diff --git a/docs/diffusion/contributing.md b/docs/diffusion/contributing.md
index cc83b1b56..7de656100 100644
--- a/docs/diffusion/contributing.md
+++ b/docs/diffusion/contributing.md
@@ -2,6 +2,14 @@
 
 This guide outlines the requirements for contributing to the SGLang Diffusion module (`sglang.multimodal_gen`).
 
+## On AI-Assisted ("Vibe Coding") PRs
+
+Vibe-coded PRs are welcome — we judge code quality, not how it was produced. The bar is the same for all PRs:
+
+- **No over-commenting.** If the name says it all, skip the docstring.
+- **No over-catching.** Don't guard against errors that virtually never happen in practice.
+- **Test before submitting.** AI-generated code can be subtly wrong — verify correctness end-to-end.
+
 ## Commit Message Convention
 
 We follow a structured commit message format to maintain a clean history.
diff --git a/python/sglang/multimodal_gen/.claude/.skills/diffusion-perf/SKILL.md b/python/sglang/multimodal_gen/.claude/.skills/diffusion-perf/SKILL.md
new file mode 100644
index 000000000..76ae5ff8f
--- /dev/null
+++ b/python/sglang/multimodal_gen/.claude/.skills/diffusion-perf/SKILL.md
@@ -0,0 +1,81 @@
+---
+name: diffusion-perf
+description: Measure and compare sglang-diffusion performance. Use when benchmarking a model, comparing before/after performance, or generating a perf report for a PR.
+user-invocable: true
+allowed-tools: Bash, Read
+argument-hint: <model-path> [--prompt "..."] [--baseline baseline.json]
+---
+
+# Diffusion Performance Measurement
+
+Measure sglang-diffusion e2e latency via `--perf-dump-path`, then extract or compare results from the JSON dump.
+
+## JSON dump structure
+
+`--perf-dump-path` writes a JSON file with:
+
+```json
+{
+  "total_duration_ms": 14959.11,
+  "steps": [
+    {"name": "TextEncodingStage", "duration_ms": 611.83},
+    {"name": "DenoisingStage", "duration_ms": 14289.46}
+  ],
+  "denoise_steps_ms": [
+    {"step": 0, "duration_ms": 240.5},
+    {"step": 1, "duration_ms": 279.1}
+  ],
+  "commit_hash": "abc123",
+  "timestamp": "...",
+  "memory_checkpoints": {}
+}
+```
+
+Key fields:
+- `total_duration_ms` — e2e walltime (warmup excluded when `--warmup` is used)
+- `steps` — per-stage breakdown
+- `denoise_steps_ms` — per denoising step timing
+
+## Workflow
+
+### 1. Single measurement
+
+```bash
+sglang generate --model-path $MODEL --prompt "$PROMPT" --warmup --perf-dump-path result.json
+```
+
+Then read `total_duration_ms` from `result.json`.
+
+### 2. Before/after comparison
+
+```bash
+# Baseline (on main branch or before changes)
+sglang generate --model-path $MODEL --prompt "$PROMPT" --warmup --perf-dump-path baseline.json
+
+# New (after changes)
+sglang generate --model-path $MODEL --prompt "$PROMPT" --warmup --perf-dump-path new.json
+
+# Compare — outputs a Markdown table suitable for PR descriptions
+python python/sglang/multimodal_gen/benchmarks/compare_perf.py baseline.json new.json
+```
+
+### 3. Extracting a single number
+
+To get e2e latency in seconds from a dump:
+
+```bash
+python3 -c "import json; print(f\"{json.load(open('result.json'))['total_duration_ms']/1000:.2f}\")"
+```
+
+## Arguments
+
+If `$ARGUMENTS` is provided, parse it as:
+- First positional arg → `--model-path`
+- `--prompt "..."` → generation prompt (default: `"A curious raccoon"`)
+- `--baseline <file>` → if given, run comparison against this baseline file
+
+## Notes
+
+- Always use `--warmup` for accurate timing (excludes CUDA warmup from measurement).
+- Keep `--prompt` and all server/sampling args identical between baseline and new runs.
+- For PR descriptions, paste the output of `compare_perf.py` directly.
diff --git a/python/sglang/multimodal_gen/.claude/CLAUDE.md b/python/sglang/multimodal_gen/.claude/CLAUDE.md
new file mode 100644
index 000000000..26cb86ae6
--- /dev/null
+++ b/python/sglang/multimodal_gen/.claude/CLAUDE.md
@@ -0,0 +1,108 @@
+# CLAUDE.md — sglang-diffusion (multimodal_gen)
+
+## What is this?
+
+SGLang's diffusion/multimodal generation subsystem. Separate from the LLM runtime (`srt`). Supports 20+ image/video diffusion models (Wan, FLUX, HunyuanVideo, LTX, Qwen-Image, etc.) with distributed inference, LoRA, and multiple attention backends.
+
+## Quick Start
+
+```bash
+# One-shot generation
+sglang generate --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers --prompt "A curious raccoon" --save-output
+
+# Start server
+sglang serve --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers --num-gpus 4
+
+# Python API
+from sglang import DiffGenerator
+gen = DiffGenerator.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
+result = gen.generate(sampling_params_kwargs={"prompt": "A curious raccoon"})
+```
+
+## Architecture
+
+```
+CLI / Python API / HTTP Server (FastAPI + OpenAI-compatible)
+    ↓ ZMQ
+Scheduler (request queue, batching, dispatch)
+    ↓ multiprocessing pipes
+GPU Worker(s) → ComposedPipeline (stages: TextEncode → Denoise → Decode)
+```
+
+### Key Directories
+
+```
+runtime/
+├── entrypoints/        # CLI (generate/serve), HTTP server, Python API (DiffGenerator)
+├── managers/           # scheduler.py, gpu_worker.py
+├── pipelines_core/     # ComposedPipelineBase, stages/, schedule_batch.py (Req/OutputBatch)
+├── pipelines/          # Model-specific pipelines (wan, flux, hunyuan, ltx, qwen_image, ...)
+├── models/             # encoders/, dits/, vaes/, schedulers/
+├── layers/             # attention/, lora/, quantization/
+├── loader/             # Model loading, weight utils
+├── server_args.py      # ServerArgs (all CLI/config params)
+└── distributed/        # Multi-GPU (TP, SP: ulysses/ring)
+configs/
+├── pipeline_configs/    # Per-model pipeline configs
+├── sample/             # SamplingParams
+└── models/             # DiT, VAE, Encoder configs
+```
+
+### Key Classes
+
+| Class | Location | Purpose |
+|-------|----------|---------|
+| `DiffGenerator` | `runtime/entrypoints/diffusion_generator.py` | Python API entry point |
+| `ComposedPipelineBase` | `runtime/pipelines_core/composed_pipeline_base.py` | Pipeline orchestrator (stages) |
+| `Scheduler` | `runtime/managers/scheduler.py` | ZMQ event loop, request dispatch |
+| `GPUWorker` | `runtime/managers/gpu_worker.py` | GPU inference worker |
+| `Req` / `OutputBatch` | `runtime/pipelines_core/schedule_batch.py` | Request/output containers |
+| `ServerArgs` | `runtime/server_args.py` | All config params |
+| `SamplingParams` | `configs/sample/sampling_params.py` | Generation params |
+| `PipelineConfig` | `configs/pipeline_configs/base.py` | Model structure config |
+
+### Key Functions
+
+| Function | Module | Purpose |
+|----------|--------|---------|
+| `build_pipeline()` | `runtime/pipelines_core/__init__.py` | Instantiate pipeline from model_path |
+| `get_model_info()` | `registry.py` | Resolve pipeline + config classes |
+| `launch_server()` | `runtime/launch_server.py` | Start multi-process server |
+
+## Adding a New Model
+
+1. Create pipeline in `runtime/pipelines/` extending `ComposedPipelineBase`
+2. Define stages via `create_pipeline_stages()` (TextEncoding → Denoising → Decoding)
+3. Add config in `configs/pipeline_configs/`
+4. Register in `registry.py` via `register_configs()`
+
+## Multi-GPU
+
+```bash
+# Sequence parallelism (video frames across GPUs)
+sglang serve --model-path ... --num-gpus 4 --ulysses-degree 2 --ring-degree 2
+
+# Tensor parallelism (model layers across GPUs)
+sglang serve --model-path ... --num-gpus 2 --tp-size 2
+```
+
+## Testing
+
+```bash
+# Tests live in test/ subdirectory
+python -m pytest python/sglang/multimodal_gen/test/
+
+# No need to pre-download models — auto-downloaded at runtime
+# Dependencies assumed already installed via `pip install -e "python[diffusion]"`
+```
+
+## Perf Measurement
+
+Look for `Pixel data generated successfully in xxxx seconds` in console output. With warmup enabled, use the line containing `warmup excluded` for accurate timing.
+
+## Env Vars
+
+Defined in `envs.py` (300+ vars). Key ones:
+- `SGLANG_DIFFUSION_ATTENTION_BACKEND` — attention backend override
+- `SGLANG_CACHE_DIT_ENABLED` — enable Cache-DiT acceleration
+- `SGLANG_CLOUD_STORAGE_TYPE` — cloud output storage (s3, etc.)