Deploy Qwen-Image-2512
ImageQwen-Image-2512 is the #1 ranked open-source image generation model. It excels at realistic human faces, accurate text rendering, and complex compositions. Built by Alibaba's Tongyi Lab.
Deploy Qwen-Image-2512 in minutes
Starting at $0.72/hr on dedicated GPU
Specifications
| Model | GPU | VRAM | Price | Action |
|---|---|---|---|---|
Qwen-Image-2512 2512 (Latest) | RTX A6000 | 48 GB | $0.72/hr | Deploy |
Prices include the service fee. Charges follow actual running time.
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) starting at $0.72/hr with no setup required.
Compare Qwen-Image-2512
Source-backed GPU, VRAM, and cost comparisons for nearby deployment choices.
Qwen Image 2512 (Latest) vs FLUX.2 Klein 9B FP8 (Recommended)
Compare Qwen Image 2512 (Latest) against FLUX.2 Klein 9B FP8 (Recommended) by GPU tier, VRAM, and base hourly cost.
Qwen Image 2512 (Latest) vs Flux Kontext Dev FP8 (Character Consistency)
Compare Qwen Image 2512 (Latest) against Flux Kontext Dev FP8 (Character Consistency) by GPU tier, VRAM, and base hourly cost.
Qwen Image 2512 (Latest) vs FLUX.2 Dev Full (64GB)
Compare Qwen Image 2512 (Latest) against FLUX.2 Dev Full (64GB) by GPU tier, VRAM, and base hourly cost.
Qwen Image 2512 (Latest) vs Z Image Turbo
Compare Qwen Image 2512 (Latest) against Z Image Turbo by GPU tier, VRAM, and base hourly cost.
Qwen Image 2512 (Latest) vs Z Anime Distill 8-step AIO
Compare Qwen Image 2512 (Latest) against Z Anime Distill 8-step AIO by GPU tier, VRAM, and base hourly cost.
Use Cases
- ✓Marketing and advertising visuals
- ✓Product photography
- ✓Text-heavy graphic design
- ✓Realistic portrait generation
Related Models
Known Issues & Fixes
Real failures from running Qwen-Image-2512 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-2512?
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-2512?
Starting at $0.72/hr on a dedicated GPU. Charges are calculated from actual running time, with auto-stop when credits run out.
How long does Qwen-Image-2512 take to deploy?
Most deployments complete in 10–20 minutes including model download and environment setup.
Can I run Qwen-Image-2512 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.
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