build(deps): bump diffusers from 0.27.2 to 0.40.0 in /skills/remove-ai-marks/scripts

[dependabot]

Bumps diffusers from 0.27.2 to 0.40.0.

Release notes

Sourced from diffusers's releases.

Diffusers 0.40.0: New pipelines, tensor-parallel support, improved CLI, and more

[!TIP] This release features several new pipelines, including LTX2.5, MiniMax H3, and Wan Animate 2. We're also graduating Modular Diffusers out of the experimental phase and announcing its stable support. Additionally, this release includes minimal support for tensor-parallel. There's a lot more that went down in this release. So, please consult the notes for details.

New Pipelines

MiniMax-H3

MiniMax-H3 generates video and its soundtrack together. A single transformer denoises one packed sequence containing the text conditioning, the conditioning media, and the target video and audio latents — there is no separate vocoder and no post-hoc audio pass. Its conditioner is a Qwen3VLForConditionalGeneration whose unnormalized 50th-decoder-layer hidden state is read instead of the last one.

MiniMax-H3 is integrated as Modular Diffusers blocks only — MiniMaxH3Blocks and their MiniMaxH3ModularPipeline are the whole integration. The conversion ships both checkpoint partitions in one repository and exposes three workflows (t2va, fl2va, ref2va) that can be pruned at from_pretrained time so only that task's components are declared and downloaded.

MiniMax Music 3

MiniMax Music 3 produces complete songs up to five minutes long from lyrics and a music description, with expressive vocals and long-range structure. It is a hybrid of an autoregressive and a diffusion stage: an 8B Qwen3-based global language model predicts one semantic audio token per frame while a small depth decoder fills in seven residual RVQ codebooks, and their fused hidden states condition a 2.4B flow-matching transformer that produces Flow-VAE latents in overlapping chunks. A DAC-style decoder turns the latents into 44.1 kHz stereo audio.

Stable Audio 3

Stable Audio 3 is a text-to-audio model from Stability AI that generates high-quality stereo audio at 44.1 kHz. It uses a rectified-flow DiT conditioned on a frozen T5Gemma text encoder (via cross-attention) and on duration (a float embedded by StableAudio3DurationEmbedder and used for adaptive layer norm), and decodes with the SAME (Semantically-Aligned Music Encoder) autoencoder, AutoencoderSAME.

Three pipelines ship: StableAudio3Pipeline, StableAudio3AudioToAudioPipeline, and StableAudio3InpaintPipeline.

Thanks to @​buffett0323 for the contribution (huggingface/diffusers#14119).

LTX-2.5

LTX-2.5 reuses the existing LTX2Pipeline / LTX2VideoTransformer3DModel / AutoencoderKLLTX2Video classes — there is no separate pipeline class. The user-visible difference is the text encoder: LTX-2.5 is paired with a Gemma 4 (gemma4_unified) checkpoint, loaded automatically from a converted LTX-2.5 repo.

Lightricks/LTX-2.5-Diffusers ships both the distilled DiT (transformer/) and the full/SFT DiT (transformer_full/), plus everything two-stage generation needs. Alongside the checkpoint support, this release adds:

• LTX2VideoDiffusionDecoderModel and LTX2VideoDiffusionDecodePipeline — a second video decoder over the same latent space, so latents are interchangeable between decoders.
• A duration_head that predicts shot length from the text-connector output, so num_frames is auto-predicted by default when the loaded pipeline has one.
• Prompt enhancement through a separate off-the-shelf google/gemma-4-E2B-it checkpoint (enable_prompt_enhancement=True).
• LTX25AutoBlocks for Modular Diffusers (#14453).

Wan-Animate-2

Wan-Animate-2 by the Alibaba Wan Team animates a reference character image with the motion of a driving video. The driving video is processed in fixed-length segments: each segment runs a reference-extraction pass that caches the driving segment's K/V in every transformer layer, denoises against that cache, and is decoded inside the loop, because the next segment conditions on the previous segment's decoded tail frames.

Two presets are available — the base checkpoint samples with classifier-free guidance, and the distilled checkpoint samples in few steps without it. Guidance is owned by the pipeline's guider component, so there is no guidance_scale argument.

Thanks to @​kelseyee for authoring the integration (huggingface/diffusers#14413).

JoyAI-Image-Edit-Plus

JoyAI-Image-Edit-Plus extends the JoyAI-Image family (an 8B MLLM paired with a 16B MMDiT) to multi-image instruction-guided editing. It accepts 1–5 reference images plus a text instruction and composes elements from the references into a new image.

Thanks to @​tangyanf for the contribution (huggingface/diffusers#14032).

Cosmos 3 follow-ups

... (truncated)

Commits

• d035dcd Release: v0.40.0-release
• 5900dbf [CI] Fix build and test step in release workflow (#14543)
• 3681e65 Restructure skill files and reference guides for CLI based installation. (#1...
• 192cf68 [distributed] fix corrupted gradient problem under ring CP. (#14274)
• 0aa743c [tests] migrate lora tests for pipelines to use new mixins (#14268)
• 4e0466f fix: image preprocessing for cosmos3 (#14519)
• ac56fa2 [CI] Fix authentication on forked PRs (#14534)
• 7d2e86a [core] Support tensor parallelism for model inference (CUDA, Neuron) (#13718)
• 360bef8 Feat -- Stable Audio 3 (#14119)
• aeda394 [CI] Allow single file tests to run as fast tests (#14324)
• Additional commits viewable in compare view