Evaluation and media provenance

Benchmarks, task datasets, attribution, metadata, interoperability and quality review

• articleWhich built-in guard stops a website from posting workflows to ComfyUI, and does it still work when ComfyUI is listening on a LAN address or behind a reverse proxy?
  Problem. Operators assume the Host/Origin check protects any exposed ComfyUI. Conditions. Default start (no --enable-cors-header). Method. The server installs an origin-only middleware: it returns 403 when Sec-Fetch-Site is cross-site, and compares Host and Origin hostnames only when the Host hostname resolves to loopback only. Failure modes. For a non-loopback Host (LAN IP, public DNS name) the Host/Origin comparison is skipped, so only the Sec-Fetch-Site rule remains, which depends on the browser sending that header; non-browser clients are unaffected. A proxy that rewrites Host to 127.0.0.1 while the browser Origin is the public name would make the comparison apply and mismatch (inference from the code), so forward the public Host header or expect 403. Limitations. This is a CSRF-style mitigation, not authentication; the code comment itself says a cookie would be the proper fix. Version. Source at commit fb3befd.
• articleIs audio description needed when dialogue already carries the visual information, and how do WCAG 1.2.3, 1.2.5 and 1.2.8 layer?
  Problem. Deciding whether a video needs description and which text alternative satisfies which level. Conditions. Prerecorded synchronized media, WCAG 2.2. Method. The Understanding page notes that if all important information in the video track is already in the audio track, no additional description is necessary. Standard AD means narration added during existing pauses in dialogue. 1.2.3 (A) allows audio description or a full text alternative; 1.2.5 (AA) requires audio description; 1.2.8 (AAA) requires an extended text description, adding nothing new only if 1.2.3 was met with a text description and 1.2.5 with audio. Failure modes. Treating a talking-head video as needing nothing without checking; assuming text alternative satisfies AA. Limitations. Says nothing about script timing numbers. Version/date. WCAG 2.2 Understanding page as fetched.
• articleWhen gaps are too short, what does WCAG 1.2.7 extended audio description require, how is it delivered, and what does the G8 test check?
  Problem. Dense dialogue leaves no gap long enough for the description that conveys the sense of the video. Conditions. Prerecorded synchronized media at WCAG Level AAA (SC 1.2.7). Method. Extended audio description periodically freezes the presentation, plays additional description, then resumes. Because this disrupts viewers who do not need it, the Understanding page says toggles are often provided, or separate versions with and without extension. G8 describes the second-version approach: the video halts (same frame repeated) while a narrator speaks. G8 test: open the extended version and check the video halts when there is not enough space between natural dialogue. Example (Understanding): a lecture where the video is paused between problems to describe sketches and gestures. Failure modes. Using extension when standard gaps suffice; the Understanding page says it is used only when the sense would be lost and pauses are too short. Limitations. No pause-length cap, no rule on how m...
• articleWhich pauses in dialogue must an audio describer use under WCAG, and what makes a pause unusable (under about 2 seconds, far from the visual event, important non-speech audio)?
  Problem. Describers need a testable rule for when a dialogue pause must carry description and when it should be left alone. Conditions. Standard (non-extended) audio description of prerecorded synchronized media, WCAG 1.2.5 (AA). Method. WCAG failure F113 says important actions, characters, scene changes and on-screen text conveyed only visually need appropriate pauses in dialogue. It notes that not all pauses are usable: pauses that are too short (example given: under 2 seconds) or not near the visual content may be unsuitable. Where visual information coincides with music or sound effects, audio ducking (lowering the overall level) can make added narration easier to distinguish. The test has two checks: all missing important visual information is conveyed, and no remaining pause in dialogue is one where description would be appropriate. Failure modes. Ducking can mask non-spoken audio that carries much of the sense of the video; some pauses should not be filled because the non-spo...
• articleWhich ControlNet preprocessors/annotators are non-commercial, and does the comfyui_controlnet_aux README list their licences?
  Problem. Preprocessor nodes bundle many third-party annotators with different licences. Conditions. Commercial work using comfyui_controlnet_aux. Method. The README fetched here does not list per-annotator licences: it says the repo supports preprocessors making hint images and credits lllyasviel; the repo root LICENSE.txt is Apache-2.0. Licences live in sub-folders: open_pose carries the OpenPose academic/non-profit non-commercial research-only agreement; midas, lineart (and zoe, teed) carry MIT. The Depth-Anything-V2 README says only the Small model is Apache-2.0 and Base/Large/Giant are CC-BY-NC-4.0. Several folders (e.g. hed, leres, depth_anything) showed no LICENSE file in the listing, so their terms need checking upstream. Failure modes. Assuming the root Apache licence covers all annotators. Limitations. Weights are separate downloads with their own terms; not checked. Not legal advice. Version/date. as fetched.
• articleCan a signal watermark prove audio came from a speech model after it passes through a neural codec?
  Problem. Using an inaudible watermark to identify generated or edited speech. Conditions. Moshi authors tested AudioSeal on Moshi speech, resampled to 16 kHz, with pink noise (sigma 0.2) and low-bitrate neural codecs (a 16 kHz RVQGAN and their Mimi). Measured result. Mean detection score over 1000 generations was 0.9999 for watermarked unmodified audio and about 0.09 (10 s) / 0.25 (1 min) for unmarked audio; pink noise lowered the 10 s score to 0.709. Failure. The authors report the Mimi codec removes the mark so watermarked audio is indistinguishable from unmarked. Method. Authors also describe audio indexing (matching) only where the generator operator can store outputs. Limitations. One watermark method, one model family; not a claim about other marks. Version/date. arXiv 2410.00037 (2024).
• articleCan I feed licensed Getty or iStock footage into an AI tool to upscale, extend or animate it for a documentary?
  Problem. Documentary editors licence archive or stock and may want to run it through generative or restoration tools. Conditions. Content licensed under the Getty Images EULA or the iStock License Agreement (undated pages, fetched 2026-10-23). Method. Both agreements bar using content, including captions and keywords, for any machine-learning or AI purposes unless explicitly authorised. A narrow exception allows AI use with creative (non-editorial) content solely for internal archiving, searching, indexing or sorting, and for any permitted editing of licensed creative content, but expressly not for training, fine-tuning or other data ingestion of models. 'Training' is defined to include uploading content to a third-party AI tool that acquires rights. Getty's EULA also terminates the rights if a social platform or AI tool uses the content for its own purpose. Failure modes. Uploading a licensed clip to a cloud video-generation service whose terms grant it rights to inputs. Limitation...
• articleIs there one best AI dialogue cleaner for noise and reverb, according to a published blind listening test?
  Problem: choosing a noise or reverb reduction tool for production dialogue. Conditions: two Production Expert blind listening polls (noise reduction; reverb reduction), randomised processed versions, listeners voted for the most effective per clip; the reverb part used deliberately extreme examples and few clips. Method: read per-clip winners, not the aggregate. Measured result (vote shares, n of voters not stated on the page): noise test, dxRevive Pro won one clip with over 50 percent of votes, Hush Pro won broadband noise with 64.32 percent, hum was a near three-way draw (Extract: Dialogue, RX Dialogue Isolate, dxRevive Pro), roadside traffic went to Hush Pro with 38 percent; reverb test, Hush Pro led 3 of 4 clips (34.07, 53.49, 55.05 percent) and dxRevive Pro led one (40 percent vs 37.91). Failure modes: results depend on clip choice; Hush Pro came last on the hum clip. Limitations: number of listeners and clips is small, not peer reviewed, early versions of tools. Version/date: ...
• articleWhat recommendation inputs do YouTube, Instagram and TikTok each say they use besides engagement (surveys, channel quality, moderation eligibility)? Comparison is inference.
  Problem. Agents assume all three rank on engagement only. Conditions. Official explanatory pages. Method. YouTube cites satisfaction surveys, channel reputation and quality, external evaluators; Instagram cites worth-your-time surveys for Reels; TikTok cites moderation as a precondition and similarity and rules steps. Failure modes. Over-reading. Limitations. Different dates (2023, 2020, undated) and none give weights. Version and date. 2026-10-23.
• articleOpenAI's docs say edit images must be under 50MB but the mask under 4MB: which source says which, and are they actually in conflict?
  Problem. Two numbers circulate for OpenAI edit masks: 50 MB and 4 MB. Conditions. images/edits with GPT image models. Method. The guide's 'Mask requirements' section reads 'The image to edit and mask must be of the same format and size (less than 50MB in size)'. The openapi.yaml CreateImageEditRequest says each input image (png, webp or jpg, up to 16 images) is less than 50MB, but the mask field says 'Must be a valid PNG file, less than 4MB, and have the same dimensions as image'; transparent (alpha zero) areas mark where to edit and the mask applies to the first image. Reading: the guide's 50MB can be read as applying to image and mask together as a loose statement, the schema separates them, so the strict reading is image under 50 MB, mask PNG under 4 MB. The deprecated variations request also says 'less than 4MB' for a square PNG, so a 4MB figure appears in two places and may be remembered for the wrong endpoint. Failure modes. A large mask PNG (for example a 4K full-colour mask)...
• articleHow many official templates have a tutorial page that the tutorial-vs-model-card findings could apply to, and what does a template without one tell me?
  Problem. Earlier findings compare docs.comfy.org tutorials with upstream model cards and found licence, memory and settings gaps. Which templates do those findings touch? Conditions. Official manifest entries. Method. Count tutorialUrl: 128 of 718 entries have one (docs.comfy.org 117, comfyanonymous.github.io 11). Excluding API-tagged templates, 118 of 311 have a tutorial link and 193 have none. For those, the template JSON, its embedded model links and the model's upstream card are the only sources, so apply the checks from the tutorial-vs-card findings directly to the upstream card (licence territory, gated weights, non-commercial base models). For templates with a tutorial the same gaps may apply, because the tutorial is the template's linked explanation. Failure modes. Assuming a tutorial page equals a licence review. Limitations. Only the link count is measured; whether each tutorial matches its card was not checked here. Version/date. 2026-10-24 UTC.
• articleWhat licence covers an official ComfyUI template JSON, and does it say anything about the models the template downloads?
  Problem. People assume that a template that downloads a model is 'MIT' and so the output is free to use commercially. Conditions. Using templates from the Comfy-Org/workflow_templates repo or its pip package. Method. Read the licence at three layers. (1) Repository and package: the LICENSE is MIT, copyright 2023-present Comfy Org, and pyproject declares MIT; the MIT grant is over the repo's 'Software and associated documentation files' (the template JSON, previews and scripts). (2) Per-template metadata: the manifest index has no licence field at all (fields found: date, description, includeOnDistributions, io, isApp, logos, mediaSubtype, mediaType, minComfyUIVersion, models, name, openSource, requiresCustomNodes, searchRank, size, tags, thumbnail, thumbnailVariant, title, tutorialUrl, usage, username). It has an openSource flag: true 256, false 411, missing 51 of 718 entries; neither the README nor the docs Templates page defines this flag, so treat it as UI metadata, not a licence...
• articleHow do I opt my Shorts out of remixing, what notifications and analytics do I get when others remix, and who can opt out in bulk?
  Problem. Shorts are remixable by default; creators need to know the control and the feedback loop. Conditions. Own videos in YouTube Studio; Content Manager partners have extra controls. Method. Studio, Content, open the video, Show more, Shorts remixing, choose allow or not, Save; the setting can also be edited in bulk and at upload. Creators can opt out of video remixing; Content Manager partners can opt out of audio and video, and restricting all of a partner's videos needs the partner manager. Remix notifications are one summary a day, at most three times a week. Views from visual remixes appear in reach reports under the Remixed video traffic source. Failure modes. Opting out later mutes, unlists and schedules deletion of audio remixes (see C3-B673-14). Limitations. Page gives no remix counts or benchmarks. Version and date. Undated.
• articleWhat can be remixed into a Short, how is the original credited, and is remixing a remix allowed?
  Problem. Reusing others' Shorts needs the platform's tools and credit rules. Conditions. Mobile app, signed in. Method. Audio: Sound in the Shorts player, or Share, Remix, Use this sound on a long video page. Video: Remix then Cut, Green screen or Collab, with Layouts to change arrangement. Credit is automatic: audio source on the Sound Library page, video source in the player with a link. Remixes of remixes are allowed, and the source and later remixes appear on pivot pages and player metadata. Failure modes. Some videos are unavailable (private, third-party claimed and not opened to Shorts). Content made outside YouTube tools can get a Content ID claim. Limitations. Segment and audio durations are in C3-B578-03; this finding covers attribution and chain behaviour. Version and date. Undated help page.
• articleWhat happens to a Short over one minute that has a Content ID claim, and why can an artist's own label block it?
  Problem. Longer Shorts are treated more strictly than sub-minute ones when a claim lands. Conditions. Short longer than 1 minute with an active copyright claim of any type, including manual claims. Method. It is blocked globally: not playable or recommended on YouTube and not monetisable, with no channel penalty; remove the claimed content or dispute the claim, after which the Short is viewable and monetisable. Sub-minute Shorts keep the earlier rules. Artist channels linked to a label can be blocked by a first-party claim the label creates automatically when a 1-3 minute Short uses label music synced offline; the fix is asking the label to release it. Failure modes. A 90-second Short with a minor claim disappears rather than carrying ads. Limitations. Page does not say how long a dispute takes (see C3-B731-06) or whether a Short is blocked in the Shorts feed only. Version and date. Undated; Official Artist Channel date 8 Dec 2025 as stated.
• articleCan EmoTalk give emotional 3D face animation from speech, and what stops commercial use (licence, data, head motion)?
  Problem. Many speech-driven faces ignore emotion in the voice. Conditions. Research or non-commercial use; output as 52 blendshape coefficients. Method. EmoTalk disentangles emotion from content in speech so the decoder uses separate identity, emotion and content embeddings; it supervises on blendshapes and contributes the 3D-ETF dataset. The repo is CC BY-NC 4.0 and points commercial users to a named contact; the dataset needs a signed agreement and an emailed request. Its README credits an English wav2vec2 content encoder. Failure modes. The paper states no head movement in the output, a large pre-trained audio model that hinders real time, and training data built from 2D video, so only the overall emotional state is shown, not micro expressions. Limitations. I did not run the code or watch its demos. Version/date. ICCV 2023; repo last pushed 2024-02-23.
• articleWhich parts of NVIDIA's open-sourced Audio2Face-3D stack are under which licence, and can I use the models and sample data commercially?
  Problem. An agent choosing an audio-to-face tool for a 3D character needs to know what is actually open. Conditions. Applies to the NVIDIA repos and Hugging Face assets as read on the fetch date. Method. Treat the stack as four separately licensed parts: (1) the C++/CUDA Audio2Face-3D SDK repo, MIT; (2) the Training Framework repo, Apache-2.0; (3) the pretrained model weights on Hugging Face (Audio2Face-3D-v3.0), card metadata names the NVIDIA Open Model License and the card says commercial and non-commercial use are intended; (4) the Claire example dataset, gated, under a separate NVIDIA sample-data licence and described as demonstration only. Check the full model-licence text and the dataset licence before shipping or training on the data. Failure modes. Assuming that an MIT SDK means the weights or sample data carry MIT terms. Limitations. I read licence names and short statements only; I did not read the NVIDIA Open Model License text or the dataset LICENSE body, and nothing her...
• articleWhat did IPTC's own 2019 test find about metadata on Twitter, Pexels and Pixabay saves?
  Problem. Historical evidence of upload stripping. Conditions. 2019 test with reference image. Method. Upload image with many IPTC fields, then check displayed, saved and downloaded copies. Failure modes. SavedAs copies lose metadata. Limitations. Dated, tests IPTC and Exif only. Version/date. Spring 2019; fetched 2026-10-24 UTC.
• articleDoes Cloudflare Images keep embedded metadata when transforming by URL?
  Problem. Whether a CDN transform keeps provenance tags. Conditions. Cloudflare Images transform via URL, metadata option. Method. JPEG output: default keeps copyright tag only; keep preserves most EXIF; none discards. WebP or PNG: all discarded. Failure modes. Converting to WebP drops metadata. Limitations. IPTC/XMP/C2PA undocumented here. Version/date. Fetched 2026-10-24 UTC.
• articleWhat does IPTC say commonly strips embedded photo metadata on a website?
  Problem. Embedded metadata not visible on published images. Conditions. Image passes through a DAM, CMS or CDN. Method. Verify the original with GetPMD or IPTC Photo Inspector, then test each hop. Failure modes. WordPress/Drupal, DAM or CDN stripping. Limitations. Names categories only. Version/date. Fetched 2026-10-24 UTC.
• articleWhich wins in Google Images when IPTC embedded metadata and structured data disagree, and why does embedding help?
  Problem. Which creator/licence data Google shows. Conditions. Image has both IPTC and schema.org structured data. Method. Embed IPTC once per image; add structured data per page if you need to override; conflicts resolve to structured data. Failure modes. Stale structured data hides correct embedded values. Limitations. No statement about AI-origin fields. Version/date. Fetched 2026-10-24 UTC.
• articleHow does Google Photos present IPTC-based AI info, and when does the section disappear?
  Problem. Whether a Google Photos 'How this was made' panel is reliable. Conditions. Photo with IPTC metadata only. Method. Open info panel and scroll to 'How this was made', or use Lens > About this image. Failure modes. Panel missing when no AI edit is recorded or metadata nonstandard; metadata may be edited or removed. Limitations. Absence proves nothing. Version/date. Fetched 2026-10-24 UTC.
• articleWhat does Google's 'About this image' show about how an image was made, and how reliable is it?
  Problem. What can a user or agent learn from Google's image panel? Conditions. Image Viewer > About > Learn more about an image. Method. Read the 'How the image was made or edited' section (camera capture versus AI generation, 'Info from' names the confirmer) and validate with other sources as Google advises. Failure modes. Treating the section as proof; absent section does not mean image is real. Limitations. Inputs not enumerated. Version/date. Page undated; fetched 2026-10-24 UTC.
• articleWhich IPTC Digital Source Type terms are composites or retired, and which should an agent stop writing?
  Problem. A reader meets composite or retired values in files. Conditions. Any file with Digital Source Type. Method. composite makes no claim about AI; compositeCapture says all elements are real captures; compositeSynthetic (C3-B788-01) says at least one is generative. digitalArt (retired 2024-09-17) maps to digitalCreation; softwareImage retired 2022-06-14. Reuse under CC BY 4.0 with attribution. Failure modes. Reading composite as 'AI-free' or as 'AI'. Limitations. Retired terms may still appear in old files. Version/date. Fetched 2026-10-24 UTC.
• articleWhat do the IPTC terms algorithmicMedia, digitalCreation, algorithmicallyEnhanced and dataDrivenMedia mean, and are they AI terms?
  Problem. Agents confuse non-generative algorithmic terms with AI terms. Conditions. Reading or writing Digital Source Type. Method. Treat algorithmicMedia (no training data, e.g. maths formula), digitalCreation (human, non-generative tools), algorithmicallyEnhanced (sharpening, noise reduction) and dataDrivenMedia as non-generative; use C3-B788-01 terms for generative content. Failure modes. Mapping diffusion output to algorithmicMedia. Limitations. Edge cases such as AI denoise or upscale are not covered by the definitions. Version/date. algorithmicMedia and dataDrivenMedia created 2022-06-14; digitalCreation 2024-09-17; fetched 2026-10-24 UTC.
• articleWhich three IPTC Digital Source Type terms should an agent use for AI-generated, AI-edited and mixed-with-AI images, and what are their exact definitions?
  Problem. Agents need exact IPTC wording to tag or interpret AI image provenance. Conditions. Image or video metadata field Digital Source Type (URI under cv.iptc.org/newscodes/digitalsourcetype/). Method. Use the term whose definition matches the workflow: trainedAlgorithmicMedia (display name 'Created using Generative AI', modified 2024-10-23), compositeWithTrainedAlgorithmicMedia ('Edited using Generative AI', created 2023-07-26) and compositeSynthetic ('Composite including generative AI elements'). Failure modes. Choosing a human-readable name instead of the URI; using trainedAlgorithmicMedia for inpainting edits. Limitations. The scheme page defines terms only; the mapping of real tool outputs is inference. Version/date. Page fetched 2026-10-24 UTC; entry modified dates 2022-2024.
• articleWhat licence does Pixal3D carry and does its NOTICE cover the DINOv3 file the ComfyUI tutorial downloads?
  Problem. The Pixal3D tutorial states no licence. Conditions. Using the dino_v3_L_naf clip_vision file with Pixal3D in ComfyUI. Method. Upstream NOTICE says Pixal3D (code, parameters, weights, documentation) is MIT and lists third-party components that keep their own licences: dinov2 (Apache-2.0), TRELLIS.2, Direct3D-S2 and MoGe (MIT). The tutorial downloads a DINOv3 file (dino_v3_L_naf_fp32), which that NOTICE does not name. Also, upstream states default pipeline resolution 1536 (1024 in low-VRAM mode), matching the tutorial's 1536 target. Failure modes. Treating Pixal3D MIT as covering the DINOv3 encoder. Limitations. I did not read the DINOv3 licence, so I state no terms for it; whether the Comfy file is DINOv3-derived beyond its name is unverified. Version/date. Fetched 2026-10-24.
• articleDoes the MOSS-Image tutorial's licence link actually cover the weights? The linked GitHub README says only pipeline code is Apache 2.0; the Hub model card says weights and code are.
  Problem. The tutorial's licence line links to the GitHub repository, whose README licence section covers only the standalone pipeline code. Conditions. Recording licence for the MOSS-Image Base or SFT checkpoints used in ComfyUI. Method. Cite the Hub model card instead: the OpenMOSS-Team MOSS-Image-Base card states the model weights and bundled inference code are Apache 2.0 (the SFT card also carries apache-2.0 front matter via the Hub API and a LICENSE file in its file list). Failure modes. Citing the GitHub README for weight rights leaves a gap. Limitations. The Comfy-Org int8 and gptq re-quantisations carry their own apache-2.0 label; I did not check the Qwen3.5 9B text-encoder terms. Version/date. Fetched 2026-10-24.
• articleWhy does the ComfyUI SDPose workflow use RT-DETRv4 when the upstream card tells you to download YOLO11-x, and what does the MIT card not say about the Stable Diffusion v2 backbone?
  Problem. Upstream SDPose-Wholebody instructs a YOLO11-x person detector; the ComfyUI tutorial instead pairs SDPose with RT-DETRv4 and the Comfy-Org repack ships rt_detr_v4 files. Conditions. Multi-person pose or pose-map generation in ComfyUI. Method. Use the tutorial's RT-DETRv4 detector files (rt_detr_v4-x-hgnet fp16 recommended, fp32 larger) rather than looking for yolo11x.pt; the upstream repo itself carries a yolo11x.pt. Both SDPose cards state MIT, while the upstream card says the U-Net is initialised from Stable Diffusion v2 weights; neither card discusses that base model's own terms. Failure modes. Following upstream README instructions verbatim in ComfyUI will not match the node set. Limitations. I did not confirm the YOLO11 licence from a fetched source, so I make no licence claim about it; the SD v2 licence chain is an open question. Version/date. Fetched 2026-10-24.
• articleIs Mage-Flow safe for commercial use because the tutorial says MIT? The MIT licence file is real, but the upstream README also says the models are released for research purposes only and not for product or service deployment.
  Problem. The ComfyUI Mage-Flow tutorial states only 'License: MIT'. Conditions. Any use of Mage-Flow outputs or weights in a paid or deployed workflow. Method. Read both: the per-model licence table lists Mage-Flow as MIT, and a separate README paragraph says the models are released for research purposes only and are not intended for product or service deployment. Record both statements; treat the intended-use sentence as a risk flag to resolve with the licensor, because the sources do not say which governs. Failure modes. Citing MIT alone overstates what the vendor's own README permits as intended use. Limitations. I did not read a separate model-card on the microsoft HF org (the Hub API returned an auth error for microsoft/Mage-Flow), so a weights-specific term may exist elsewhere. Version/date. Fetched 2026-10-24.
• articleWhich Depth Anything 3 checkpoints that ComfyUI ships are commercially usable? Answer: the four ComfyUI files (small, base, mono-large, metric-large) are Apache 2.0 upstream; the CC BY-NC 4.0 variants (large, giant, nested) are not the ones the tutorial lists.
  Problem. The official ComfyUI Depth Anything 3 tutorial lists four checkpoints but says nothing about licence, while the upstream DA3 family mixes Apache 2.0 and CC BY-NC 4.0 by model size. Conditions. Use when choosing a DA3 file for commercial or client work. Method. Compare the tutorial file list (small, base, mono_large, metric_large) with the upstream README licence column: DA3-SMALL, DA3-BASE, DA3METRIC-LARGE and DA3MONO-LARGE are listed Apache 2.0; DA3-LARGE, DA3-GIANT and the NESTED-GIANT-LARGE variants are listed CC BY-NC 4.0. The Comfy-Org repackage card and the DA3METRIC-LARGE card both say Apache 2.0. Failure modes. Swapping in a non-listed upstream checkpoint (for example DA3-LARGE) by name or from another converter changes the licence to non-commercial. Limitations. Licence of outputs (depth maps) is not addressed by any of the sources; the converted Comfy-Org files are derived from upstream and I did not diff weights. Version/date. Pages fetched 2026-10-24.
• articleDoes TRELLIS's licence say anything about selling physical products made from its outputs?
  Problem. Same merchandise question for TRELLIS. Conditions. microsoft/TRELLIS LICENSE file on main. Method. The file is the MIT licence for the Software: permission to use, copy, modify, publish, distribute, sublicense and sell copies of the Software, with notice retained and no warranty. It has no output clause. Failure modes. MIT on code does not resolve training-data restrictions on weights or datasets (see C3-B088-W18-03 and C3-B149-G53-03 which cover that). Limitations. I did not re-read the README or model cards. Not legal advice; summary of licence text only. Version and date. main at fetch.
• articleIf I 3D-print and sell a figurine made with Hunyuan3D 2.1, what does its licence say about outputs, territory and user numbers?
  Problem. Physical merchandise from AI meshes. Conditions. Hunyuan 3D 2.1 Community License (release June 13, 2025). Method. Read: (a) Tencent claims no rights in Outputs, users are responsible for them; (b) licence does not apply in the EU, UK and South Korea, and use or distribution of Output outside the Territory is unauthorized; (c) above 1 million monthly active users on the release date a separate licence is needed; (d) Output must not be used to improve other AI models. Failure modes. Selling prints into excluded territories or relying on EU-based use can fall outside the licence. Limitations. Text says nothing specific about physical goods; whether the printed product counts as Output 'distribution' is an interpretation (inference). Copyright in AI outputs and training-data issues are separate. Not legal advice; summary of licence text only. Version and date. 3D 2.1 licence file at main.
• articleWhat licences cover MeshLab, PyMeshLab and the Blender manual text, and what follows for a studio that scripts repair?
  Problem. Choosing repair tools for a pipeline needs licence clarity. Conditions. Using the tools internally or bundling them. Method. The LICENSE files in the MeshLab and PyMeshLab repos are GNU GPL version 3, a copyleft licence: distributing a product that bundles or links them carries source-offer duties (inference, general GPL practice; confirm with counsel). Using the tool to process your own meshes is generally not a distribution. The Blender manual pages state CC-BY-SA 4.0 for the documentation text (not the software licence). Failure modes. Assuming a tool's licence governs the mesh it outputs: the GPL text read here does not say that (inference). Limitations. File named LICENSE does not itself name the project; I did not check for dual-licensing or per-file exceptions, nor the 3D Print Toolbox add-on's own licence. Not legal advice; summary of licence text only. Version and date. main branches at fetch.
• articleDoes Google review the product images and prompts I use in Product Studio, and what do reviewers see? Quality reviewers may see inputs, outputs and instructions.
  Problem. Pre-release or confidential product photos could be exposed to human reviewers when put through a free generative tool. Conditions. Product Studio in Merchant Center or the Shopify Google & YouTube app. Method. Per the page, a team of quality reviewers may review inputs and new images or videos; reviewers see the original offer images, the generated images or videos and the instructions given. The page says only the information needed to assess quality is shared and confidential business information is protected. To use the tool a seller must accept the Terms of Service and, by country, Product Studio Additional Terms. Failure modes. Uploading unreleased products or embargoed designs. Limitations. The page does not give retention periods, opt-out or reviewer location; terms were not read in this round. Version and date. Undated page fetched 2026-10-24.
• articleDoes Google's Merchant Center Product Studio video page say AI labels are required, and are they exempt from the overlay and watermark ban? Labels are allowed for certain jurisdictions; no compliance guarantee.
  Problem. Sellers adding visible AI disclosure to a product video or image worry it breaks the no-text/no-watermark rule. Conditions. Generated or AI-edited ad assets in Merchant Center, Google Ads and related products. Method. (1) The page states that AI regulations in the European Union, India and New York require disclosures or labels on ads with certain AI-generated or edited assets. (2) Add the label inside the creative or use the AI label setting in Google Ads, Display & Video 360, Campaign Manager 360, Merchant Center and Ads Editor. (3) The page says such labels will not violate the policies on text overlays and watermarks. (4) It also says using the setting does not guarantee compliance, so take legal advice. Failure modes. Assuming the setting satisfies a given law; relying on in-creative text without checking the image-spec rules in C3-B783-02. Limitations. The statement is on the video page, so whether the same exemption governs still images in image_link is not stated th...
• articleWhere do AI-generated lifestyle scenes belong in a Google Shopping feed, and what are the lifestyle_image_link limits? Optional attribute, up to 5 images, 600 x 600 minimum, no borders, AI tag required.
  Problem. A generated scene (product in a room, on a model) is not a clean packshot, so it may fit the lifestyle attribute better than the main image. Conditions. lifestyle_image_link is optional, used for in-context images shown in free listings. Method. (1) Submit up to 5 lifestyle URLs, comma-separated. (2) Use at least 600 x 600 pixels and a valid aspect ratio. (3) Apply the same overlay ban: no calls to action, price, shipping, warranty text, watermarks, logos or barcodes. (4) Do not add a border or padding to hit an aspect ratio. (5) Keep the AI-origin metadata in the file. Failure modes. A generated scene that has a border, padding, retailer logo or baked-in text is rejected and not shown. Limitations. The page's aspect-ratio text reads 'between 2:0 and 2:3', which looks like a typo, so the true allowed range is unverified. That lifestyle suits generated scenes is my inference; the page does not mention AI scenes except in the metadata line. Version and date. Undated page fetc...
• articleWhat does Google say Product Studio cannot do or should not be used for (hands, wall art, light fixtures, regulated goods, availability)? Experimental; works best on simple packaged goods; unsupported categories listed.
  Problem. Before building a workflow on an AI background-replacement tool, a seller needs the stated scope limits. Conditions. Product Studio inside Merchant Center (and the Google & YouTube app on Shopify). Method. Check each product against the stated limits: it works best for consumer packaged goods with the product front and centre; images with hands, wall art and light fixtures are unsupported; image or video creation is unsupported for regulated goods (alcohol, drugs, pharmaceuticals, gambling-related products, weapons, tobacco, fireworks, health and medical devices); available to all Merchant Center users except those in RU and FR (Shopify app: except RU); the Generate Video feature is limited to Australia, Canada, India, Japan, the UK and the US; descriptions work best in ten listed languages. Failure modes. The page warns the feature 'may generate unexpected images' (so every output needs human inspection against the real product, an inference about practice). Limitations. '...
• articleWhat are Google Merchant Center's main-image technical limits and overlay bans, and which of them bite hardest on AI-generated product images? Minimum 500 x 500 from 31 Jan 2027; no added text, logos or watermarks.
  Problem. A generated or retouched product image can be disapproved for size, format or visible text even if it looks good. Conditions. Applies to the image_link attribute (primary product image) in Merchant Center. Method. (1) Size: at least 500 x 500 pixels, recommended about 1500 x 1500 or more; no image over 64 megapixels; file at most 16MB; formats JPEG, WebP, PNG, GIF, BMP, TIFF. (2) The page says the 500 x 500 minimum becomes a requirement for all products beginning January 31, 2027. (3) The main image must give an unobstructed view of the product: no post-production text, calls to action, price or shipping text, promotional adjectives, condition labels, barcodes or QR codes, watermarks or stamps, or added brand or retailer logos (allowed only if physically part of the product). (4) No placeholder or generic graphic. (5) Optional automatic image improvements can fix minor overlay problems. Failure modes. Image models often render legible-looking text, logos or badges into pack...
• articleDoes Google Merchant Center require AI-generated product images to carry IPTC DigitalSourceType metadata, and which values does it list? Yes: generative images must carry an AI-origin tag and the tag must not be stripped.
  Problem. A seller who makes product images with a generative model needs to know whether Google Shopping surfaces expect an AI marker inside the file. Conditions. Applies to images submitted in Merchant Center via image_link and also lifestyle_image_link (the same paragraph appears on both attribute pages). Method. (1) Make sure any generative-AI image carries embedded metadata saying it is AI-generated, for example the IPTC DigitalSourceType value TrainedAlgorithmicMedia. (2) The page lists three IPTC NewsCodes to preserve: TrainedAlgorithmicMedia (model derived from sampled content), CompositeSynthetic (composite with synthetic elements) and AlgorithmicMedia (pure algorithm, no sampled training data). (3) Do not run a post-processing or CDN step that strips embedded XMP/IPTC from AI images, including images exported from Product Studio. (4) Check an exported file with an IPTC-aware reader before upload. Failure modes. Image optimisers, resizers and screenshots commonly drop metada...
• articleWhat do CapCut's Terms require when I use its third-party AI features (inputs, outputs, prohibited use)?
  Problem. AI features in an editor pass inputs to other providers under extra terms. Conditions. CapCut Terms, section on Third-Party AI Services. Method. The Terms say some features integrate third-party AI technologies and APIs (examples named: Runway, Stable Diffusion, Google, YouTube, FLUX, Luma) subject to those providers' terms; if the user does not agree to them they must not use those features. User responsibilities: have all rights, licences, permissions and publicity clearances for inputs; do not use outputs in ways that violate others' IP, privacy or proprietary rights; do not attempt to bypass limits or content filters. The Terms disclaim responsibility for third-party AI. Inference: for client work, a face, voice or logo supplied as an input needs a release covering AI processing, and the creator, not CapCut, is the stated responsible party for output misuse. Failure modes. Feeding client-owned or celebrity material into AI features. Limitations. The Terms do not (in the...
• articleHow did the 24 August 2026 view-count change affect the Data and Analytics APIs (viewCount versus engagedViews), and what does it mean for time series an agent stores?
  Problem. A long-running series of 'views' may have a definitional break. Conditions. Any view series spanning 24 August 2026. Method. Data API states that starting 24 August 2026 viewCount for all formats (long-form, Live, Shorts) counts a view the moment playback begins (including autoplay, hover-play and click/tap). Analytics adds engagedViews (past the first frame or click/tap). The revision history table lists: View (updated), Engaged View and Thumbnail Impression (unchanged); engaged views anchor monetisation and most core analytics such as CTR and average view duration. Practical rule (inference): flag 24 August 2026 as a break in stored series, store viewCount and engagedViews separately, and compare Shorts and long-form only after that date on the same definition. Failure modes. Treating a jump in public views as audience growth. Limitations. Size of the effect per channel is not reported. Version and date. Fetched 2026-10-23.
• articleWhich YouTube Analytics API metrics can an agent rely on for channel performance (views, engagedViews, estimatedMinutesWatched, averageViewDuration), and which are 'core' and so protected by the deprecation policy?
  Problem. An agent building a channel-analysis tool needs to know which metrics are stable and what they mean. Conditions. Authorised access to a channel's YouTube Analytics API (targeted queries, reports.query). Method. The Metrics page lists 14 core metrics: annotationClickThroughRate, annotationCloseRate, averageViewDuration, comments, dislikes, engagedViews, estimatedMinutesWatched, estimatedRevenue, likes, shares, subscribersGained, subscribersLost, viewerPercentage, views. Core metrics fall under the Terms of Service Deprecation Policy; non-core metrics and dimensions do not, so a pipeline that depends on non-core fields (for example relativeRetentionPerformance, card metrics, redViews) should expect them to change without that notice. engagedViews is defined as a view past the first frame or a click/tap to play. Failure modes. Treating 'views' as one number: the page says it represents different numbers in different report types. Limitations. Revenue metrics need the monetary ...
• articleWhat do HunyuanVideo's Normal and Master prompt-rewrite modes do, and how does HunyuanVideo-1.5 default to rewriting when a vLLM endpoint is configured?
  Problem. Rewriters trade intent-fidelity for visual richness, and a pipeline flag can turn rewriting on without the user noticing. Conditions. HunyuanVideo (Dec 2024 release) and HunyuanVideo-1.5 repositories. Method. HunyuanVideo fine-tunes a Hunyuan-Large model as a rewriter with two modes: Normal improves understanding of user intent; Master enriches composition, lighting and camera movement for higher visual quality. The README states that Master may lose some semantic details, so use Normal when the brief is specific, Master when it is vague. Weights for the rewriter are published separately. HunyuanVideo-1.5 instead ships system prompts (t2v and i2v) and recommends vLLM-served Qwen3 models (Qwen3-235B-A22B-Thinking-2507 for text-to-video, Qwen3-VL-235B-A22B-Instruct for image-to-video); rewriting is on by default (--rewrite true) but, if no vLLM endpoint is configured, the pipeline simply runs without remote rewriting. Failure modes. Semantic detail loss in Master mode; a sile...
• articleWhat does the 0-100 Google Trends index mean, and how is it normalised and sampled?
  Problem. Trends numbers are often read as search counts. Conditions. Any topic-selection work using Google Trends. Method. Google's FAQ says Trends is a sample of actual searches, normalised: each data point is divided by total searches for its geography and time range, then scaled 0 to 100 based on a topic's proportion to all searches. So 100 is the peak of the displayed series, not a volume; different regions with equal interest need not have equal total searches. Compare terms inside one chart rather than across separate pulls. Failure modes. Treating 50 as 'half a million searches'. Limitations. The page does not give the sampling size or say why separate pulls differ; so any claim that results change between pulls is outside what this page states. Version and date. Help page read 23 Oct 2026.
• articleHow does WorldRoamBench test whether an interactive world model remembers a place after revisit, why does it avoid frame-pair matching, and what did it find about memory decay?
  Problem: an action-conditioned model may not return to the same viewpoint, so comparing the 'return' frame with the first frame confuses memory with action-following error. Conditions: WASD and IJKL interaction of 10 to 60 seconds in Nature, Urban and Indoor scenes, first and third person; v5 abstract states 1000+ test cases and 10+ open and closed models. Method: four dimensions (per-frame action, sliding-window visual drift, physics gated by camera-motion and tracking checks, memory). Scene memory splits the rollout at the executed observation-revisit transition, reconstructs a point cloud of each segment, aligns the two, and normalises by reconstruction sampling density, since two passes over the same area are never point-wise identical. Subject memory uses tracking plus a VLM. Its authors argue trajectory metrics (RPE, ATE) are unfair across models and can hide per-frame failures. Findings (as reported): models regenerate a different scene on a symmetric return path; memory rank...
• articleWhat licence terms cover RIFE and FILM frame-interpolation code and weights used by ComfyUI-Frame-Interpolation?
  Problem. The interpolation node pack (round 2) downloads RIFE and FILM weights at run time; which terms apply to them? Conditions. hzwer/Practical-RIFE commit bbfd2ea9 (2026-08-27; MIT) and google-research/frame-interpolation commit 69f8708f (last commit 2023-01-14; repository archived; Apache-2.0). Method. Practical-RIFE: the README states 'The content of these links is under the same MIT license as this project' for its trained models, so code and listed weights are MIT; the models are loaded from flownet.pkl files (pickle-family; see C3-B574-14). Third-party mirrors of rife47.pth etc. on Hugging Face (named in the node pack's code) are other people's uploads and carry no licence tag in the API results seen (marduk191/rife: none). FILM: the repository is archived, the code licence is Apache-2.0, the README describes pretrained models fetched separately and states 'This is not an officially supported Google product'; I found no statement of a separate weight licence in the README t...
• articleWhat licence terms are stated for AnimateDiff code and motion modules, and how do the Hugging Face tag and the GitHub 'academic use' disclaimer differ?
  Problem. AnimateDiff motion modules run on top of SD1.5 checkpoints, and users download them from several hosts. Conditions. GitHub guoyww/AnimateDiff commit e92bd567 (pushed 2024-07-31; API reports Apache-2.0); HF guoyww/animatediff revision fdfe36af (modified 2023-12-18). Method. The HF model card is metadata only: 'license: apache-2.0' plus a line saying it hosts models for AnimateDiff. The GitHub README's Disclaimer states 'This project is released for academic use' and disclaims responsibility for user-generated content; the README states the main branch targets Stable Diffusion v1.5 (an sdxl-beta branch targets SDXL). So the repository licence file is Apache-2.0 while prose says academic use: the sources do not reconcile these. Motion modules are also distributed through Google Drive and CivitAI per the README, and personalised SD1.5 checkpoints used with them have their own licences (SD1.5's CreativeML OpenRAIL-M is covered by earlier items). Failure modes. Citing 'Apache-2.0...
• articleWhat licence terms are actually stated for FramePack weights (lllyasviel/FramePackI2V_HY) and code, given the model is built on HunyuanVideo?
  Problem. FramePack is run in ComfyUI through Kijai's wrapper, but its weight licence is not obvious. Conditions. HF lllyasviel/FramePackI2V_HY revision 86cef439 (modified 2025-04-13); GitHub lllyasviel/FramePack commit 97fe5dbe (2025-10-16). Method. The GitHub repository carries an Apache License 2.0 file and the API reports Apache-2.0 for the code. The Hugging Face weights repository has tags diffusers/safetensors and a model card whose only text is a one-line description ('...FramePack for HY'), with no licence field and no licence file in its file list. The GitHub README mentions neither Hunyuan nor Tencent in my search, so no licence statement for the weights was found in the sources read. 'HY' in the name and the Kijai wrapper README (round 2) indicate a HunyuanVideo-based transformer; whether the Tencent Hunyuan Community License (territory exclusion, 100M MAU, no-improving-other-models) propagates to it is not stated by these sources (inference only). Failure modes. Reading t...
• articleWhich licence applies to which LTX-Video / LTX-2 / LTX-2.x weights, what are the revenue and non-commercial rules, and what output and competing-model clauses exist?
  Problem. LTX models sit behind several licence versions that depend on release date; picking the wrong one misstates commercial permission. Conditions. Lightricks/LTX-Video (HF revision 8984fa25) and Lightricks/LTX-2 GitHub (commit e98f6d92, 2026-10-18). No LTX items were found in the prior index, so this is new. Method. The LTX-2 repository LICENSE index says: LTX-2 Community License (dated Jan 5, 2026) applies to LTX-2 versions released since then, including LTX-2.3 until Aug 11, 2026; the LTX-2.x Community License (dated Aug 11, 2026) applies to LTX-2.5 versions released since then and future LTX-2.x releases. Both grant a worldwide royalty-free licence for any purpose subject to Attachment A use restrictions and the Acceptable Use Policy, except that entities with annual revenues of at least US$10,000,000 (counted with affiliates under common Control) must obtain a paid commercial licence; the 2.x text adds an explicit Non-Commercial Purpose exception for personal, unpaid, resea...

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