Feat:(model) qwen image vae checkpoint

[Pfannkuchensack]

Summary

Adds standalone model support for Qwen Image so users no longer need the full ~40 GB Diffusers pipeline. A GGUF transformer can now be combined with a standalone VAE checkpoint, a standalone Qwen2.5-VL encoder (Diffusers folder or ComfyUI single-file fp8), and the Component Source (Diffusers) field becomes a fallback rather than a hard requirement. All standalone components are also exposed as installable starter models, so a fully working GGUF setup can be installed in one click.

Why: The Qwen Image PR (#9000) only allowed loading the VAE and text encoder from the full Diffusers pipeline. That meant ~40 GB on disk just to use a tiny VAE (~250 MB) plus the encoder (~16 GB), and re-downloading both for every model variant. The smallest fully-standalone setup with this PR drops to ~12 GB (GGUF transformer + ~250 MB VAE + ~7 GB ComfyUI fp8 encoder).

How:

Backend

• VAE checkpoint: new VAE_Checkpoint_QwenImage_Config detects single-file Qwen Image VAEs via 5D conv weights + z_dim=16 and loads them via AutoencoderKLQwenImage (init_empty_weights + load_state_dict). The generic VAE checkpoint matcher now explicitly excludes Qwen Image VAEs so they aren't misclassified as FLUX.
• Qwen2.5-VL encoder (Diffusers folder): new ModelType.QwenVLEncoder + ModelFormat.QwenVLEncoder with QwenVLEncoder_Diffusers_Config recognising directories that contain text_encoder/ (with Qwen2_5_VLForConditionalGeneration / Qwen2VLForConditionalGeneration) + tokenizer/. The new QwenVLEncoderLoader handles Tokenizer and TextEncoder submodel loading from the folder layout.
• Qwen2.5-VL encoder (ComfyUI single-file): new QwenVLEncoder_Checkpoint_Config matches consolidated single-file checkpoints (e.g. qwen_2.5_vl_7b_fp8_scaled.safetensors) by detecting both LM keys (model.embed_tokens / model.layers.*) and visual tower keys (visual.patch_embed.* / visual.blocks.*). The new QwenVLEncoderCheckpointLoader loads the safetensors, dequantises ComfyUI fp8 weights via weight * weight_scale (with block-wise expansion, mirroring the Z-Image Qwen3 loader), strips comfy_quant / weight_scale / scaled_fp8 metadata, fetches the architecture config from Qwen/Qwen2.5-VL-7B-Instruct (offline-cache fallback), and instantiates Qwen2_5_VLForConditionalGeneration via init_empty_weights + assign load. Tokenizer comes from the same HF repo with offline fallback.
• Text encoder invocation: qwen_image_text_encoder.py now branches on whether model_root is a file. Single-file checkpoints get tokenizer + image processor from HuggingFace (Qwen/Qwen2.5-VL-7B-Instruct, ~10 MB, cached); the existing folder layout path is unchanged. BnB-quantised loading falls back to the cached encoder for single-file checkpoints since BnB can't load from a bare safetensors and the file is already FP8.
• Loader invocation: QwenImageModelLoaderInvocation gains optional vae_model and qwen_vl_encoder_model fields. Resolution priority for each component: standalone override → main model (if Diffusers) → Component Source. Bumped to v1.2.0.
• Starter models: three new starter entries — Qwen Image VAE (single-file checkpoint, ~250 MB), Qwen2.5-VL Encoder (fp8 scaled) (ComfyUI single-file, ~7 GB), and Qwen2.5-VL Encoder (Diffusers) (multi-folder HF download text_encoder+tokenizer+processor, ~16 GB). All 8 GGUF main starters (Q2_K / Q4_K_M / Q6_K / Q8_0 for both Edit and txt2img) declare the VAE + fp8 encoder as dependencies, so installing any of them auto-installs a complete generation-ready setup. The Qwen Image starter bundle gets the VAE and fp8 encoder prepended too.

Frontend

• New params state, selectors and reducers for qwenImageVaeModel and qwenImageQwenVLEncoderModel, plus a migration entry.
• Combobox UI added alongside the existing Component Source picker (single component now renders all three: VAE / Qwen2.5-VL Encoder / Component Source).
• New useQwenImageVAEModels / useQwenVLEncoderModels hooks, isQwenImageVAEModelConfig / isQwenVLEncoderModelConfig type guards, and Model Manager category + format badge entries.
• Graph builder passes the new fields to the loader node.
• Readiness check for GGUF Qwen Image now allows either a standalone source or a Component Source for the VAE and encoder independently.
• schema.ts patched manually for the new ModelType / ModelFormat values, the QwenVLEncoder_Diffusers_Config and QwenVLEncoder_Checkpoint_Config schemas, the new loader fields, and the AnyModelConfig union.

Related Issues / Discussions

Follow-up to #9000 (Qwen Image full pipeline support). Closes the standalone-component gap that was called out for users with limited disk space.

QA Instructions

Quickest verification (recommended):
Install one of the GGUF starter models (e.g. Qwen Image Edit 2511 (Q4_K_M)) from the starter list. The VAE and fp8 encoder should be auto-installed as dependencies, and the model should generate without any further configuration.

Setup options for manual testing:

1. VAE only: install Qwen Image VAE from the starter list (or download vae/diffusion_pytorch_model.safetensors from a Qwen Image HF repo manually, ~250 MB). Verify it's identified as a Qwen Image VAE checkpoint.
2. Encoder folder: install Qwen2.5-VL Encoder (Diffusers) from the starter list (or download text_encoder/ + tokenizer/ (+ optionally processor/) from Qwen/Qwen-Image-Edit-2511 manually). Verify it's identified as qwen_vl_encoder / qwen_vl_encoder.
3. Encoder single-file: install Qwen2.5-VL Encoder (fp8 scaled) from the starter list (or qwen_2.5_vl_7b_fp8_scaled.safetensors directly, ~7 GB). Verify it's identified as qwen_vl_encoder / checkpoint. First generation will fetch the tokenizer + processor configs from Qwen/Qwen2.5-VL-7B-Instruct (~10 MB) and cache them.
4. Full standalone setup: GGUF transformer + standalone VAE + standalone Qwen2.5-VL encoder (folder or single-file), with no Component Source set — should generate successfully.

Cases to verify on the Qwen Image generation tab:

• Diffusers main model, no overrides → all submodels come from main (existing behaviour).
• Diffusers main model + standalone VAE → VAE override is used, encoder still from main.
• GGUF main + Component Source only → unchanged behaviour, still works.
• GGUF main + standalone VAE + Component Source → VAE from standalone, encoder from Component Source.
• GGUF main + standalone VAE + standalone Encoder (folder), no Component Source → both come from the standalone models, no error.
• GGUF main + standalone VAE + ComfyUI single-file Encoder, no Component Source → generates after first-time tokenizer/processor download.
• GGUF main with neither standalone Encoder nor Component Source → readiness check blocks generation with a clear reason.
• Quantized encoder (int8 / nf4) still works against a standalone encoder folder. Single-file Encoder + int8 / nf4 falls back to the cached non-BnB path (still works, no error).

Starter model checks:

• Starter list shows three new entries under model components: Qwen Image VAE, Qwen2.5-VL Encoder (fp8 scaled), Qwen2.5-VL Encoder (Diffusers).
• Installing any GGUF Qwen Image starter (Q2_K / Q4_K_M / Q6_K / Q8_0, Edit or txt2img) also auto-installs the VAE and fp8 encoder.

Automated checks:

• pytest tests/app/invocations/test_qwen_image_model_loader.py tests/backend/model_manager/configs/ — 16 passed.
• pytest -k "qwen_image" (excluding unrelated PIL get_flattened_data test) — 53 passed.
• Frontend: pnpm lint:tsc / pnpm lint:eslint / pnpm lint:prettier / pnpm lint:knip all green.

Merge Plan

Standard merge.

Checklist

• The PR has a short but descriptive title, suitable for a changelog
• Tests added / updated (if applicable)
• ❗Changes to a redux slice have a corresponding migration
• Documentation added / updated (if applicable)
• Updated What's New copy (if doing a release after this PR)