Metadata-Version: 2.4
Name: abstractvision
Version: 0.3.18
Summary: Model-agnostic generative vision abstractions (image/video) for the Abstract ecosystem
Author-email: Laurent-Philippe Albou <contact@abstractcore.ai>
License-Expression: MIT
Project-URL: Homepage, https://github.com/lpalbou/abstractvision
Project-URL: Repository, https://github.com/lpalbou/abstractvision
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Multimedia
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
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Dynamic: license-file

# AbstractVision

[![PyPI version](https://img.shields.io/pypi/v/abstractvision.svg)](https://pypi.org/project/abstractvision/)
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Model-agnostic generative vision API (images, optional video) for Python and the Abstract* ecosystem.

## What you get

- A small orchestration API: [`VisionManager`](src/abstractvision/vision_manager.py)
- A packaged capability registry (“what models can do”): [`VisionModelCapabilitiesRegistry`](src/abstractvision/model_capabilities.py) backed by [`vision_model_capabilities.json`](src/abstractvision/assets/vision_model_capabilities.json)
- Shared model metadata that now also drives local catalog surfacing and backend request normalization across the CLI, playground, and AbstractCore paths
- Optional artifact-ref outputs (small JSON refs): [`LocalAssetStore`](src/abstractvision/artifacts.py) and [`RuntimeArtifactStoreAdapter`](src/abstractvision/artifacts.py)
- Built-in backends (execution engines): [`src/abstractvision/backends/`](src/abstractvision/backends/)
  - OpenAI-compatible HTTP: [`openai_compatible.py`](src/abstractvision/backends/openai_compatible.py)
  - Local Diffusers: [`huggingface_diffusers.py`](src/abstractvision/backends/huggingface_diffusers.py)
  - Local stable-diffusion.cpp / GGUF: [`stable_diffusion_cpp.py`](src/abstractvision/backends/stable_diffusion_cpp.py)
  - Local MLX-Gen Apple Silicon backend for curated AbstractFramework MLX presets: [`mflux.py`](src/abstractvision/backends/mflux.py)
- CLI for manual testing (`abstractvision cli`, legacy alias: `abstractvision repl`): [`abstractvision`](src/abstractvision/cli.py)
- Self-contained local Playground UI/API: [`playground/vision_playground.html`](playground/vision_playground.html) (docs: [`playground/README.md`](playground/README.md))

## How it fits together (diagram)

```mermaid
flowchart LR
  Caller[Python / CLI / AbstractCore] --> VM[VisionManager]
  VM --> BE[VisionBackend]
  BE --> VM
  VM -->|optional| Store[MediaStore]
  Store --> Ref[Artifact ref dict]
  VM -->|no store| Asset["GeneratedAsset (bytes + mime)"]
```

## Status (current backend support)

- Development status: **Alpha** (0.x). The public API is stable-by-design, but breaking changes may still happen and will be called out in `CHANGELOG.md`.
- Built-in backends implement images (`text_to_image`, `image_to_image`) plus backend-dependent video (`text_to_video`, `image_to_video`).
- Local MLX-Gen supports `text_to_image` for curated FLUX.2, Qwen Image, Z-Image, ERNIE Image Turbo, FIBO, and Bonsai ternary models; supports `image_to_image` for FLUX.2 klein/base, Qwen Image Edit, ERNIE Image Turbo, FIBO, and FIBO Edit models; and supports Wan 2.2 TI2V for local `text_to_video` and first-frame `image_to_video`.
- Local Diffusers `text_to_video` remains experimental and is temporarily disabled from the normal local runtime surfaces pending [`docs/backlog/planned/0023_local_runtime_capability_quarantine_for_glm_mflux_and_t2v.md`](docs/backlog/planned/0023_local_runtime_capability_quarantine_for_glm_mflux_and_t2v.md).
- Remote `text_to_video` / `image_to_video` are also supported through the OpenAI-compatible backend **when** endpoints are configured.
- `multi_view_image` is part of the public API (`VisionManager.generate_angles`) but no built-in backend implements it yet.

Details: [`docs/reference/backends.md`](docs/reference/backends.md).

## Installation

```bash
pip install abstractvision
```

The base install is lightweight. It includes the shared API, capability
registry, artifact helpers, CLI, AbstractCore plugin entry point, and the
stdlib OpenAI-compatible HTTP backend. Local inference runtimes are explicit
extras.

Optional extras:

| Extra | Use |
|---|---|
| `abstractvision[openai]` | Official OpenAI provider intent marker; no SDK dependency today. |
| `abstractvision[openai-compatible]` | Generic local/remote OpenAI-shaped endpoint intent marker; stdlib-only today. |
| `abstractvision[models]` | Curated Hugging Face download helpers for cache-backed local quantized vision model presets. |
| `abstractvision[diffusers]` | Install Torch/Diffusers and related packages for local Diffusers generation. |
| `abstractvision[huggingface]` | Compatibility alias for callers that still request the historical Diffusers extra. |
| `abstractvision[sdcpp]` | Install `stable-diffusion-cpp-python` for the pip binding fallback. |
| `abstractvision[mlx-gen]` | Install the optional MLX-Gen Apple Silicon image/video runtime. |
| `abstractvision[mflux]` | Compatibility alias for the MLX-Gen Apple Silicon image/video runtime. |
| `abstractvision[local]` | Convenience for both local backend dependency sets, including `diffusers` and `sdcpp`. |
| `abstractvision[all]` | All runtime backend dependencies, without contributor tooling. |
| `abstractvision[apple]` / `abstractvision[all-apple]` | Native macOS Python profile: Diffusers/Torch MPS, stable-diffusion.cpp bindings, and MLX-Gen. |
| `abstractvision[gpu]` | GPU Diffusers/Torch profile. Install a CUDA/ROCm-enabled PyTorch wheel when needed. |
| `abstractvision[all-gpu]` | Full GPU-relevant local vision profile: Diffusers plus stable-diffusion.cpp bindings. |
| `abstractvision[abstractcore]` | Compatibility marker only; AbstractCore is still supplied by the host application. |

`stable-diffusion-cpp-python` is currently constrained below `0.4.6` because
that release's source distribution is missing vendored CMake files required by
native Linux builds.

Contributor-only extras:

| Extra | Use |
|---|---|
| `abstractvision[diffusers-dev]` / `abstractvision[huggingface-dev]` | Looser dependency pins for newer/unreleased Diffusers pipelines; install Diffusers `main` separately if needed. |
| `abstractvision[test]` | Local test dependencies. |
| `abstractvision[docs]` | Documentation build tooling. |
| `abstractvision[dev]` | Full contributor workflow: tests, docs, build, lint, formatting, and pre-commit. Do not use this as an application runtime profile. |

Note (CUDA): on Windows/Linux, `pip install "abstractvision[diffusers]"` may install a CPU-only PyTorch build. If you want to use an NVIDIA GPU, install a CUDA-enabled PyTorch build first (see <https://pytorch.org/get-started/locally/>) and verify `torch.cuda.is_available()` is `True`.

AbstractCore is not installed by AbstractVision. When an AbstractCore application
has AbstractVision installed in the same environment, AbstractCore can discover
the plugin entry point and use the integration modules lazily.

If you hit “missing pipeline class” errors for newer model families, see [`docs/getting-started.md`](docs/getting-started.md). In that case you may need Diffusers from source (`main`):

```bash
pip install -U "abstractvision[diffusers-dev]"
pip install -U "git+https://github.com/huggingface/diffusers@main"
```

For local development from a repo checkout:

```bash
pip install -e ".[dev]"
```

## Usage

Start here:
- Getting started: [`docs/getting-started.md`](docs/getting-started.md)
- FAQ: [`docs/faq.md`](docs/faq.md)
- Troubleshooting: [`docs/troubleshooting.md`](docs/troubleshooting.md)
- API reference: [`docs/api.md`](docs/api.md)
- Architecture: [`docs/architecture.md`](docs/architecture.md)
- Capability registry + catalog policy: [`docs/reference/capabilities-registry.md`](docs/reference/capabilities-registry.md), [`docs/adr/README.md`](docs/adr/README.md)
- Docs index: [`docs/README.md`](docs/README.md)

### First Apple-local MLX model

For Apple Silicon local image generation, prefer the AbstractFramework
MLX-Gen q4 presets first. They are published in the
[AbstractFramework/mlx-gen Hugging Face collection](https://huggingface.co/collections/AbstractFramework/mlx-gen/)
and are the default recommendation for local memory efficiency. q8 variants are
also listed as separate model repos and should be selected explicitly when quality is more important
than memory footprint. Qwen and ERNIE q4 prepared folders can mix q4 and q8
components, but they remain the default prepared choice.

The downloader stores curated presets in the Hugging Face cache by default and
imports older `~/models/<preset>` trees on first use. Generation stays
cache-only unless you explicitly enable runtime downloads.

```bash
pip install "abstractvision[models,mlx-gen]"
abstractvision model-presets
abstractvision catalog --provider mlx-gen
# Tip: `--provider mlx-gen` implies `--target mlx` (you usually set one or the other).
abstractvision download AbstractFramework/flux.2-klein-4b-4bit --provider mlx-gen
abstractvision download AbstractFramework/qwen-image-2512-4bit --provider mlx-gen
abstractvision download AbstractFramework/qwen-image-edit-2511-4bit --provider mlx-gen
abstractvision download AbstractFramework/z-image-turbo-4bit --provider mlx-gen
abstractvision download AbstractFramework/ernie-image-turbo-8bit --provider mlx-gen
abstractvision download briaai/FIBO --provider mlx-gen
abstractvision download briaai/Fibo-lite --provider mlx-gen
abstractvision download briaai/Fibo-Edit --provider mlx-gen
abstractvision download prism-ml/bonsai-image-ternary-4B-mlx-2bit --provider mlx-gen
abstractvision download Wan-AI/Wan2.2-TI2V-5B-Diffusers --provider mlx-gen
abstractvision t2i --provider mlx-gen --model AbstractFramework/flux.2-klein-4b-4bit "a product photo of a matte black espresso machine" --steps 4 --guidance-scale 1.0
abstractvision t2i --provider mlx-gen --model AbstractFramework/flux.2-klein-4b-8bit "a product photo of a matte black espresso machine" --steps 4 --guidance-scale 1.0
abstractvision i2i --provider mlx-gen --model AbstractFramework/qwen-image-edit-2511-4bit --image ./input.png "replace the background with a clean white studio setup" --steps 20 --guidance-scale 2.5 --strength 0.75
abstractvision t2i --provider mlx-gen --model briaai/FIBO "a studio product photo of a white ceramic mug with the AbstractFramework logo" --steps 50 --guidance-scale 4.0
abstractvision t2i --provider mlx-gen --model prism-ml/bonsai-image-ternary-4B-mlx-2bit "a bonsai tree in a quiet ceramic studio" --steps 4 --guidance-scale 1.0
abstractvision i2i --provider mlx-gen --model briaai/Fibo-Edit --image ./input.png "remove the background and keep the object edges clean" --steps 20 --guidance-scale 4.0
abstractvision t2v --provider mlx-gen --model Wan-AI/Wan2.2-TI2V-5B-Diffusers "a red fox walking through a snowy forest, cinematic" --frames 121 --fps 24 --steps 50 --guidance-scale 5.0
abstractvision i2v --provider mlx-gen --model Wan-AI/Wan2.2-TI2V-5B-Diffusers --image ./first-frame.png "slow camera push-in" --frames 121 --fps 24 --steps 50 --guidance-scale 5.0
```

Select q4 or q8 by using the exact published model id, for example
`AbstractFramework/flux.2-klein-4b-4bit` or
`AbstractFramework/flux.2-klein-4b-8bit`. Quantization is metadata of the
published model folder, not a generation-time override.

The shipped MLX-Gen backend currently supports curated q4/q8 prepared folders
for `flux2-klein-4b`, `flux2-klein-9b`, `flux2-klein-base-4b`,
`flux2-klein-base-9b`, `qwen-image`, `qwen-image-edit`, `z-image`, and
`z-image-turbo` families, plus the q4/q8 `ernie-image-turbo` prepared folders.
MLX-Gen 0.18.8+ also runs official runtime snapshots such as `briaai/FIBO`,
`briaai/Fibo-lite`, `briaai/Fibo-Edit`, `briaai/Fibo-Edit-RMBG`,
`prism-ml/bonsai-image-ternary-4B-mlx-2bit`, and
`Wan-AI/Wan2.2-TI2V-5B-Diffusers`. Bonsai is a pre-packed ternary 2-bit
checkpoint, not a q4/q8 prepared folder; use its exact repo id and keep
guidance at 1.0. The binary 1-bit Bonsai checkpoint is not surfaced because
stock MLX cannot run it yet. `image_to_image` is implemented for FLUX.2
klein/base, Qwen Image Edit, ERNIE Image Turbo, FIBO, and FIBO Edit models; FIBO
Edit snapshots support mask inputs where the runtime supports them. Edit strength
is passed as `strength` and normalized to MLX-Gen's `image_strength` parameter
where the runtime supports it. Wan 2.2 TI2V uses the official snapshot through
MLX-Gen 0.18.8+ for Wan 2.2 `text_to_video` and first-frame `image_to_video`; it is not
an AbstractFramework q4/q8 prepared folder, so select it by its exact repo id.

One-shot `t2i`, `i2i`, `t2v`, and `i2v` commands store results in the local
asset store and print an artifact ref followed by the local content path. Use
`--open` when you want the generated output opened after storage, or
`--store-dir <dir>` to choose the asset store directory.
MLX-Gen video commands report frame/step progress on stderr by default; pass
`--no-progress` when you need quiet output.

Stable Diffusion does not currently have a curated MLX-Gen q4/q8 preset in
AbstractVision, so full Diffusers downloads remain explicit.

Install the Diffusers runtime extra, download a Diffusers snapshot, then select
the Diffusers backend explicitly:

```bash
pip install "abstractvision[models,diffusers]"
abstractvision catalog --provider diffusers
# Tip: `--provider diffusers` implies `--target diffusers` (you usually set one or the other).
abstractvision download stable-diffusion --provider diffusers
abstractvision download sd1.4 --provider diffusers
abstractvision download sd1.5-inpaint --provider diffusers
abstractvision download sdxl-base --provider diffusers
abstractvision download sdxl-inpaint --provider diffusers
abstractvision download sd3-medium --provider diffusers
abstractvision download sd3.5-large --provider diffusers
abstractvision download ernie-image --provider diffusers
abstractvision download qwen-image-edit-2511 --provider diffusers
abstractvision download flux2-dev --provider diffusers
export ABSTRACTVISION_BACKEND=diffusers
export ABSTRACTVISION_MODEL_ID=runwayml/stable-diffusion-v1-5
export ABSTRACTVISION_DIFFUSERS_DEVICE=auto
abstractvision cli
```

Notes:
- `abstractvision download qwen-image-edit-2511 --provider diffusers` downloads the curated official 16-bit Diffusers snapshot.
- `GLM-Image` remains in the packaged registry, but local Diffusers `GLM-Image` is temporarily disabled pending the follow-up tracked in [`docs/backlog/planned/0023_local_runtime_capability_quarantine_for_glm_mflux_and_t2v.md`](docs/backlog/planned/0023_local_runtime_capability_quarantine_for_glm_mflux_and_t2v.md).
- `CogVideoX-2b` downloads are still available for experimentation, but local `text_to_video` is currently marked experimental and disabled from the normal product surfaces.

For a fresh cache, you can also permit the interactive CLI to download missing files:

```bash
ABSTRACTVISION_DIFFUSERS_ALLOW_DOWNLOAD=1 abstractvision cli
```

More recommendations by VRAM: [`docs/getting-started.md`](docs/getting-started.md).

### Capability-driven model selection

```python
from abstractvision import VisionModelCapabilitiesRegistry

reg = VisionModelCapabilitiesRegistry()
assert reg.supports("runwayml/stable-diffusion-v1-5", "text_to_image")
assert reg.supports("Qwen/Qwen-Image-Edit-2511", "image_to_image")

print(reg.list_tasks())
print(reg.models_for_task("text_to_image"))
print(reg.models_for_task("image_to_image"))
```

### Backend wiring + generation (artifact outputs)

The base install is import-light and does not install Torch/Diffusers. Heavy
local backend modules are imported lazily (see [`src/abstractvision/backends/__init__.py`](src/abstractvision/backends/__init__.py)).
Install `abstractvision[diffusers]` for local Diffusers, or
`abstractvision[sdcpp]` for the optional stable-diffusion.cpp python binding
fallback.

```python
from abstractvision import LocalAssetStore, VisionManager, VisionModelCapabilitiesRegistry, is_artifact_ref
from abstractvision.backends import OpenAICompatibleBackendConfig, OpenAICompatibleVisionBackend

reg = VisionModelCapabilitiesRegistry()

backend = OpenAICompatibleVisionBackend(
    config=OpenAICompatibleBackendConfig(
        base_url="http://localhost:1234/v1",
        api_key="YOUR_KEY",      # optional for local servers
        model_id="REMOTE_MODEL", # optional (server-dependent)
    )
)

vm = VisionManager(
    backend=backend,
    store=LocalAssetStore(),         # enables artifact-ref outputs
    model_id="Qwen/Qwen-Image-Edit-2511",  # optional: capability gating
    registry=reg,                   # optional: reuse loaded registry
)

out = vm.generate_image("a cinematic photo of a red fox in snow")
assert is_artifact_ref(out)
print(out)  # {"$artifact": "...", "content_type": "...", ...}

png_bytes = vm.store.load_bytes(out["$artifact"])  # type: ignore[union-attr]
```

When installed next to AbstractCore, AbstractVision is also discovered as a
`llm.vision` capability plugin. The plugin defaults to the official OpenAI
image endpoint (`https://api.openai.com/v1`) and reads `OPENAI_API_KEY`.
Set `OPENAI_BASE_URL` when you need a local or remote compatible `/v1` server,
and use the same `OPENAI_API_KEY` bearer token if that endpoint requires auth.
Set `ABSTRACTVISION_BACKEND=openai-compatible` when you want to force
compatible-endpoint semantics. Set `ABSTRACTVISION_MODEL_ID`,
`OPENAI_IMAGE_MODEL_ID`, or `OPENAI_IMAGE_MODEL` when you need an explicit
image model (static default OpenAI model: `gpt-image-1`). AbstractVision does
not query provider `/models` catalogs to discover or select image models
automatically, but you can inspect them explicitly with
`abstractvision provider-models`, `VisionManager.list_provider_models(...)`,
or the AbstractCore plugin method `llm.vision.list_provider_models(...)`.
After inspection, set the model env var explicitly for newer provider models
when available to your account. Set `ABSTRACTVISION_BACKEND=mlx-gen`,
`ABSTRACTVISION_BACKEND=diffusers`, or `ABSTRACTVISION_BACKEND=sdcpp` when you
want AbstractCore to launch local AbstractVision generation directly. For
MLX-Gen, set `ABSTRACTVISION_MFLUX_MODEL=AbstractFramework/flux.2-klein-4b-4bit`
or use routed model ids such as
`mlx-gen/AbstractFramework/flux.2-klein-4b-4bit`. Legacy `mflux` provider values
remain accepted as compatibility aliases.

### Interactive testing (CLI)

```bash
abstractvision models
abstractvision provider-models --openai --task text_to_image
abstractvision provider-models --base-url http://localhost:1234/v1 --task text_to_image
abstractvision tasks
abstractvision show-model runwayml/stable-diffusion-v1-5

abstractvision cli
```

Inside the interactive CLI:

```text
/t2i "a watercolor painting of a lighthouse" --width 512 --height 512 --steps 10 --open
/i2i --image ./input.png "make it watercolor" --steps 20 --guidance-scale 6.5 --open
```

For a newer but still relatively small local model, try `black-forest-labs/FLUX.2-klein-4B` after installing Diffusers
from source (see [`docs/getting-started.md`](docs/getting-started.md)):

```text
/backend diffusers black-forest-labs/FLUX.2-klein-4B mps float16
/t2i "a product photo of a matte black espresso machine" --steps 4 --guidance-scale 1.0 --open
```

Local Diffusers `text_to_video` remains experimental and is temporarily
disabled from the normal bundled local surfaces. Use MLX-Gen Wan 2.2 TI2V for
local Apple Silicon `text_to_video` / `image_to_video`, or use the
OpenAI-compatible backend for remote video endpoints.

For Apple Silicon local generation through MLX-Gen:

```text
/backend mlx-gen AbstractFramework/flux.2-klein-4b-4bit
/t2i "a product photo of a matte black espresso machine" --steps 4 --guidance-scale 1.0 --open
/backend mlx-gen prism-ml/bonsai-image-ternary-4B-mlx-2bit
/t2i "a bonsai tree in a quiet ceramic studio" --steps 4 --guidance-scale 1.0 --open
/backend mlx-gen AbstractFramework/qwen-image-edit-2511-4bit
/i2i --image ./input.png "replace the background with a clean white studio setup" --steps 20 --guidance-scale 2.5 --strength 0.75 --open
/backend mlx-gen Wan-AI/Wan2.2-TI2V-5B-Diffusers
/t2v "a red fox walking through a snowy forest, cinematic" --frames 121 --fps 24 --steps 50 --guidance-scale 5.0 --open
/i2v --image ./first-frame.png "slow camera push-in" --frames 121 --fps 24 --steps 50 --guidance-scale 5.0 --open
```

`/t2v` and `/i2v` show MLX-Gen video progress by default; add
`--no-progress` to suppress progress output in scripts.

OpenAI-compatible server example:

```text
/backend openai http://localhost:1234/v1
/t2i "a watercolor painting of a lighthouse" --width 512 --height 512 --steps 10 --open
```

The CLI/REPL can also be configured via `ABSTRACTVISION_*` env vars; see [`docs/reference/configuration.md`](docs/reference/configuration.md).

### Local web playground

The playground is owned by AbstractVision and runs without AbstractCore. It is
a local/dev testing surface; use AbstractCore/Gateway for production routing,
authentication, and browser-origin policy.

```bash
abstractvision playground --port 8091
```

Open `http://127.0.0.1:8091/vision_playground.html`. The page and the API are served by the same process.

Current behavior:
- The UI is split into task tabs (`Text→Image`, `Image→Image`, `Text→Video`, and a placeholder `Image→Video` tab for later work).
- Each active task tab has its own model selector and unload button. Switching models in a tab unloads the current active backend first to free memory before loading the replacement.
- The Image→Image tab is enabled only for models that both advertise `image_to_image` in the packaged capability registry and remain enabled by the selected backend.
- MLX-Gen FLUX.2 klein/base, Qwen Image Edit, ERNIE Image Turbo, FIBO, and FIBO Edit models are surfaced for `Image→Image` edits when cached.
- The Text→Video tab is experimental in the playground UI. Use shell/REPL or AbstractCore for the most complete MLX-Gen Wan video controls.
- Model-specific request normalization happens at the API/backend layer, not just in the page.
- Local video export packages generated frames into MP4 via an external `ffmpeg` binary on `PATH`.
- Response logs intentionally show only a shortened `b64_json` preview instead of the full base64 image payload.

One-shot commands default to the OpenAI-compatible HTTP backend, but they also support local providers:

```bash
abstractvision t2i --base-url http://localhost:1234/v1 "a studio photo of an espresso machine"
abstractvision i2i --base-url http://localhost:1234/v1 --image ./input.png "make it watercolor"
abstractvision t2i --provider diffusers --model qwen-image "a studio photo of an espresso machine"
```

#### Local GGUF via stable-diffusion.cpp

If you want to run GGUF diffusion models locally, use the stable-diffusion.cpp backend (`sdcpp`). Start with a
single-file Stable Diffusion model when possible; Qwen Image and FLUX GGUF component sets are heavier.

Recommended:
- `abstractvision` auto-installs `sd-cli` into `~/.abstractvision/bin` on first use (set `ABSTRACTVISION_SDCPP_AUTO_INSTALL=0` to disable).
- If you prefer python bindings: install `abstractvision[sdcpp]` (uses `stable-diffusion-cpp-python`).

Alternative (external executable): install `sd-cli` from <https://github.com/leejet/stable-diffusion.cpp/releases>.

In the REPL:

```text
/backend sdcpp /path/to/sd-v1-5.gguf /path/to/sd-cli
/t2i "a watercolor painting of a lighthouse" --width 512 --height 512 --steps 10 --open
```

Curated FLUX/Qwen GGUF bundle example:

```bash
abstractvision download flux2-klein-base-4b --provider sdcpp
abstractvision download qwen-image-edit-2511-gguf --provider sdcpp
```

```text
/backend sdcpp flux2-klein-base-4b /path/to/sd-cli
/t2i "a product photo of a matte black espresso machine" --steps 4 --guidance-scale 1.0 --sampling-method euler --diffusion-fa --offload-to-cpu --open
```

The package resolves the required VAE and text-encoder companions from the cache automatically for curated `sdcpp`
model keys. Manual component wiring remains available for advanced cases.

Extra flags are forwarded via `request.extra`. In CLI mode they are forwarded to `sd-cli`; in python bindings mode, keys are mapped to python binding kwargs when supported and unsupported keys are ignored.

### AbstractCore tool integration (artifact refs)

If you’re using AbstractCore tool calling, AbstractVision can expose vision tasks as tools:

```python
from abstractvision.integrations.abstractcore import make_vision_tools

tools = make_vision_tools(vision_manager=vm, model_id="Qwen/Qwen-Image-Edit-2511")
```

Install `abstractcore` in the host application environment when you use these helpers; it is not pulled in by AbstractVision.

## AbstractFramework ecosystem

AbstractVision is part of the **AbstractFramework** ecosystem and is designed to compose with:

- **AbstractFramework** (project hub): <https://github.com/lpalbou/AbstractFramework>
- **AbstractCore** (orchestration + tool calling): <https://github.com/lpalbou/abstractcore>
- **AbstractRuntime** (runtime services, including artifact storage): <https://github.com/lpalbou/abstractruntime>

In practice:
- AbstractVision standardizes *generative vision outputs* (image/video) behind `VisionManager`.
- AbstractCore can discover and use AbstractVision via the capability plugin (`src/abstractvision/integrations/abstractcore_plugin.py`) or you can expose vision tasks as tools (`src/abstractvision/integrations/abstractcore.py`).
- Artifact refs returned by AbstractVision are designed to travel across processes; `RuntimeArtifactStoreAdapter` bridges to an AbstractRuntime-style artifact store (`src/abstractvision/artifacts.py`).

## Project

- Release notes: [`CHANGELOG.md`](CHANGELOG.md)
- Contributing: [`CONTRIBUTING.md`](CONTRIBUTING.md)
- Security: [`SECURITY.md`](SECURITY.md)
- Acknowledgments: [`ACKNOWLEDGMENTS.md`](ACKNOWLEDGMENTS.md)
- Agent docs: [`llms.txt`](llms.txt) and [`llms-full.txt`](llms-full.txt)

## Requirements

- Python >= 3.9

## License

MIT License - see LICENSE file for details.

## Author

Laurent-Philippe Albou

## Contact

contact@abstractcore.ai
