This release is a pre-release and may not be stable for production use.
Diffusers Dynamic Offloader
DDO is a model-agnostic offload manager for low-VRAM Diffusers-style inference. It provides dense-weight routes and official Diffusers compatibility routes while preserving quantized backend math.
Getting started
Follow the installation guide, then choose full-pipeline, staged or Modular Diffusers integration.
Normal linear use requires no native allocator or compiler. Native VMM is optional: it uses a matching packaged DLL/.so and bootstrap before CUDA initialization. The installation guide describes local candidate wheels; it does not assume they are available on a package index.
from diffusers_dynamic_offloader import enable_offload
result = enable_offload(transformer, preset="auto", component="transformer")
transformer = result.module
This snippet selects the default linear route. See native VMM integration for its separate loading contract.
Documentation
- User guides: installation, integration, presets and reporting.
- Developer guides: architecture, diagnostics, builds and validation.
Examples and benchmarks
Model runners, custom-block integration and measured comparisons live in DDO examples. See the test hardware and support boundaries when interpreting results. Selected workloads do not establish universal speedups or model support.
License
Apache-2.0; preserve required copyright, license and attribution notices.
Metadata
Release files for diffusers-dynamic-offloader 0.1.0rc2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| diffusers_dynamic_offloader-0.1.0rc2-py3-none-any.whl | Python 3 | none | any | Details |
Release files / diffusers_dynamic_offloader-0.1.0rc2-py3-none-any.whl
| Download URL | diffusers_dynamic_offloader-0.1.0rc2-py3-none-any.whl |
|---|---|
| Size | 99.7 kB |
| Tags | Python 3 |
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