abstractvision 0.3.5

Model-agnostic generative vision abstractions (image/video) for the Abstract ecosystem

AbstractVision

Model-agnostic generative vision API (images, optional video) for Python and the Abstract* ecosystem.

What you get

• A small orchestration API: VisionManager
• A packaged capability registry (“what models can do”): VisionModelCapabilitiesRegistry backed by vision_model_capabilities.json
• Optional artifact-ref outputs (small JSON refs): LocalAssetStore and RuntimeArtifactStoreAdapter
• Built-in backends (execution engines): src/abstractvision/backends/
  • OpenAI-compatible HTTP: openai_compatible.py
  • Local Diffusers: huggingface_diffusers.py
  • Local stable-diffusion.cpp / GGUF: stable_diffusion_cpp.py
• CLI/REPL for manual testing: abstractvision
• Self-contained local Playground UI/API: playground/vision_playground.html (docs: playground/README.md)

How it fits together (diagram)

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: text_to_image and image_to_image.
• Video (text_to_video, image_to_video) is supported only via 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.

Installation

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[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[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 plus stable-diffusion.cpp bindings.
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. In that case you may need Diffusers from source (main):

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

For local development from a repo checkout:

pip install -e ".[dev]"

Usage

Start here:

• Getting started: docs/getting-started.md
• FAQ: docs/faq.md
• API reference: docs/api.md
• Architecture: docs/architecture.md
• Docs index: docs/README.md

First local model (Diffusers / cross-platform)

Install the local runtime extra, pre-download the model outside the REPL, then select the Diffusers backend explicitly:

pip install "abstractvision[diffusers]"
huggingface-cli download runwayml/stable-diffusion-v1-5
export ABSTRACTVISION_BACKEND=diffusers
export ABSTRACTVISION_MODEL_ID=runwayml/stable-diffusion-v1-5
export ABSTRACTVISION_DIFFUSERS_DEVICE=auto
abstractvision repl

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

ABSTRACTVISION_DIFFUSERS_ALLOW_DOWNLOAD=1 abstractvision repl

More recommendations by VRAM: docs/getting-started.md.

Capability-driven model selection

from abstractvision import VisionModelCapabilitiesRegistry

reg = VisionModelCapabilitiesRegistry()
assert reg.supports("runwayml/stable-diffusion-v1-5", "text_to_image")

print(reg.list_tasks())
print(reg.models_for_task("text_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). Install abstractvision[diffusers] for local Diffusers, or abstractvision[sdcpp] for the optional stable-diffusion.cpp python binding fallback.

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="zai-org/GLM-Image",    # 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 (or ABSTRACTVISION_API_KEY). Set OPENAI_BASE_URL only when you need to override that OpenAI-compatible base for the official OpenAI profile. Set ABSTRACTVISION_BACKEND=openai-compatible plus ABSTRACTVISION_BASE_URL for a local or remote compatible /v1 server. 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=diffusers or ABSTRACTVISION_BACKEND=sdcpp when you want AbstractCore to launch local AbstractVision generation directly.

Interactive testing (CLI / REPL)

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 repl

Inside the REPL:

/t2i "a watercolor painting of a lighthouse" --width 512 --height 512 --steps 10 --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):

/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

OpenAI-compatible server example:

/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.

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.

abstractvision playground --port 8091

Open http://127.0.0.1:8091/vision_playground.html, select a cached model, then load it. The page and the API are served by the same process.

One-shot commands (OpenAI-compatible HTTP backend only):

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"

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:

• macOS (Apple Silicon / Metal): install sd-cli (stable-diffusion.cpp executable) from releases and use CLI mode for Metal acceleration.
• Otherwise (pip-only convenience): pip install "abstractvision[sdcpp]" installs the stable-diffusion.cpp python bindings (stable-diffusion-cpp-python>=0.4.2,<0.4.6), but this may run CPU-only depending on the wheel build.

Alternative (external executable):

• Install sd-cli: https://github.com/leejet/stable-diffusion.cpp/releases

In the REPL:

/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

FLUX.2-klein-4B GGUF component example:

/backend sdcpp /path/to/flux-2-klein-4b-Q8_0.gguf /path/to/flux2_ae.safetensors /path/to/Qwen3-4B-Q4_K_M.gguf /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

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:

from abstractvision.integrations.abstractcore import make_vision_tools

tools = make_vision_tools(vision_manager=vm, model_id="zai-org/GLM-Image")

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
• Contributing: CONTRIBUTING.md
• Security: SECURITY.md
• Acknowledgments: ACKNOWLEDGMENTS.md
• Agent docs: llms.txt and llms-full.txt

Requirements

• Python >= 3.9

License

MIT License - see LICENSE file for details.

Author

Laurent-Philippe Albou

Contact

[e-mail]