raw
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return raw
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class StyleRefReferenceImages:
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"""Load a style's inspiration images as a ComfyUI IMAGE batch."""
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@classmethod
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def INPUT_TYPES(cls) -> dict[str, Any]:
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return {
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"required": {
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"style": ("STYLEREF_STYLE", {"tooltip": "From StyleRef Load."}),
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},
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"optional": {
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"max_images": (
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"INT",
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{
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"default": MAX_IMAGES,
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"min": 1,
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"max": MAX_IMAGES,
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"step": 1,
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"tooltip": "Load at most this many inspiration images.",
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},
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),
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},
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}
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RETURN_TYPES = ("IMAGE", "INT")
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RETURN_NAMES = ("images", "count")
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OUTPUT_TOOLTIPS = (
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"All inspiration images as one batch (resized to the first image's size) "
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"— wire into IPAdapter, a reference input, or Preview Image.",
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"How many images were loaded.",
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)
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FUNCTION = "fetch"
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CATEGORY = CATEGORY
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DESCRIPTION = "The style's inspiration images as an IMAGE batch, for image conditioning."
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def fetch(self, style: dict[str, Any], max_images: int = MAX_IMAGES):
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if not is_style(style):
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raise StyleRefError(
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"The `style` input is not a StyleRef style — connect StyleRef Load."
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)
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urls = inspiration_image_urls(style)[: max(1, max_images)]
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if not urls:
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raise StyleRefError(
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f"“{style.get('name') or style.get('ref')}” has no inspiration images. "
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"Add some on styleref.io, or disconnect this node."
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)
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# Lazy on purpose — see the module docstring.
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import numpy as np
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from PIL import Image
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frames = []
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size: tuple[int, int] | None = None
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for url in urls:
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try:
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image = Image.open(io.BytesIO(_download(url))).convert("RGB")
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except StyleRefError:
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raise
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except Exception as err: # noqa: BLE001 - a bad file must name itself
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raise StyleRefError(f"Could not decode inspiration image {url}: {err}") from err
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if size is None:
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size = image.size
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elif image.size != size:
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# One batch = one resolution in ComfyUI. The first image sets
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# it; the rest are resized to match.
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image = image.resize(size, Image.LANCZOS)
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frames.append(np.asarray(image, dtype=np.float32) / 255.0)
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batch = np.stack(frames, axis=0) # (B, H, W, 3) float32 in 0-1
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# ComfyUI's IMAGE type is a torch tensor — SaveImage/PreviewImage and
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# IPAdapter all call `.cpu()` on it, which a bare ndarray lacks. torch
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# ships with every ComfyUI install; the numpy fallback only matters to
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# the unit tests, whose dev extras are pillow + numpy only.
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try:
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import torch
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return (torch.from_numpy(batch), len(frames))
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except ImportError:
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return (batch, len(frames))
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