Model reference · open weights
Qwen-Image-2512 is an open-weight image model from Qwen. Qwen-Image-2512 (BF16) weighs 57.7 GB; the smallest configuration that runs it is H100 80 GB.
Summary of the Qwen/Qwen-Image-2512 model card, 2026-10-01. The estimate below is for another build of the family.
What it is
| Released by | Qwen |
|---|---|
| Released | 2025-12-30 |
| Parameters | 20.4B |
| VRAM | 57.7 GB for the weights |
What it runs on
| Card | One image | Memory |
|---|---|---|
| RTX 3060 12 GB … L40S 48 GB | does not fit | |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| Qwen-Image-2512 ↗ | 20.4B | BF16 | 57.7 GB | H100 80 GB |
| Qwen-Image-2512-GGUF ↗ packaged by unsloth 15 builds: Q2_K 7.3 GB … F16 40.9 GB the transformer alone, to use with the text encoders and VAE |
— | GGUF | 13.2 GB | RTX 4090 24 GB |
| Qwen-Image-2512 (above) ↗ packaged by unsloth |
20.4B | BF16 | 57.7 GB | H100 80 GB |
| Qwen-Image-2512-unsloth-bnb-4bit ↗ packaged by unsloth |
10.9B | INT4 | 18.7 GB | RTX 4090 24 GB |
From the model card
💜 Qwen Chat   |   🤗 Hugging Face   |   🤖 ModelScope   |    📑 Tech Report    |    📑 Blog    🖥️ Demo   |   💬 WeChat (微信)   |   🫨 Discord  |    Github  
We are excited to introduce Qwen-Image-2512, the December update of Qwen-Image’s text-to-image foundational model. You are welcome to try the latest model at Qwen Chat. Compared to the base Qwen-Image model released in August, Qwen-Image-2512 features the following key improvements:
We conducted over 10,000 rounds of blind model evaluations on AI Arena, and the results show that Qwen-Image-2512 is currently the strongest open-source model—while remaining highly competitive even among closed-source models.
Install the latest version of diffusers
pip install git+https://github.com/huggingface/diffusers
The following contains a code snippet illustrating how to use Qwen-Image-2512:
from diffusers import DiffusionPipeline
import torch
model_name = "Qwen/Qwen-Image-2512"
# Load the pipeline
if torch.cuda.is_available():
torch_dtype = torch.bfloat16
device = "cuda"
else:
torch_dtype = torch.float32
device = "cpu"
pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype).to(device)
# Generate image
prompt = '''A 20-year-old East Asian girl with delicate, charming features and large, bright brown eyes—expressive and lively, with a cheerful or subtly smiling expression. Her naturally wavy long hair is either loose or tied in twin ponytails. She has fair skin and light makeup accentuating her youthful freshness. She wears a modern, cute dress or relaxed outfit in bright, soft colors—lightweight fabric, minimalist cut. She stands indoors at an anime convention, surrounded by banners, posters, or stalls. Lighting is typical indoor illumination—no staged lighting—and the image resembles a casual iPhone snapshot: unpretentious composition, yet brimming with vivid, fresh, youthful charm.'''
negative_prompt = "低分辨率,低画质,肢体畸形,手指畸形,画面过饱和,蜡像感,人脸无细节,过度光滑,画面具有AI感。构图混乱。文字模糊,扭曲。"
# Generate with different aspect ratios
aspect_ratios = {
"1:1": (1328, 1328),
"16:9": (1664, 928),
"9:16": (928, 1664),
"4:3": (1472, 1104),
"3:4": (1104, 1472),
"3:2": (1584, 1056),
"2:3": (1056, 1584),
}
width, height = aspect_ratios["16:9"]
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
num_inference_steps=50,
true_cfg_scale=4.0,
generator=torch.Generator(device="cuda").manual_seed(42)
).images[0]
image.save("example.png")
Enhanced Huamn Realism
In Qwen-Image-2512, human depiction has been substantially refined. Compared to the August release, Qwen-Image-2512 adds significantly richer facial details and better environmental context. For example:
A Chinese female college student, around 20 years old, with a very short haircut that conveys a gentle, artistic vibe. Her hair naturally falls to partially cover her cheeks, projecting a tomboyish yet charming demeanor. She has cool-toned fair skin and delicate features, with a slightly shy yet subtly confident expression—her mouth crooked in a playful, youthful smirk. She wears an off-shoulder top, revealing one shoulder, with a well-proportioned figure. The image is framed as a close-up selfie: she dominates the foreground, while the background clearly shows her dormitory—a neatly made bed with white linens on the top bunk, a tidy study desk with organized stationery, and wooden cabinets and drawers. The photo is captured on a smartphone under soft, even ambient lighting, with natural tones, high clarity, and a bright, lively atmosphere full of youthful, everyday energy.
For the same prompt, Qwen-Image-2512 yields notably more lifelike facial features, and background objects—e.g., the desk, stationery, and bedding—are rendered with significantly greater clarity than in Qwen-Image.
A 20-year-old East Asian girl with delicate, charming features and large, bright brown eyes—expressive and lively, with a cheerful or subtly smiling expression. Her naturally wavy long hair is either loose or tied in twin ponytails. She has fair skin and light makeup accentuating her youthful freshness. She wears a modern, cute dress or relaxed outfit in bright, soft colors—lightweight fabric, minimalist cut. She stands indoors at an anime convention, surrounded by banners, posters, or stalls. Lighting is typical indoor illumination—no staged lighting—and the image resembles a casual iPhone snapshot: unpretentious composition, yet brimming with vivid, fresh, youthful charm.
Here, hair strands serve as a key differentiator: Qwen-Image’s August version tends to blur them together, losing fine detail, whereas Qwen-Image-2512 renders individual strands with precision, resulting in a more natural and realistic appearance.
Another case:
An East Asian teenage boy, aged 15–18, with soft, fluffy black short hair and refined facial contours. His large, warm brown eyes sparkle with energy. His fair skin and sunny, open smile convey an approachable, friendly demeanor—no makeup or blemishes. He wears a blue-and-white summer uniform shirt, slightly unbuttoned, made of thin breathable fabric, wi
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
Running it yourself
Rent a machine by the hour. ComfyUI is installed on it. Open ComfyUI through the tunnel, then Templates → Qwen Image 2512: it loads ComfyUI's own build of this model, the files above.
# on your rented machine: pip install diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image-2512", torch_dtype=torch.bfloat16).to("cuda")
image = pipe(prompt="a red bicycle on a cobbled street").images[0]
image.save("/workspace/out.png")
# on your rented machine (the ssh line is on its page in the console)
# get REPO FILE FOLDER: one file into /workspace/models/FOLDER, where ComfyUI loads it from
get() { hf download "$1" "$2" --local-dir /workspace/hf-files && mkdir -p "/workspace/models/$3" && mv "/workspace/hf-files/$2" "/workspace/models/$3/$4"; }
# the files of the template “Qwen Image 2512” (29.6 GB)
get Comfy-Org/Qwen-Image_ComfyUI split_files/text_encoders/qwen_2.5_vl_7b_fp8_scaled.safetensors text_encoders
get Comfy-Org/Qwen-Image_ComfyUI split_files/vae/qwen_image_vae.safetensors vae
get Comfy-Org/Qwen-Image_ComfyUI split_files/diffusion_models/qwen_image_2512_fp8_e4m3fn.safetensors diffusion_models
get lightx2v/Qwen-Image-2512-Lightning Qwen-Image-2512-Lightning-4steps-V1.0-fp32.safetensors loras
start-comfyui
# on your computer, in a second terminal: ComfyUI in your browser at http://localhost:8188
# HOST and PORT are your machine's, from its page in the console
ssh -L 8188:localhost:8188 dev@HOST -p PORT