[Model] Support SenseNova-U1.5-8B-MoT and its distilled 8-step LoRA (#6516)

Signed-off-by: MrlixiangWE <mrdanaer@gmail.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>

recipes/SenseNova/SenseNova-U1.5.md

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+# SenseNova-U1.5
+
+> Unified image generation and understanding, with an official 8-step distilled LoRA
+
+## Summary
+
+- Vendor: SenseNova
+- Model: `sensenova/SenseNova-U1.5-8B-MoT`
+- LoRA: `sensenova/SenseNova-U1.5-8B-MoT-LoRAs` (`SenseNova-U1.5-8B-MoT-LoRA-8step.safetensors`)
+- Task: text2img, img2img, img2text (visual understanding), text2text (chat)
+- Mode: Offline inference, Online serving (OpenAI-compatible API)
+- Maintainer: Community
+
+## When to use this recipe
+
+U1.5 runs on the same `SenseNovaU1Pipeline` as U1 — the checkpoint keeps
+`model_type: neo_chat`, so it resolves without `--model-class-name`. Relative to U1 the
+config flips two fields, `use_pixel_head` to `true` (the flow-matching head becomes a
+`ConvDecoder`) and `noise_scale_max_value` from 8.0 to 16.0; both are already read by
+`SenseNovaU1Config`. The checkpoint is 13 shards / 50.2 GB on disk, 30.3 GB of that fp32, and
+loads to roughly 34 GB in bf16.
+
+Use this recipe for U1.5 specifically, including its distilled few-step LoRA. For U1 see
+[SenseNova-U1](SenseNova-U1.md).
+
+## Hardware Support
+
+### GPU
+
+#### 1x A800 80GB
+
+- 1x NVIDIA A800 80GB PCIe with an Intel Xeon Gold 6336Y, driver 595.84, Linux
+- Python 3.12.13, vLLM 0.28.0, torch 2.13.0+cu130, CUDA 13.0, diffusers 0.40.0,
+  transformers 5.14.1, flashinfer 0.6.16.post3, BF16, TP=1
+- Peak GPU memory: 34.3 GB at 1024x1024, 36.4 GB at 1536x2720
+
+##### Text-to-Image
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+    --model sensenova/SenseNova-U1.5-8B-MoT \
+    --prompt "Close portrait of an elderly woman by a farmhouse window, warm natural light." \
+    --width 1024 --height 1024 \
+    --seed 42 --num-inference-steps 50 --cfg-scale 4.0 \
+    --extra-body '{"think": false, "cfg_norm": "none", "timestep_shift": 3.0, "t_eps": 0.02}' \
+    --output sensenova_u15_t2i.png
+```
+
+Think mode (`"think": true`) is recommended for higher image quality.
+
+##### Image-to-Image Editing
+
+```bash
+python examples/offline_inference/image_to_image/image_edit.py \
+    --model sensenova/SenseNova-U1.5-8B-MoT \
+    --prompt "Turn this into an oil painting" \
+    --image input.png --resolution 1024 \
+    --seed 42 --num-inference-steps 25 --cfg-scale 4.0 \
+    --extra-args '{"think": true, "img_cfg_scale": 1.0, "cfg_norm": "none", "timestep_shift": 3.0}' \
+    --output sensenova_u15_edit.png
+```
+
+##### 8-step distilled LoRA
+
+The distilled LoRA is fused into the generation tower at load time:
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+    --model sensenova/SenseNova-U1.5-8B-MoT \
+    --lora-path SenseNova-U1.5-8B-MoT-LoRA-8step.safetensors --lora-backend distill \
+    --prompt "Close portrait of an elderly woman by a farmhouse window, warm natural light." \
+    --width 1024 --height 1024 \
+    --seed 42 --num-inference-steps 8 --cfg-scale 1.0 \
+    --extra-body '{"think": false, "cfg_norm": "none", "timestep_shift": 3.0, "t_eps": 0.02}' \
+    --output sensenova_u15_lora8.png
+```
+
+**Use `--cfg-scale 1.0` with this LoRA.** It is distilled with DMD and runs without
+classifier-free guidance; the default `4.0` applies guidance twice and produces a
+blown-out, posterised image.
+
+##### Online serving
+
+```bash
+vllm serve sensenova/SenseNova-U1.5-8B-MoT --omni --port 8091
+
+python examples/online_serving/sensenova_u1/openai_chat_client.py \
+    -s http://127.0.0.1:8091 -m img2text -i input.png -p "Describe this image."
+```
+
+`-s` takes the base URL; the client appends `/v1` itself.
+
+#### Measured latency (1x A800 80GB, 25 steps, median of 3 after a warmup)
+
+| Resolution | Step latency | Total |
+| --- | --- | --- |
+| 1024x1024 | 209.5 ms | 5.24 s |
+| 1536x1536 | 458.5 ms | 11.46 s |
+
+At 1536x2720 with 50 steps, end-to-end is 43.0 s.
+
+#### Measured latency (1x NVIDIA H200 139GB, single run, no warmup)
+
+Reported by @hsliuustc0106 against PR head `29c090d`, on one reserved CUDA device.
+
+- Python 3.12.13, vLLM 0.28.0, torch 2.13.0+cu130, CUDA 13.0, diffusers 0.40.0,
+  transformers 5.14.1, flashinfer 0.6.16.post3, BF16, TP=1
+- `sensenova/SenseNova-U1.5-8B-MoT` with `SenseNova-U1.5-8B-MoT-LoRA-8step.safetensors`,
+  seed 42, 1024x1024
+
+| Case | Stage latency | Peak GPU memory |
+| --- | --- | --- |
+| 50 steps, cfg 4.0, think off | 9017 ms | 34384 MiB |
+| 8 steps, cfg 1.0, no LoRA control | 624.00 ms, 77.85 ms/step | 34376 MiB |
+| 8 steps, cfg 1.0, distilled LoRA | 623.85 ms, 77.98 ms/step | 34364 MiB |
+| 50 steps, cfg 4.0, think on, `VLLM_OMNI_SENSENOVA_PAGED_DECODE=1` | 81622 ms | 34594 MiB |
+
+Each row runs the Text-to-Image command above with `--num-inference-steps` and
+`--cfg-scale` as listed and `think` set through `--extra-body`; the LoRA row adds
+`--lora-path` and `--lora-backend distill`.
+
+Single runs without a preceding warmup, so the think-on row carries the first-request
+compile rather than a steady state. The distilled LoRA fused into 168 parameters, and its
+image differs from the same-seed no-LoRA control in 1,048,574 of 1,048,576 pixels, MAE 58.62.
+
+```bash
+pytest -m "cpu and not cuda" tests/diffusion/models/sensenova_u1/ tests/diffusion/lora/test_loader.py -q
+pytest -m "cpu and not cuda" tests/config/test_environment_variables.py tests/diffusion/test_diffusion_worker.py -q
+pytest -m cuda tests/diffusion/models/sensenova_u1/test_sensenova_u1_attention_gqa.py \
+    tests/diffusion/models/sensenova_u1/test_sensenova_u1_paged_decode.py -q --tb=short
+```
+
+71, 13 and 9 passed. Serving reached `/health`; `img2text` returned a 1921-character
+description and `text2text` the expected answer.
+
+#### Verification
+
+```bash
+pytest -q tests/diffusion/models/sensenova_u1/
+```
+
+## Notes
+
+- Both `use_pixel_head` and `noise_scale_max_value` come from `config.json`; no flag is needed.
+- The checkpoint carries no `configuration_neo_chat.py`, so the loader logs a few
+  "does not appear to have a file named configuration_neo_chat.py" errors during startup and
+  then proceeds on the in-tree config. Generation is unaffected.
+- The LoRA targets the generation tower only (`*_mot_gen`); understanding-tower weights are
+  untouched.
+- Autoregressive decode (think, text-to-text and image-to-text) runs on a paged K/V cache under
+  a captured CUDA graph. It falls back to the ordinary cache when the device or the bundled
+  `flash_attn_varlen_func` cannot support it; set `VLLM_OMNI_SENSENOVA_PAGED_DECODE=0` to force
+  that fallback.
+- The first request after startup costs about 0.7 s more than the steady state whether the paged
+  path is on or off. Measured on one A800 with the inductor, triton and vLLM compile caches all
+  cleared, median of three runs: 718 ms above steady with the path on, 679 ms with it off.
+- Each request captures its own graphs, and a think request captures twice because the sequence
+  grows past the 512 bucket, so the capture cost is paid per request rather than once at
+  startup.