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Deploy Qwen Image

Image

Catalogue notes · not a new performance benchmark

Qwen Image models from Alibaba Tongyi provide precise text and semantic image editing alongside high-quality generation. They support bilingual Chinese and English prompts for global workflows.

Review a Qwen Image setup

Available Variants (2)

ModelGPUVRAMConfigured estimateAction
Qwen Image Edit 2511
Edit 2511
RTX A600048 GBReview current priceDeploy
Qwen Image Generation
Generate
RTX A600048 GBReview current priceDeploy

These configured estimates include the service fee and configured disk. Review the current quote before starting. Capacity, startup and output quality are not verified by this table.

Requirements

ModelPilot assigns a 48GB cloud GPU to this deployment. Actual local VRAM requirements vary with model variant, precision, quantization, resolution, and workflow settings.

On ModelPilot, deploy on a dedicated cloud GPU (up to 80GB VRAM) with a current quote and spending controls shown before you start.

Includes full ComfyUI environment with custom node support.

Use Cases

  • ✓Image editing and inpainting
  • ✓Bilingual prompt support
  • ✓Text-guided image modification
  • ✓Chinese-language image generation

Related Models

Known Issues & Fixes

Real failures from running Qwen Image and similar pipelines, with the mechanism behind each one.

'Value not in list: gemma_path ... not in []', or the pod hard-exits on a missing tokenizer.model even though you supplied a CLIP/T5

Cause: Newer models replaced CLIP/T5 with a full LLM encoder. LTX-2 19B's LTXVGemmaCLIPModelLoader wants a DIRECTORY encoder: weights come from the non-gated Comfy-Org/ltx-2 repo, but config.json + tokenizer.model come from the GATED google/gemma-3-12b-it repo — without HF token + accepted license the sidecar 403s and the loader sees an empty list.

Fix: Supply HF token + accept the Gemma license so the gated config/tokenizer sidecar downloads into the encoder directory. Note LTX-2.3's default graph instead uses a single-file FP4 encoder (gemma_3_12B_it_fp4_mixed, non-gated, no tokenizer sidecar).

A v2 workflow fails to load its encoder, or a user reuses v1 encoders and gets nothing

Cause: FLUX.2 dropped FLUX.1's dual T5-XXL+CLIP_L for a single Mistral-3-Small encoder (18GB fp8 / 36GB bf16) — and the smaller Klein 4B/9B variants use a Qwen-3 encoder instead. The encoder stack isn't stable across versions or even size tiers of one release.

Fix: Match encoder files to the exact variant (mistral*3_small_flux2 for FLUX.2 dev; qwen_3*\* for Klein). Don't reuse the previous version's encoders.

'Value not in list' on ONE encoder even though the others are present, or conditioning is silently broken

Cause: Encoder count is model-specific and modern models pair a small CLIP with a large, easy-to-forget LLM/VL encoder: HiDream's QuadrupleCLIPLoader needs FOUR (incl. llama-3.1-8b fp8_scaled ~8.5GB); HunyuanVideo needs DUAL (clip_l + llava_llama3, +vision for i2v); Qwen-Image needs Qwen-2.5-VL-7B (~7GB fp8 to ~16GB bf16); Z-Image needs Qwen-3-4B (8GB). A 'not in list' error usually means you shipped N-1.

Fix: Ship the full encoder set for the exact model; the missing piece is almost always the large LLM/VL encoder, not the CLIP.

User expects an anime specialist but gets photorealism, or mis-sizes VRAM

Cause: Z-Image Turbo is a photoreal-leaning generalist, not an anime model; Z-Anime (SeeSee21 fine-tune) is a separate community model on the identical architecture and loads via the same zimage deploy path. Encoder architecture is Qwen-3-4B, VAE = Flux's ae.safetensors (335MB). fp8 diffusion 6.15GB (fits 12GB), bf16 12.3GB; GGUF Q3-Q8 for low VRAM.

Fix: A Turbo base and its fine-tunes share architecture + VAE (and the Qwen-3-4B encoder architecture — a fine-tune may ship its own tuned encoder weights, e.g. Z-Anime's qwen_3_4b-engineer-v4-fp8), so one zimage deploy path serves both.

A workflow that rendered perfectly once produces a BLACK VOID / garbage on a later redeploy — identical JSON, config, and GPU

Cause: Unpinned by default: models pull from floating HF /resolve/main (branch HEAD), custom nodes are git-clone-latest (no SHA), Docker tags are reused, and only ~95% file-size is validated, not content hash. An upstream re-upload or newer node code silently changed bytes/behavior behind unchanged names.

Fix: Pin weights to a commit/revision, nodes to a SHA, images to a digest. Verify a pinned HF URL with `curl -sI -r 0-0 -A 'Mozilla/5.0' -L '<url>'` — MUST include -L (HF /resolve/ 302-redirects to a CDN; without -L you see 302 not 206).

Workflow won't instantiate: 'the following node types were not found: BlockifyMask' despite the node repo being installed

Cause: The node was pinned to a commit predating when the required node class was added upstream. (The inverse also bites: a too-NEW pin can fail to import, forcing a rollback to an older release.)

Fix: Two-sided constraint: pin new enough that EVERY referenced node class is registered, but NOT so new it needs a ComfyUI newer than the baked image. Verify the pinned commit's NODE_CLASS_MAPPINGS registers all referenced classes AND imports cleanly against the deployed ComfyUI.

Hitting a specific error message? Browse ComfyUI error fixes.

Frequently Asked Questions

How much GPU memory is allocated for Qwen Image?

The listed ModelPilot deployment uses a 48GB cloud GPU. Local memory needs can vary with precision, quantization, and workflow settings.

How much does it cost to run Qwen Image?

Pricing varies by hardware and storage. Review the current quote and spending controls before starting.

How long does Qwen Image take to deploy?

Startup depends on capacity, image and model downloads, and runtime readiness. The deployment page reports progress; a catalogue entry does not guarantee a startup time.

Can I run Qwen Image on my local GPU?

It depends on the selected variant, precision, quantization, and workflow settings. Compare the variants below with your available VRAM; the table shows ModelPilot's cloud GPU allocation, not a universal local minimum.

Ready to deploy Qwen Image?

Pick a configuration, review its dependencies, licence, current quote and spending controls, then choose whether to start.