[diffusion] model: support minimax-h3 (#33275)

Co-authored-by: zhenaozhenfu <zhenaozhenfu@minimaxi.com>
Co-authored-by: BBuf <1182563586@qq.com>
Co-authored-by: andyluo7 <andy.luo@amd.com>
Co-authored-by: Zijie Xia <zijie_xia@icloud.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: chao-xue <877184285@qq.com>
Co-authored-by: Cursor <cursoragent@cursor.com>

docs_new/docs/sglang-diffusion/api/cli.mdx

@@ -75,15 +75,17 @@ Use `sglang generate --help` and `sglang serve --help` for the full argument lis
 ### Model and runtime
 
 - `--model-path {MODEL}`: model path or Hugging Face model ID
+- `--model-variant {NAME}`: semantic checkpoint variant to load when one model repository contains multiple weight partitions. The pipeline maps this stable name to the repository layout before loading; for example, MiniMax-H3 accepts `fl2va` and `ref2va`. This is a server/load-time choice, unlike a request's `task`.
+- `--model-subfolder {PATH}`: advanced direct override for a component subfolder inside the model repository. Prefer `--model-variant` when the pipeline exposes semantic routing. If both are supplied, they must resolve to the same weight partition.
 - `--lora-path {PATH}` and `--lora-nickname {NAME}`: load a LoRA adapter
 - `--lora-merge-mode {auto|merge|dynamic}`: choose how LoRA is applied. `auto` statically merges regular weights and uses dynamic LoRA for FSDP-sharded weights to avoid full-gather peaks.
 - `--num-gpus {N}`: number of GPUs to use
-- `--performance-mode {manual|auto|speed|memory}` / `--mode`: preset for latency/throughput and memory defaults. `auto` is the default and keeps safe offload defaults, using FSDP only for validated DiT-offload replacement paths; `speed` also enables `--enable-torch-compile` by default unless you explicitly disable it. Use `manual` to keep performance-related server args under explicit user control. Explicit offload, FSDP, and parallelism flags take precedence in all modes.
+- `--performance-mode {manual|auto|speed|memory}` / `--mode`: preset for latency/throughput and memory defaults. `auto` is the default and keeps safe offload defaults, using FSDP only for validated DiT-offload replacement paths; `speed` also enables `--enable-torch-compile` unless the model-specific deployment config opts out or you explicitly disable it. Use `manual` to keep performance-related server args under explicit user control. Explicit offload, FSDP, and parallelism flags take precedence in all modes.
 - `--tp-size {N}`: tensor parallelism size, mainly for encoders
 - `--sp-degree {N}`: sequence parallelism size
 - `--ulysses-degree {N}` and `--ring-degree {N}`: USP parallelism controls
 - `--enable-cfg-parallel {true|false}`: enable or explicitly disable CFG parallelism
-- `--encoder-parallel {auto|fold|dp|replicate}`: how the text/image encoders use the GPUs the DiT replica leaves idle during encoding. `auto` (the default for `generate`) TP-folds an encoder wide enough to pay for the per-layer all-reduce and replicates the rest; `fold` forces the shard whenever the dims allow it; `dp` splits a batched encode across ranks (needs `--batching-max-size > 1` to engage, and is the `serve` default); `replicate` encodes redundantly on every rank. `fold` and `replicate` are bitwise-identical to single-GPU encoding. See [Encoder Parallelism](/docs/sglang-diffusion/encoder_parallel).
+- `--encoder-parallel {auto|fold|dp|replicate}`: how the text/image encoders use the GPUs the DiT replica leaves idle during encoding. `auto` (the default for both `generate` and `serve`) TP-folds an encoder wide enough to pay for the per-layer all-reduce, selects DP for a server batch when it can engage, and otherwise replicates; `fold` forces the shard whenever the dims allow it; `dp` splits a batched encode across ranks and needs `--batching-max-size > 1` to engage; `replicate` encodes redundantly on every rank. `fold` and `replicate` are bitwise-identical to single-GPU encoding. See [Encoder Parallelism](/docs/sglang-diffusion/encoder_parallel).
 - `--warmup-mode {off|request|server}`: control startup warmup for `sglang serve`; `off` skips warmup, `request` primes the request path, and `server` runs a full synthetic server warmup before serving traffic
 - `--enable-torch-compile {true|false}`: compile native diffusion hot paths. When no warmup mode is configured, this also enables server warmup so first real requests do not pay compile latency.
 - `--offload-during-compile {true|false}`: when compile warmup is active, temporarily layerwise-offload DiT weights and move resident non-DiT components off-device so `max-autotune` fits on tighter-memory GPUs; the configured serving residency is restored before real traffic. Skipped under existing layerwise offload, Cache-DiT, or FSDP.
@@ -102,6 +104,8 @@ Use `sglang generate --help` and `sglang serve --help` for the full argument lis
 - `--prompt {PROMPT}` and `--negative-prompt {PROMPT}`
 - `--image-path {PATH} [{PATH} ...]`: input image(s) for image-to-video or image-to-image generation
 - `--num-inference-steps {STEPS}` and `--seed {SEED}`
+- `--num-outputs-per-prompt {N}` / `--num-outputs {N}`: generate multiple outputs for each prompt. A scalar seed expands as `seed + output_index`.
+- `--quality {PROFILE}`: select a model-owned request quality/performance profile. Supported names and deployment constraints are model-specific.
 - `--height {HEIGHT}`, `--width {WIDTH}`, `--num-frames {N}`, `--fps {FPS}`
 - `--output-path {PATH}`, `--output-file-name {NAME}`, `--save-output`, `--return-frames`
 
@@ -178,7 +182,7 @@ sglang generate \
 HTTP server-only arguments are ignored by `sglang generate`.
 </Note>
 
-For diffusers pipelines, Cache-DiT can be enabled with `SGLANG_CACHE_DIT_ENABLED=true` or `--cache-dit-config`. See [Cache-DiT](../cache_dit).
+For supported pipelines, Cache-DiT can be enabled with `SGLANG_CACHE_DIT_ENABLED=true` or `--cache-dit-config`. See [Cache-DiT](../cache_dit).
 
 For supported image pipelines, breakable CUDA graph can be enabled with `--enable-breakable-cuda-graph`, but you must declare every served resolution in `--warmup-resolutions` so warmup captures matching graph signatures.