SenseNova-U1.5-8B-MoT - SGLang Documentation

1. Model Introduction

SenseNova-U1.5-8B-MoT is a text-to-image model in the SenseNova-U1 family. It is useful for high-resolution poster, infographic, and enterprise visual-generation workloads where prompt following, layout structure, and Chinese text rendering are important. Built on NEO-Unify, this release improves composition, color harmony, material rendering, and local visual detail for native high-resolution generation. It also focuses on clearer Chinese and English text rendering, stronger infographic layout structure, and more reliable execution of complex prompts with object counts, spatial relationships, styles, and multiple constraints.

2. SGLang-diffusion Installation

SGLang-diffusion offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements. Please refer to the official SGLang-diffusion installation guide for installation instructions.

3. Model Deployment

3.1 Offline Image Generation

SenseNova-U1.5-8B-MoT is a text-to-image model. You can run a single offline generation job with sglang generate:

Command

sglang generate \
  --model-path sensenova/SenseNova-U1.5-8B-MoT \
  --prompt "A cinematic mountain lake at sunrise, realistic photography." \
  --width 2048 \
  --height 2048 \
  --guidance-scale 4.0 \
  --num-inference-steps 50 \
  --seed 42 \
  --output-file-path output_sensenova_u15.png

3.2 Recommended Resolutions

SenseNova-U1 was trained around the 2K-pixel resolution tier. The SGLang pipeline only supports positive --width and --height values that are divisible by 32, and generation quality may degrade for untrained size combinations. Recommended resolution tiers:

Aspect ratio	Resolution
1:1	`2048 x 2048`
16:9 / 9:16	`2720 x 1536` / `1536 x 2720`
3:2 / 2:3	`2496 x 1664` / `1664 x 2496`
4:3 / 3:4	`2368 x 1760` / `1760 x 2368`
2:1 / 1:2	`2880 x 1440` / `1440 x 2880`
3:1 / 1:3	`3456 x 1152` / `1152 x 3456`
9:16 4K	`2304 x 4096`

4. API Usage

For complete API documentation, please refer to the official API usage guide.

4.1 Generate an Image

Example

import base64
from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:30000/v1")

response = client.images.generate(
    model="sensenova/SenseNova-U1.5-8B-MoT",
    prompt="A clean technology poster with structured layout and clear typography.",
    n=1,
    response_format="b64_json",
)

image_bytes = base64.b64decode(response.data[0].b64_json)
with open("output_sensenova_u15.png", "wb") as f:
    f.write(image_bytes)

5. Advanced usage

5.1 Dynamic request batching

Start the server with dynamic batching enabled:

Command

sglang serve \
  --model-path sensenova/SenseNova-U1.5-8B-MoT \
  --port 30000 \
  --batching-max-size 2 \
  --batching-delay-ms 100

Submit requests concurrently so they arrive within the batching delay. Requests in one batch must use the same resolution, inference steps, guidance settings, and output options. Requests with multiple outputs or think_mode=true are executed sequentially.

5.2 Ascend NPU optimizations

On Ascend NPU, SenseNova-U1 automatically uses fused inference attention, RMSNorm, and SwiGLU MLP operators for supported inputs. FIA and SwiGLU fall back when their requirements are not met. Run one complete warmup request before benchmarking because the fused MLP packs its gate and up projection weights on first use.

6. Benchmark

6.1 Speedup benchmark

6.1.1 Single-run profile

NVIDIA A800
Ascend 910C

This is a single-run profile for smoke validation rather than a formal benchmark. Environment: NVIDIA A800; workload: vbench text-to-image, 1 prompt, concurrency 1, 1 output per prompt; warmup: none; repetitions: 1; dispersion: not reported; baseline: none; revisions: PR checkout and resolved model snapshot at run time; output validation: request completed and returned one image.Server Command:

Command

sglang serve \
  --model-path sensenova/SenseNova-U1.5-8B-MoT \
  --port 30000

Benchmark Command:

Command

python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
  --dataset vbench \
  --task text-to-image \
  --num-prompts 1 \
  --max-concurrency 1 \
  --port 30000

Result:

Output

================= Serving Benchmark Result =================
Task:                                         text-to-image
Model:                                        /workspace/models/ModelScope/SenseNova/SenseNova-U1.5-8B-MoT
Dataset:                                      vbench
--------------------------------------------------
Benchmark duration (s):                       13.10
Request rate:                                 inf
Max request concurrency:                      1
Successful requests:                          1/1
Completed outputs:                            1
Outputs per prompt:                           1
--------------------------------------------------
Request throughput (req/s):                   0.08
Output throughput (outputs/s):                0.08
Latency Mean (s):                             13.10
Latency Median (s):                           13.10
Latency P90 (s):                              13.10
Latency P95 (s):                              13.10
Latency P99 (s):                              13.10
--------------------------------------------------
Peak Memory Max (MB):                         34326.00
Peak Memory Mean (MB):                        34326.00
Peak Memory Median (MB):                      34326.00
------------------------------------------------------------

Environment: one Ascend 910C; workload: four concurrent 2048 x 2048 text-to-image requests, 50 denoising steps, CFG 4, BF16.

Batch size	NPU operators	Duration (s)	Throughput (images/s)	Mean latency (s)	Peak reserved memory (MB)
1	Disabled	234.28	0.01707	146.47	35698
2	Disabled	234.83	0.01703	176.06	37870
1	FIA + RMSNorm + SwiGLU	205.94	0.01942	128.80	35718
2	FIA + RMSNorm + SwiGLU	195.02	0.02051	146.33	37850

Optimized B1 improves throughput by 13.76% over unoptimized B1. Optimized B2 improves throughput by 20.42% over unoptimized B2 and by 5.60% over optimized B1.

​1. Model Introduction

​2. SGLang-diffusion Installation

​3. Model Deployment

​3.1 Offline Image Generation

​3.2 Recommended Resolutions

​4. API Usage

​4.1 Generate an Image

​5. Advanced usage

​5.1 Dynamic request batching

​5.2 Ascend NPU optimizations

​6. Benchmark

​6.1 Speedup benchmark

​6.1.1 Single-run profile

1. Model Introduction

2. SGLang-diffusion Installation

3. Model Deployment

3.1 Offline Image Generation

3.2 Recommended Resolutions

4. API Usage

4.1 Generate an Image

5. Advanced usage

5.1 Dynamic request batching

5.2 Ascend NPU optimizations

6. Benchmark

6.1 Speedup benchmark

6.1.1 Single-run profile