Built with Qwen. Non-commercial research and evaluation use only (Qwen Research License, see LICENSE and NOTICE). An NVFP4 quantization of Qwen/Qwen-Image-2.1, made from the official BF16 weights with SVDQuant + GPTQ and calibrated on both text-to-image prompts and image edits. Both large components run on native Blackwell FP4 tensor cores, and the pipeline loads with a single DiffusionPipeline.frompretrained call. At 512x512 on an RTX PRO 6000 Blackwell it is about 1.7x faster than BF16 and needs about 40% less VRAM, and it matches BF16 closely on text-to-image, typography and edits, including a hard recolor edit that earlier 4-bit text encoders failed. trustremotecode=True loads two small…
qwen-image-2.1-nf4 is an open-weight model for text to image from Summer Summer, released under other. It has 3.7B parameters. At 16-bit it needs about 8.8 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
A complete Hugging Face pipeline bundle with the Qwen-Image-2.1 DiT in NF4 and a compact Qwen3.5-0.8B NF4 student text encoder. A learned MLP adapter maps Qwen3.5's 1024-dimensional token features into Qwen-Image's 4096-dimensional text-conditioning space.
Parameters3.7B
Context—
Weights5.9 GB
Licenseother
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve qwen-image-2.1-nf4 (3.7B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 7.3 GB | 8.8 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 3.7 GB | 4.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 1.8 GB | 2.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
qwen-image-2.1-nf4 on every accelerator the SAVRN Index prices, at every precision
Model Card
A complete Hugging Face pipeline bundle with the Qwen-Image-2.1 DiT in NF4 and a compact Qwen3.5-0.8B NF4 student text encoder. A learned MLP adapter maps Qwen3.5's 1024-dimensional token features into Qwen-Image's 4096-dimensional text-conditioning space. VAE, scheduler, Qwen3-VL-compatible pipeline processor, student tokenizer, adapter, inference helper, and a generated sample are included. The adapter was distilled from the pinned Qwen-Image Qwen3-VL teacher on 192 synthetic training prompts and 32 validation prompts. Validation token-embedding cosine similarity reached 0.9392. This is an embedding alignment metric, not a guarantee of image-quality parity; the included sample is an…
Excerpt from the card by Summer Summer, licensed other.
Identity and Version
- Repository
- summerMC/qwen-image-2.1-nf4
- Publisher
- Summer Summer
- Task
- Text to image
- Modality
- Image
- Library
- diffusers
- Parameters
- 3.7B parameters
- Languages
- Not stated by the source
- Revision
- d1840178b1960a95a665c65d4e04a32daa957451
- First published
- 2026-09-26
- Last updated
- 2026-09-27
Files and Weights
38 files, 5.9 GB in total. The weights are 4 files totalling 5.9 GB in safetensors.
Weights4 files · 5.9 GB
Configuration23 files · 39.4 KB
Tokenizer6 files · 35.9 MB
Documentation1 file · 4.0 KB
Other3 files · 961.9 KB
Repository1 file · 1.7 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| qwen35_adapter/adapter.safetensors | Weights | 86.1 MB | 03ca61890c6a |
| text_encoder/model.safetensors | Weights | 766.7 MB | a0f090afeb97 |
| transformer/diffusion_pytorch_model.safetensors | Weights | 3.7 GB | 24bd037116c4 |
| vae/diffusion_pytorch_model.safetensors | Weights | 1.4 GB | a07a1b7c4ee2 |
| acceptance.json | Configuration | 798 B | — |
| artifact_metadata.json | Configuration | 547 B | — |
| benchmark.json | Configuration | 773 B | — |
| benchmark_qwen_nf4_hf.py | Configuration | 157 B | — |
| config.json | Configuration | 841 B | — |
| environment.json | Configuration | 167 B | — |
| inference.py | Configuration | 8.3 KB | — |
| model_index.json | Configuration | 434 B | — |
| processor/added_tokens.json | Configuration | 707 B | — |
| processor/config.json | Configuration | 1.5 KB | — |
| processor/preprocessor_config.json | Configuration | 782 B | — |
| processor/special_tokens_map.json | Configuration | 613 B | — |
| processor/video_preprocessor_config.json | Configuration | 817 B | — |
| qwen35_adapter/config.json | Configuration | 339 B | — |
| qwen35_adapter_training_metrics.json | Configuration | 3.1 KB | — |
| qwen35_inference.py | Configuration | 3.5 KB | — |
| scheduler/scheduler_config.json | Configuration | 485 B | — |
| text_encoder/config.json | Configuration | 2.3 KB | — |
| text_encoder/generation_config.json | Configuration | 116 B | — |
| train_qwen35_adapter.py | Configuration | 8.7 KB | — |
| transformer/config.json | Configuration | 892 B | — |
| vae/config.json | Configuration | 2.1 KB | — |
| validation.json | Configuration | 1.5 KB | — |
| README.md | Documentation | 4.0 KB | — |
| processor/chat_template.jinja | Other | 5.3 KB | — |
| qwen35_tokenizer/chat_template.jinja | Other | 7.8 KB | — |
| sample_768x768_nf4.png | Other | 948.9 KB | 3a267ef4b132 |
| .gitattributes | Repository | 1.7 KB | — |
| processor/merges.txt | Tokenizer | 1.7 MB | — |
| processor/tokenizer.json | Tokenizer | 11.4 MB | aeb13307a71a |
| processor/tokenizer_config.json | Tokenizer | 5.4 KB | — |
| processor/vocab.json | Tokenizer | 2.8 MB | — |
| qwen35_tokenizer/tokenizer.json | Tokenizer | 20.0 MB | 06b9509352d2 |
| qwen35_tokenizer/tokenizer_config.json | Tokenizer | 1.1 KB | — |
License and Download
- License
- other
- Access
- Open weights, no gate
- Download size
- 5.9 GB
Download from Summer Summer
Released by Summer Summer through its official repository on Hugging Face.
Built From
- Derived from Qwen/Qwen-Image-2.1
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 5.9 GB |
| 16-bit | 7.3 GB |
| 8-bit | 3.7 GB |
| 4-bit | 1.8 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About qwen-image-2.1-nf4
How much GPU memory does qwen-image-2.1-nf4 need?
About 8.8 GB at 16-bit and 2.2 GB at 4-bit: the weights (3.7B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run qwen-image-2.1-nf4 on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
What license is qwen-image-2.1-nf4 released under?
other, as its publisher declares it. Read the license text before commercial use.
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