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addon for qvac image/video generation","maintainers":[{"name":"mafintosh","email":"mathiasbuus@gmail.com"},{"name":"prdn","email":"paolo.ardoino@gmail.com"}],"readme":"# diffusion-cpp\n\nNative C++ addon for image, video, interactive world-walk, and ESRGAN\ninference through\n[qvac-ext-stable-diffusion.cpp](https://github.com/tetherto/qvac-ext-stable-diffusion.cpp),\nbuilt for the Bare Runtime.\n\nThe package exposes four JS entry points:\n\n| API                    | Entry point                                 | Use case                                                                        |\n| ---------------------- | ------------------------------------------- | ------------------------------------------------------------------------------- |\n| `ImgStableDiffusion`   | `@qvac/diffusion-cpp`                       | Text-to-image, image-to-image, FLUX.2 reference fusion, optional ESRGAN upscale |\n| `VideoStableDiffusion` | `@qvac/diffusion-cpp/video` or named export | Wan and LTX text-to-video / image-to-video                                      |\n| `WorldStableDiffusion` | `@qvac/diffusion-cpp/world`                 | ABot-World interactive walk: block-by-block generation under keyboard input     |\n| `EsrganUpscaler`       | named export from `@qvac/diffusion-cpp`     | Standalone PNG/JPEG upscaling                                                   |\n\n## Table of Contents\n\n- [Supported Models](#supported-models)\n- [Supported Platforms](#supported-platforms)\n- [Building From Source](#building-from-source)\n- [Downloading Models](#downloading-models)\n- [Examples](#examples)\n- [Image API](#image-api)\n  - [Constructor Files](#constructor-files)\n  - [Image Config](#image-config)\n  - [Image Generation Parameters](#image-generation-parameters)\n- [Image-to-Image and FLUX.2 Fusion](#image-to-image-and-flux2-fusion)\n- [Video API](#video-api)\n  - [Video Files](#video-files)\n  - [Video Parameters](#video-parameters)\n- [LTX-2 Text-to-Video With Audio](#ltx-2-text-to-video-with-audio)\n- [ABot-World Interactive Walk](#abot-world-interactive-walk)\n- [ESRGAN Upscaler](#esrgan-upscaler)\n- [Response Streams and Stats](#response-streams-and-stats)\n- [Cancellation and Unload](#cancellation-and-unload)\n- [Assessing fit](#assessing-fit)\n- [Operational Notes](#operational-notes)\n- [Credits](#credits)\n  - [Test Images](#test-images)\n- [License](#license)\n\n## Supported Models\n\n| Family                       | Mode                                   | Notes                                                                             |\n| ---------------------------- | -------------------------------------- | --------------------------------------------------------------------------------- |\n| SD1.x / SD2.x                | image                                  | All-in-one checkpoints through `files.model`                                      |\n| SDXL                         | image                                  | All-in-one checkpoints or split encoders when required                            |\n| SD3                          | image                                  | Supports split CLIP-L / CLIP-G / T5-XXL inputs                                    |\n| FLUX.2 [klein]               | image, img2img, multi-reference fusion | Split diffusion + Qwen3 LLM + VAE                                                 |\n| Wan 2.1                      | text-to-video, image-to-video          | Single diffusion expert; I2V requires CLIP vision                                 |\n| Wan 2.2 TI2V-5B Turbo Q5_K_S | text-to-video                          | Community-distilled GGUF with Wan 2.2 VAE; use `scripts/download-model-wan2.2.sh` |\n| LTX-2 / LTXAV                | text-to-video + audio                  | Gemma text encoder, video VAE, audio VAE, embedding connectors                    |\n| ABot-World                   | interactive world walk                 | Causal block-by-block generation under keyboard input; see [ABot-World guide](docs/abot-world.md) |\n| ESRGAN                       | upscale                                | Standalone or post-generation image upscale                                       |\n\n## Supported Platforms\n\n| Platform | Architecture | Status | GPU backend    |\n| -------- | ------------ | ------ | -------------- |\n| macOS    | arm64, x64   | Tier 1 | Metal          |\n| Linux    | arm64, x64   | Tier 1 | Vulkan         |\n| Android  | arm64        | Tier 1 | Vulkan, OpenCL |\n| iOS      | arm64        | Tier 1 | Metal          |\n| Windows  | x64          | Tier 1 | Vulkan         |\n\nDependencies:\n\n- `qvac-ext-stable-diffusion.cpp`\n- `ggml`\n- Bare Runtime >= 1.24.0\n- CMake >= 3.25 and a C++20-capable compiler\n\n## Building From Source\n\nSee [build.md](./build.md) for prerequisites, platform setup, cross-compilation,\nand troubleshooting.\n\n```bash\nnpm install -g bare bare-make\nnpm install\nnpm run build\n```\n\nCUDA builds can be generated explicitly:\n\n```bash\nnpm run build:cuda\n```\n\n## Downloading Models\n\nDownload scripts populate `packages/diffusion-cpp/models/` with the files used\nby the examples.\n\n| Script                                | Model set                     |\n| ------------------------------------- | ----------------------------- |\n| `./scripts/download-model.sh`         | FLUX.2 [klein] 4B image model |\n| `./scripts/download-model-sd2.sh`     | SD2.x example model           |\n| `./scripts/download-model-sd3.sh`     | SD3 example files             |\n| `./scripts/download-model-sdxl.sh`    | SDXL example files            |\n| `./scripts/download-model-wan.sh`     | Wan 2.1 T2V 1.3B              |\n| `./scripts/download-model-wan-14b.sh` | Wan larger T2V variant        |\n| `./scripts/download-model-wan-i2v.sh` | Wan 2.1 I2V 14B + CLIP vision |\n| `./scripts/download-model-ltx.sh`     | LTX-2.3 video + audio files   |\n| `./scripts/download-model-abot.sh`    | ABot-World set into `test/model/abot` (from the [P2P model registry](docs/abot-world.md), no credentials) |\n\nThe FLUX.2 [klein] default image example uses:\n\n| Role            | File                        |\n| --------------- | --------------------------- |\n| Diffusion model | `flux-2-klein-4b-Q8_0.gguf` |\n| Text encoder    | `Qwen3-4B-Q4_K_M.gguf`      |\n| VAE             | `flux2-vae.safetensors`     |\n\nLTX-2.3 requires more companion files:\n\n| Role                 | Default file                                              |\n| -------------------- | --------------------------------------------------------- |\n| Diffusion model      | `LTX-2.3-22B-distilled-1.1-Q5_K_M.gguf`                   |\n| Text encoder         | `gemma-3-12b-it-UD-Q4_K_XL.gguf`                          |\n| Video VAE            | `ltx-2.3-22b-distilled_video_vae.safetensors`             |\n| Audio VAE            | `ltx-2.3-22b-distilled_audio_vae.safetensors`             |\n| Embedding connectors | `ltx-2.3-22b-distilled_embeddings_connectors.safetensors` |\n\nDownloads are resumable where supported by the script.\n\n## Examples\n\n| Command                                      | Description                                 |\n| -------------------------------------------- | ------------------------------------------- |\n| `npm run example`                            | Load/unload the default FLUX.2 model        |\n| `npm run generate`                           | FLUX.2 text-to-image                        |\n| `npm run generate:sd2`                       | SD2.x text-to-image                         |\n| `npm run generate:sd3`                       | SD3 text-to-image                           |\n| `npm run generate:sdxl`                      | SDXL text-to-image                          |\n| `bare examples/generate-fusion.js`           | FLUX.2 multi-reference fusion               |\n| `bare examples/img2img-flux2.js`             | FLUX.2 single-reference img2img             |\n| `bare examples/img2img-sd3.js`               | SD3 SDEdit img2img                          |\n| `bare examples/generate-video-wan.js`        | Wan 2.1 text-to-video                       |\n| `bare examples/img2vid-wan.js`               | Wan 2.1 image-to-video                      |\n| `npm run generate:video`                     | Wan text-to-video                           |\n| `npm run generate:ltx`                       | LTX-2.3 text-to-video with audio            |\n| `npm run generate:ltx-coffee`                | 9-second LTX Ingredients coffee example     |\n| `npm run generate:esrgan`                    | Image generation followed by ESRGAN upscale |\n| `bare examples/standalone-esrgan-upscale.js` | Standalone ESRGAN upscale                   |\n| `npm run walk:world`                         | ABot-World browser demo (generate + walk a world) |\n\nOutputs are written to `packages/diffusion-cpp/output/`.\n\n## Image API\n\n```js\nconst path = require('bare-path')\nconst ImgStableDiffusion = require('@qvac/diffusion-cpp')\n\nconst MODELS_DIR = path.resolve(__dirname, './models')\n\nconst model = new ImgStableDiffusion({\n  files: {\n    model: path.join(MODELS_DIR, 'flux-2-klein-4b-Q8_0.gguf'),\n    llm: path.join(MODELS_DIR, 'Qwen3-4B-Q4_K_M.gguf'),\n    vae: path.join(MODELS_DIR, 'flux2-vae.safetensors')\n  },\n  config: {\n    threads: 4,\n    device: 'gpu',\n    diffusion_fa: true\n  },\n  opts: { stats: true },\n  logger: console\n})\n\nawait model.load()\n\nconst response = await model.run({\n  prompt: 'a majestic red fox in a snowy forest, golden light, photorealistic',\n  width: 512,\n  height: 512,\n  steps: 20,\n  guidance: 3.5,\n  seed: 42\n})\n\nconst images = []\nawait response\n  .onUpdate((data) => {\n    if (data instanceof Uint8Array) images.push(data) // PNG bytes\n  })\n  .await()\n\nrequire('bare-fs').writeFileSync('output.png', images[0])\nawait model.unload()\n```\n\n### Constructor Files\n\nAll file paths must be absolute.\n\n| Key                             | Required | Description                                                                 |\n| ------------------------------- | -------: | --------------------------------------------------------------------------- |\n| `files.model`                   |      yes | Main model. All-in-one checkpoint for SD, diffusion model for split layouts |\n| `files.clipL`                   |       no | CLIP-L text encoder for SD3 / split layouts                                 |\n| `files.clipG`                   |       no | CLIP-G text encoder for SDXL / SD3                                          |\n| `files.t5Xxl`                   |       no | T5-XXL text encoder for SD3                                                 |\n| `files.llm`                     |       no | Qwen3 LLM text encoder for FLUX.2 [klein]                                   |\n| `files.vae`                     |       no | Separate VAE                                                                |\n| `files.esrgan`                  |       no | ESRGAN model for post-generation upscale                                    |\n| `files.highNoiseDiffusionModel` |       no | Wan 2.2 high-noise expert path; normally used by `VideoStableDiffusion`     |\n\nPassing any separate text encoder (`llm`, `t5Xxl`, `clipL`, `clipG`) makes the\nwrapper route `files.model` to stable-diffusion.cpp's `diffusion_model_path`.\nAll-in-one checkpoints are routed to `model_path`.\n\n### Image Config\n\n`config` is part of the constructor object. There is no second constructor\nargument.\n\n| Key                     | Type                                      | Default           | Description                                                               |\n| ----------------------- | ----------------------------------------- | ----------------- | ------------------------------------------------------------------------- |\n| `threads`               | number                                    | auto              | CPU threads for loading / CPU ops                                         |\n| `device`                | `'gpu'                                    | 'cpu'`            | `'gpu'`                                                                   | Prefer GPU backends or force CPU |\n| `main-gpu`              | number \\| `'integrated'` \\| `'dedicated'` | unset             | Pin the GPU selected by stable-diffusion.cpp                              |\n| `type`                  | weight type                               | auto              | Override weight quantization                                              |\n| `rng`                   | `'cpu'                                    | 'cuda'            | 'std_default'`                                                            | `'cuda'`                         | Context RNG; `cuda` means Philox and is not GPU-specific |\n| `sampler_rng`           | RNG type                                  | auto              | Sampler RNG override                                                      |\n| `vae_decode_only`       | boolean                                   | `false`           | Load only VAE decoder weights; leave false for img2img/fusion/hires paths |\n| `vae_tiling`            | boolean                                   | `false`           | Tile VAE decode to reduce peak VRAM                                       |\n| `flash_attn`            | boolean                                   | `false`           | Enable flash attention globally                                           |\n| `diffusion_fa`          | boolean                                   | `true`            | Enable diffusion-model flash attention; important for FLUX/LTX memory use |\n| `mmap`                  | boolean                                   | backend default   | Memory-map weights when supported                                         |\n| `offload_to_cpu`        | boolean                                   | backend default   | Keep weights on CPU/offload as supported by backend                       |\n| `backend`               | string                                    | auto              | Runtime backend for all modules or per-module assignments                 |\n| `params_backend`        | string                                    | runtime backend   | Parameter residency on a backend, CPU RAM, or disk                        |\n| `max_vram`              | number \\| string                          | `0`               | VRAM budget in GiB for graph-cut segmented execution                      |\n| `stream_layers`         | boolean                                   | `false`           | Stream diffusion layers from CPU RAM when graph cutting is active         |\n| `prediction`            | prediction type                           | auto              | Required for FLUX img2img/fusion routing; use `'flux2_flow'` for FLUX.2   |\n| `flow_shift`            | number                                    | model default     | Flow-matching noise schedule shift                                        |\n| `diffusion_conv_direct` | boolean                                   | `true`            | Use direct convolution in diffusion model                                 |\n| `vae_conv_direct`       | boolean                                   | `true`            | Use direct convolution in VAE                                             |\n| `backendsDir`           | string                                    | package prebuilds | Custom ggml backend directory                                             |\n| `lora_apply_mode`       | string                                    | auto              | LoRA application mode                                                     |\n| `upscaler_tile_size`    | number                                    | `128`             | ESRGAN tile size                                                          |\n| `max_image_pixels`      | positive integer                          | `67108864`        | Maximum decoded input or ESRGAN output pixels per image                  |\n| `max_job_pixels`        | positive integer                          | `134217728`       | Maximum decoded input pixels retained by a multi-image or video job     |\n\n`main-gpu` is resolved against the addon's own ggml device enumeration and then\npinned through `sd_ctx_params_t.backend`. If an explicit request cannot be\nsatisfied (`'integrated'` with no integrated GPU, `'dedicated'` with no discrete\nGPU, or an out-of-range index), the addon falls back to CPU instead of silently\nchoosing another GPU. Mobile targets reject `main-gpu` because they are\nsingle-GPU devices. An explicit `backend` assignment takes precedence over\n`main-gpu`.\n\n`backend` controls where graphs execute. `params_backend` controls where model\nweights remain between uses. For example, `backend: 'diffusion=cuda0,te=cpu'`\nruns the diffusion model on CUDA and the text encoder on CPU.\n\n`params_backend: 'diffusion=cpu'` keeps diffusion weights in CPU RAM and stages\nthem to the runtime backend. `params_backend: 'diffusion=disk'` reloads those\nweights from the model file on demand and releases them after use — on every\njob, not only the first — so a disk-backed module also disables eager weight\nloading for the whole context. Disk is not selected automatically.\n\n`offload_to_cpu: true` supplies a `*=cpu` default. An explicit `params_backend`\nentry written in `module=backend` form overrides that default **for that module\nonly**: `params_backend: 'te=disk'` with `offload_to_cpu: true` keeps TE weights\non disk while other parameters remain in CPU RAM.\n\nA bare entry or an assignment to `*`, `all`, or `default` sets the whole-spec\ndefault rather than a per-module override, and the last default wins. For\nexample, `params_backend: 'cuda0'` with `offload_to_cpu: true` puts every module\non `cuda0` and offloads nothing. The addon logs when the final whole-spec default\ndiffers from CPU. Equivalent defaults such as `all=cpu` do not produce an error.\nWrite `params_backend: 'diffusion=cuda0'` to move one module and leave the rest\noffloaded.\n\nA nonzero `max_vram` enables graph-cut segmentation even without\n`stream_layers`. Positive values cap the VRAM budget in GiB. Negative values\nuse detected free VRAM while reserving the absolute value as headroom, and `0`\ndisables graph cutting. Backend assignments such as\n`max_vram: 'cuda0=6,vulkan0=4'` apply one budget per device.\n\n`stream_layers` adds diffusion-layer prefetch and eviction only when graph\ncutting is active and the diffusion parameter backend is CPU, for example:\n\n```js\nconfig: {\n  backend: 'cuda0',\n  params_backend: 'diffusion=cpu',\n  max_vram: -1,\n  stream_layers: true\n}\n```\n\nIt does not stream from disk. Use `params_backend: 'diffusion=disk'` for\non-demand reads from the model file.\n\n`stream_layers` is forwarded to the engine as configured; the engine itself\nskips streaming when its prerequisites are unmet. The addon reports the cases\nit can prove before engine initialization: `max_vram` is unset, `0`, or an\nall-zero assignment such as `'cuda0=0'`. Per-backend and automatic negative\nbudgets are resolved by the engine using the selected runtime backend and its\nfree memory. That message, and the `main-gpu` and `params_backend` notices above,\nare the only diagnostics emitted at the default `verbosity: 0`; set\n`verbosity: 2` to also see the effective `backend`, `params_backend` and\n`max_vram` assignments the addon passes to the engine.\n\nThe 16-case Linux hardware matrix is available in\n`scripts/validate-layer-streaming.sh`. It expects the MiniMax-H3 files under\n`/home/shared/models/minimax-h3-q2` by default. Override that location with\n`H3_MODELS_DIR`. The runtime backend defaults to `vulkan0`; override it with\n`BACKEND`, for example `BACKEND=cuda0`. Then run the script from the package\ndirectory. Each case requires a non-empty AVI and checks the engine log for the\nexpected graph-cut, streaming, CPU RAM, or disk behavior.\n\n### Image Generation Parameters\n\n| Key                           | Type              | Description                                                         |\n| ----------------------------- | ----------------- | ------------------------------------------------------------------- |\n| `prompt`                      | string            | Required non-empty prompt                                           |\n| `negative_prompt`             | string            | Negative prompt                                                     |\n| `width`, `height`             | number            | Positive multiples of 8; FLUX img2img defaults omitted axes to 1024 |\n| `steps`                       | number            | Diffusion step count                                                |\n| `cfg_scale`                   | number            | Classifier-free guidance for SD/SDXL/SD3 and FLUX img2img examples  |\n| `guidance`                    | number            | Distilled guidance for FLUX.2                                       |\n| `sampling_method` / `sampler` | string            | Sampler name; omit for auto-selection                               |\n| `scheduler`                   | string            | Scheduler name; omit for auto-selection                             |\n| `seed`                        | number            | `-1` for random                                                     |\n| `batch_count`                 | number            | Number of images                                                    |\n| `lora`                        | string            | Absolute path to a LoRA adapter                                     |\n| `upscale`                     | boolean \\| object | Post-generation ESRGAN upscale; requires `files.esrgan`             |\n| `vae_tiling`                  | boolean           | Per-job VAE tiling                                                  |\n| `vae_tile_size`               | number \\| string  | VAE tile size, e.g. `512` or `'512x512'`                            |\n| `vae_tile_overlap`            | number            | Tile overlap fraction                                               |\n| `cache_mode`                  | string            | Step-caching algorithm                                              |\n| `cache_preset`                | string            | Cache preset: `slow`, `medium`, `fast`, `ultra`                     |\n| `cache_threshold`             | number            | Cache reuse threshold                                               |\n| `eta`                         | number            | DDIM/TCD stochasticity                                              |\n| `clip_skip`                   | number            | Skip last N CLIP layers                                             |\n\nDo not force `sampling_method: 'euler_a'` for FLUX.2 models. Leave the sampler\nunset unless you know the model family requires an override.\n\n## Image-to-Image and FLUX.2 Fusion\n\n`init_image` accepts PNG/JPEG bytes and selects image-to-image mode.\n\n- FLUX.2 uses in-context conditioning: the reference image is VAE-encoded into\n  separate latent tokens and the target starts from pure noise.\n- SD1.x / SD2.x / SDXL / SD3 use SDEdit: the input is noised according to\n  `strength` and denoised with the prompt.\n\nFor FLUX.2 img2img, load the context with `files.llm` and\n`config.prediction: 'flux2_flow'`.\n\n```js\nconst inputImage = require('bare-fs').readFileSync('assets/source.jpg')\n\nconst response = await model.run({\n  prompt: 'a cinematic portrait of the same person, professional lighting',\n  init_image: inputImage,\n  width: 1024,\n  height: 1024,\n  cfg_scale: 1.0,\n  guidance: 3.5,\n  steps: 20,\n  seed: 42\n})\n```\n\nFLUX.2 multi-reference fusion uses `init_images`, an array of PNG/JPEG buffers.\nIt is mutually exclusive with `init_image` and requires\n`config.prediction: 'flux2_flow'`.\n\n```js\nconst response = await model.run({\n  prompt: 'blend @image1 and @image2 into one scientist in a black studio',\n  init_images: [image1Bytes, image2Bytes],\n  width: 624,\n  height: 624,\n  cfg_scale: 1.0,\n  guidance: 3.5,\n  steps: 10,\n  seed: 10\n})\n```\n\n`@image1`, `@image2`, etc. are prose anchors for the text encoder. The FLUX.2\nQwen3 text encoder does not receive vision tokens; visual fusion happens in the\nDiT through attention over reference latents. Keep `increase_ref_index` unset or\n`false` for FLUX.2-klein fusion.\n\n## Video API\n\n```js\nconst path = require('bare-path')\nconst fs = require('bare-fs')\nconst VideoStableDiffusion = require('@qvac/diffusion-cpp/video')\n\nconst MODELS_DIR = path.resolve(__dirname, './models')\n\nconst model = new VideoStableDiffusion({\n  files: {\n    model: path.join(MODELS_DIR, 'wan2.1_t2v_1.3B_fp16.safetensors'),\n    t5Xxl: path.join(MODELS_DIR, 'umt5_xxl_fp16.safetensors'),\n    vae: path.join(MODELS_DIR, 'wan_2.1_vae.safetensors')\n  },\n  config: {\n    threads: 4,\n    device: 'gpu',\n    diffusion_fa: true,\n    offload_to_cpu: true,\n    vae_tiling: true\n  },\n  logger: console\n})\n\nawait model.load()\n\nconst response = await model.run({\n  mode: 'txt2vid',\n  prompt: 'a colorful bird flapping its wings',\n  negative_prompt: 'blurry, low quality, static, jittery, watermark',\n  width: 480,\n  height: 832,\n  video_frames: 81,\n  fps: 16,\n  steps: 30,\n  cfg_scale: 6.0,\n  flow_shift: 3.0,\n  seed: 42\n})\n\nlet avi = null\nawait response\n  .onUpdate((data) => {\n    if (data instanceof Uint8Array) avi = data // MJPG AVI bytes\n  })\n  .await()\n\nfs.writeFileSync('wan_t2v_seed42.avi', avi)\nawait model.unload()\n```\n\nThe default export from `@qvac/diffusion-cpp/video` and the named\n`VideoStableDiffusion` export from `@qvac/diffusion-cpp` are the same class.\n\n### Video Files\n\n| Key                             | Model family | Description                                                     |\n| ------------------------------- | ------------ | --------------------------------------------------------------- |\n| `files.model`                   | all video    | Wan single/low-noise expert or LTX diffusion transformer        |\n| `files.t5Xxl`                   | Wan          | UMT5-XXL text encoder                                           |\n| `files.vae`                     | Wan / LTX    | Wan VAE or LTX video VAE                                        |\n| `files.clipVision`              | Wan I2V      | OpenCLIP ViT-H/14; required for `mode: 'img2vid'` on Wan        |\n| `files.llm`                     | LTX          | Gemma text encoder                                              |\n| `files.audioVae`                | LTX          | Audio VAE decoder for synchronized audio                        |\n| `files.embeddingsConnectors`    | LTX          | Text-embedding connector weights; also marks the context as LTX |\n\n### Video Parameters\n\n| Key                                                          | Description                                                        |\n| ------------------------------------------------------------ | ------------------------------------------------------------------ |\n| `mode`                                                       | Required: `'txt2vid'` or `'img2vid'`                               |\n| `prompt`, `negative_prompt`                                  | Text conditioning                                                  |\n| `width`, `height`                                            | Wan: multiples of 16. LTX: multiples of 32                         |\n| `video_frames`                                               | Wan: `(4*k + 1)`. LTX: `(8*k + 1)`, max 257                        |\n| `fps`                                                        | AVI framerate metadata, default 16 for Wan examples and 24 for LTX |\n| `steps`, `cfg_scale`, `sampling_method`, `scheduler`, `seed` | Sampling controls                                                  |\n| `flow_shift`                                                 | Per-job flow-shift override; Wan 2.1 T2V 1.3B works well at `3.0`  |\n| `init_image`                                                 | First frame for `img2vid`; required by that mode                   |\n| `control_frames`, `vace_strength`                            | Optional VACE guidance                                             |\n| `temporal_tiling`                                            | LTX-only temporal VAE tiling to reduce peak VRAM                   |\n| `cache_mode`, `cache_preset`, `cache_threshold`              | Step-cache controls                                                |\n\nDecoded `init_image`, `control_frames`, and `reference_images` together are\nlimited to 128 Mi pixels per job by default; each image is limited to 64 Mi pixels by default. At\n1920×1080, up to 64 control frames fit when there are no other input images.\nAn initial or reference image reduces the available control-frame budget.\nSet `config.max_job_pixels` and `config.max_image_pixels` to positive pixel\ncounts when the target device can support a different budget. The fixed\n16,384-pixel edge and compressed-source safeguards still apply.\n\nVideo output is a single MJPG AVI `Uint8Array`. For LTX-2 models loaded with\n`audioVae`, the AVI also contains a second IEEE-float PCM stream at 48 kHz.\nVLC handles these files well.\n\n### Wan 2.2\n\nThe supported Wan 2.2 workflow is the community-distilled TI2V-5B Turbo\nQ5_K_S GGUF for text-to-video. The downloader does not include the CLIP vision\nencoder required by the wrapper's image-to-video path. Download its complete\ntext-to-video model layout before running the example:\n\n```sh\n# TI2V-5B Turbo Q5_K_S (4 steps, CFG 1)\n./scripts/download-model-wan2.2.sh\nnpm run generate:wan22\n```\n\nThe download is approximately 16.4 GB: 3.56 GB for the Q5_K_S diffusion model,\n11.4 GB for the fp16 UMT5-XXL encoder, and 1.41 GB for the Wan 2.2 VAE. The\nbackend runs one process on one device with optional CPU offload. Use dimensions\nthat are multiples of 32 for this TI2V model so the emitted AVI dimensions match\nthe requested dimensions.\n\n## MiniMax-H3 Text-to-Audio-Video\n\nMiniMax-H3 is supported for prompt-only video generation. Download the matching\nFL2VA denoiser, Qwen3-VL text encoder, video VAE, and audio VAE:\n\n```sh\n./scripts/download-model-minimax-h3.sh --q4\n```\n\nSet `H3_MODELS_DIR` when running `npm run generate:h3-coffee` with model files\nstored outside the package.\n\nThe initial integration intentionally rejects init images, control frames, and\nreference images. Use a 32-pixel spatial grid and a `17*k + 5` frame count.\nH3 is distilled: `cfg_scale` must be `1.0`, and the output stream is always\n24 FPS with its native stereo audio.\n\n```js\nconst VideoStableDiffusion = require('@qvac/diffusion-cpp/video')\nconst model = new VideoStableDiffusion({\n  files: {\n    model: '/models/minimax-h3/minimax_h3_fl2va_pruned-Q4_K.gguf',\n    llm: '/models/minimax-h3/qwen3vl_32b_minimax_h3-Q4_K_M.gguf',\n    vae: '/models/minimax-h3/vae/minimax_h3_video_vae_fp16.safetensors',\n    audioVae: '/models/minimax-h3/vae/minimax_h3_audio_vae_fp32.safetensors'\n  },\n  config: { device: 'gpu', diffusion_fa: true, offload_to_cpu: true }\n})\nawait model.load()\nconst response = await model.run({\n  mode: 'txt2vid',\n  prompt: 'A cinematic close-up of a person enjoying coffee in warm morning light.',\n  width: 960,\n  height: 544,\n  video_frames: 124,\n  fps: 24,\n  steps: 8,\n  cfg_scale: 1.0\n})\n```\n\n## LTX-2 Text-to-Video With Audio\n\n```js\nconst model = new VideoStableDiffusion({\n  files: {\n    model: path.join(MODELS_DIR, 'LTX-2.3-22B-distilled-1.1-Q5_K_M.gguf'),\n    llm: path.join(MODELS_DIR, 'gemma-3-12b-it-UD-Q4_K_XL.gguf'),\n    vae: path.join(MODELS_DIR, 'ltx-2.3-22b-distilled_video_vae.safetensors'),\n    audioVae: path.join(MODELS_DIR, 'ltx-2.3-22b-distilled_audio_vae.safetensors'),\n    embeddingsConnectors: path.join(\n      MODELS_DIR,\n      'ltx-2.3-22b-distilled_embeddings_connectors.safetensors'\n    )\n  },\n  config: {\n    threads: 4,\n    device: 'gpu',\n    diffusion_fa: true,\n    vae_tiling: true,\n    vae_conv_direct: true\n  },\n  opts: { stats: true },\n  logger: console\n})\n\nconst response = await model.run({\n  mode: 'txt2vid',\n  prompt: 'a claymation cat playing jazz on a piano',\n  negative_prompt: 'blurry, low quality, static, jittery, watermark, distorted audio',\n  width: 512,\n  height: 320,\n  video_frames: 241,\n  fps: 24,\n  steps: 10,\n  cfg_scale: 1.0,\n  temporal_tiling: true,\n  seed: 42\n})\n```\n\nLTX distilled variants are designed for low step counts and low CFG values.\nFor full dev weights, use higher steps and a larger CFG.\n\n## ABot-World Interactive Walk\n\nABot-World is a causal world model: it generates video **block-by-block under\nlive keyboard input** instead of one batch call, exposed via\n`@qvac/diffusion-cpp/world` (`WorldStableDiffusion`). Worlds are created\nnatively from a prompt + first-frame image (`createScene()`), then walked with\nWASD/IJKL (`step()`), streaming decoded PNG/JPEG frames. The four model files\nship in the QVAC P2P registry.\n\n```bash\n# models (P2P registry, no credentials) + browser demo\nnpm install -g @qvac/registry-client   # then see docs/abot-world.md for the 4 downloads\nexport ABOT_MODELS_DIR=~/abot-models ABOT_KV_CACHE=1\nnpm run walk:world                     # open http://127.0.0.1:8787\n```\n\n```js\nconst WorldStableDiffusion = require('@qvac/diffusion-cpp/world')\n\nconst world = new WorldStableDiffusion({\n  files: { model: ditGguf, taehv: taehvGguf, scene: scenePack },\n  config: { seed: 42, kvCache: true }\n})\nawait world.load()\nconst response = await world.step({ W: true }) // one generated block forward\nawait response\n  .onUpdate((data) => {\n    if (data instanceof Uint8Array) frames.push(data) // PNG/JPEG frames\n  })\n  .await()\nawait world.unload()\n```\n\n**Hardware requirements** (interactive walk, Q8 DiT + KV cache):\n\n| tier | GPU | host RAM | disk | measured |\n|---|---|---|---|---|\n| minimum @ 832x480 | 24 GB card, **>= 20 GB VRAM free** (16.3 GB steady + ~2.7 GB transient at block 0) | 8 GB | 14 GB | RTX 5090: 1.78 s/block |\n| low-VRAM @ 448x256 | ~6 GB | 8 GB | 14 GB | laptop RTX 4050 |\n| optimal | RTX 5090-class, dedicated (no co-tenant VRAM); optional dual-GPU split `ABOT_BACKEND=\"diffusion=cuda0,vae=cuda1\"` | 16 GB | NVMe | 6.7-6.8 fps generation |\n\n**Performance vs the PyTorch reference** (same 5090, same walk): the QVAC build\ngenerates **6.7 fps vs ~28.5 fps** for the reference's fp8 Triton pipeline —\nabout **4.2x slower per block**, the main known trade-off of this\nimplementation today. In exchange it starts 36x faster (first frame in 1.5 s vs\n~54 s), uses 14x less host RAM (3.1 GB vs 43.6 GB peak), one CPU core instead\nof two-plus, 14 GB on disk instead of ~38 GB, and needs no Python runtime.\n\n**Model fidelity**: the weights are converted to GGUF (DiT and umT5 at Q8_0,\nVAE and taehv at F16), so outputs are **not bit-exact** vs the PyTorch\nreference — parity is held by cosine-similarity gates instead (golden walk\nreplays 0.993-0.99995, scene packs >= 0.997, ~32-38 dB walk-level PSNR), and\nwalks are visually indistinguishable in validation.\n\nFull guide — building, the demo server (local and over-SSH playback), the world\nAPI for app developers, resolutions, performance details and troubleshooting:\n**[docs/abot-world.md](docs/abot-world.md)**.\n\n## ESRGAN Upscaler\n\n```js\nconst { EsrganUpscaler } = require('@qvac/diffusion-cpp')\nconst fs = require('bare-fs')\n\nconst upscaler = new EsrganUpscaler({\n  files: {\n    esrgan: '/absolute/path/to/RealESRGAN_x4plus_anime_6B.pth'\n  },\n  config: {\n    device: 'gpu',\n    upscaler_tile_size: 128\n  },\n  logger: console\n})\n\nawait upscaler.load()\n\nconst response = await upscaler.upscale(fs.readFileSync('input.png'), {\n  repeats: 1\n})\n\nconst images = []\nawait response\n  .onUpdate((data) => {\n    if (data instanceof Uint8Array) images.push(data)\n  })\n  .await()\n\nfs.writeFileSync('upscaled.png', images[0])\nawait upscaler.unload()\n```\n\n`repeats` controls how many ESRGAN passes are applied. One pass typically scales\nby 4x; two passes scale by 16x.\n\n## Response Streams and Stats\n\nAll three wrappers return a `QvacResponse`.\n\n- Progress updates are JSON strings like `{\"step\":1,\"total\":20,\"elapsed_ms\":...}`.\n  The stream is multi-phase: sampler sequences (one per image batch item /\n  video expert, `total` = its step count) and, when `vae_tiling` is enabled,\n  VAE tile passes (`total` = tile count). Each sequence restarts at\n  `step: 0`, so a bar renderer should key on `total` changes rather than\n  assume a single monotonic sequence. Model weights normally load eagerly at\n  `load()`, so generation emits no loader ticks. Two configurations load\n  lazily instead and do emit loader ticks inside generation: mobile targets,\n  and any `params_backend` naming `disk` for one or more modules — the latter\n  on every job, since disk-backed weights are released after each phase. In\n  those cases the first job's `conditionerMs` also absorbs weight-load time.\n- Image generation and ESRGAN emit PNG `Uint8Array` values.\n- Video generation emits one MJPG AVI `Uint8Array`.\n- If `opts.stats` is enabled, a `stats` event is emitted before completion.\n\nImage stats include load time, generation time, cumulative steps/images/pixels,\ndimensions, and seed. Video stats additionally include cumulative videos,\nframes, `fps`, `hasAudio`, and `audioSampleRate`. ESRGAN stats include upscale\ntiming, output dimensions, repeats, and the backend device actually used.\n\n## Cancellation and Unload\n\nEach wrapper has `cancel()` and `unload()` methods. Only one job may run per\nmodel instance at a time; overlapping `run()` calls fail with a busy error.\n\n```js\nconst response = await model.run({ prompt: '...', steps: 30 })\nawait model.cancel()\nawait response.await().catch(() => {})\nawait model.unload()\n```\n\nDuring ESRGAN upscale, cancellation is honored between repeat passes.\n\n## Assessing fit\n\n`assessFit` projects a load against the memory free right now. It reads model metadata and never weight data, so a GGUF file set can be the registry's weightless copy and the projection can run before anything is downloaded. A safetensors file is still read against its declared tensor data, so a header-only copy of one comes back as `status: \"error\"`. It is a module export, not an instance method — nothing is loaded to call it.\n\n```js\nconst { assessFit } = require('@qvac/diffusion-cpp')\n\nconst fit = assessFit({\n  files: { model: '/models/model.gguf' },\n  config: { backendsDir: '/opt/backends' },\n  workload: { prompt: 'a lighthouse at dusk', width: 1024, height: 1024 }\n})\n\nfit.status // 'fits' | 'does-not-fit' | 'error'\nfit.reason // 'fits' | 'does-not-fit' | 'model-unreadable' | 'unsupported-config'\nfit.changed // the engine placed the load only by altering the backend assignment\nfit.backend\nfit.paramsBackend\nfit.vaeTiling\nfit.streamLayers\nfit.report // per-device, per-module memory table, suitable for logging\n```\n\n`files` and `config` are what `createInstance` takes, plus `files.clipVision`, `files.audioVae` and `files.embeddingsConnectors`. Paths must be absolute. `workload` describes the generation the projection is sized for, and its defaults are the generation defaults:\n\n| Field | Default | Description |\n| --- | --- | --- |\n| `prompt` | a stand-in prompt | Token count drives the text-encoder memory. |\n| `width`, `height` | 512 | Output dimensions. |\n| `videoFrames` | 1 | 1 or less projects image generation. A video-only model is projected at one frame unless this is passed, while `run()` defaults to 33. |\n| `vaeTiling` | `false` | Tiled decoding trades speed for a much smaller VAE arena. |\n| `vaeTileSizeX`, `vaeTileSizeY` | 512 | The tile geometry, in pixels. |\n| `vaeTileOverlap` | 0.5 | Fraction of a tile used as the overlap seam. |\n\nEvery number must be finite, whole and in range; anything else is `status: \"error\"` with `reason: \"unsupported-config\"`.\n\nThe engine chooses the placement, so it has to be given an unpinned configuration. A `config.mainGpu`, a `config.device` of `cpu`, or a `paramsBackend` naming individual modules is `reason: \"unsupported-config\"`.\n\nA `changed` of `true` comes with `status: \"does-not-fit\"` — the configuration as given does not fit, and the engine reached a placement only by moving modules between backends. A `paramsBackend` of `*=cpu` is the exception: the engine replans every module for it, so the plan is compared against the placement that load already uses, and an equal one is `fits`.\n\nA model the engine cannot read is `status: \"error\"` with `reason: \"model-unreadable\"`; only a broken request throws.\n\nThe projection runs in the calling process and shares its engine state: the GPU backend modules load once per process, so the first `config.backendsDir` any call supplies is the one every later load uses, and the native log callback is process-global like everywhere else.\n\n## Operational Notes\n\n- Native C++ logs are process-global. Configure them once with\n  `require('@qvac/diffusion-cpp/addonLogging').setLogger(...)`.\n- Leave sampler and scheduler unset for normal use; the addon preserves\n  stable-diffusion.cpp auto-detection for model-specific defaults.\n- `diffusion_fa` defaults to true and is important for FLUX/LTX memory use.\n- For FLUX.2 img2img/fusion, set `config.prediction: 'flux2_flow'` so the JS\n  wrapper and native layer select the in-context conditioning path.\n- Wan I2V requires `files.clipVision`; LTX img2vid does not.\n- Wan dimensions must be multiples of 16; LTX dimensions must be multiples of 32.\n- Wan frame counts use `(4*k + 1)`; LTX frame counts use `(8*k + 1)`.\n- LTX audio is muxed into AVI as IEEE-float PCM at 48 kHz.\n- On Linux the shipped prebuild must not hard-link any GPU loader\n  (`libvulkan`/`libOpenCL`/`libcuda`) — an integration test asserts this. For\n  local custom builds that legitimately do (e.g. `SD_CUDA=ON`), skip it with\n  `QVAC_SKIP_PREBUILD_LINK_CHECK=true`.\n\n## Credits\n\n### Test Images\n\n`assets/von-neumann.jpg` — **John von Neumann** (1956).\nSource: U.S. Department of Energy, File ID: HD.3F.191.\nThis image is in the **Public Domain** as a work of the U.S. Federal Government.\n\n`assets/claude-shannon.jpg` — **Claude Shannon**.\nSource: Bell Labs / [Wikimedia Commons](https://commons.wikimedia.org/wiki/Category:Claude_Shannon).\nLicensed under **Creative Commons Attribution-ShareAlike (CC BY-SA)**.\nAttribution must be preserved; any redistribution of this image or a derivative\nmust be released under a compatible CC BY-SA license.\n\n## License\n\nApache-2.0 — see [LICENSE](./LICENSE) for details.\n","readmeFilename":"README.md"}