nvavif_py

Hardware-accelerated AVIF encoding/decoding for Python, powered by NVIDIA NVENC — with automatic CPU fallback.

nvavif_py leverages the AV1 hardware encoder (NVENC) available on modern NVIDIA GPUs to convert images into the AVIF (AV1 Image File Format) at exceptional speed.

Built as a native Rust extension via PyO3, it bridges the NVENC AV1 encoder with the avif-serialize crate to produce standards-compliant AVIF files, uses rav1e as a built-in CPU software encoder fallback, dav1d as fastest cpu-decoder and rayon for parallelized pixel preprocessing — all from a simple Python API.

Key Features

Hardware Accelerated: Uses av1_nvenc for lightning-fast encoding.
Automatic CPU Fallback: When NVENC is unavailable, transparently switches to the built-in multithreaded rav1e software encoder — no extra packages, no code changes needed.
High Bit Depth: Support for 8-bit and 10-bit color depths.
Chroma Flexibility: Support for YUV420 (standard) and YUV444 (high fidelity/text).
Color Matrix Control: BT.601, BT.709, and BT.2020 matrix selection with proper CICP metadata in the AVIF container.
Alpha Support: Correctly encodes transparency via a secondary AV1 auxiliary plane with independent quality control.
Auto-CQ (SSIM-guided quality): Automatically finds the optimal quantization level to hit a target SSIM perceptual quality score, using a two-probe secant approximation.
Device Selection: Explicit device="gpu", device="cpu", or device="auto" routing — pin encoding to CPU even when a GPU is present.
Multi-format Input: Accepts uint8, uint16, and float32 NumPy arrays, including HDR data. Float input is tone-mapped via the ACES Filmic operator before encoding.
GPU Tensor Support: Direct ingestion of PyTorch and CuPy GPU tensors — automatically migrated to CPU without user intervention.
EXIF Embedding: Raw EXIF metadata can be injected directly into the AVIF container.
Pillow Plugin: Auto-registers as a Pillow save handler — use img.save("out.avif") directly.
Smart Preprocessing:
- Parallel YUV conversion via Rust/Rayon.
- Automatic ICC profile → sRGB conversion.
- Automatic EXIF orientation handling.
- Automatic handling of NVENC's "even-dimension" requirement (auto-crops odd pixels).
- Graceful handling of all Pillow image modes (P, L, LA, I, F, RGBA, etc.).
- Automatic dtype normalization for NumPy arrays (float16, float64, int32, bool → uint8/float32).
GIL-Free GPU Encoding: Releases Python's GIL during the GPU encoding phase, enabling true multithreaded parallelism with ThreadPoolExecutor.
Flexible Input: Accepts PIL Images, NumPy arrays, file paths, and raw bytes.
Numpy & PIL Integration: Works out-of-the-box with numpy arrays and Pillow images.
Zero Configuration: Total "plug-and-play" experience.
Ultra-Fast Decoding: CPU-optimized AVIF GIL-free decoder powered by dav1d with custom fixed-point YUV→RGB conversion.
- 3–4× faster than standard Pillow/pillow-avif-plugin.
- Direct-to-NumPy: Zero-copy architecture — decoded pixels go straight into NumPy arrays.
- ML-Ready: Optimized for PyTorch/TensorFlow DataLoaders with configurable threading.
- Example: 5184×3456px AVIF → NumPy array in ~80ms (disk read + decode).

Why Hardware Encoding? Why Fast Decoding?

Software AV1 encoders (libaom, rav1e, SVT-AV1) produce excellent results but are CPU-intensive and slow, especially at high quality settings. NVIDIA's NVENC offloads the entire encoding pipeline to dedicated silicon on the GPU. nvavif_py includes both paths — GPU and CPU — selectable at runtime:

Metric	Software (CPU)	NVENC (GPU)
Encoding time	Dozens of seconds per image	Milliseconds per image
Encoding speed	1x	200x
CPU load	100% across cores	Near zero
Throughput	1-4 images/min	Thousands of images/min
Power efficiency	High wattage	Minimal additional draw

The built-in CPU path (rav1e) is also multi-threaded and works on any machine — no GPU required. The GPU path is preferred automatically when NVENC is available.

This makes nvavif_py ideal for workloads where throughput matters more than squeezing out the last byte of compression, while remaining fully functional everywhere.

Decoding Performance

Metric	Standard Pillow	nvavif_py.decode_file()
Decode time	320ms (5184×3456px AVIF)	~80ms
Decode speed	1×	4×
Memory copies	3+ (codec → PIL → NumPy)	1 (codec → NumPy)
YUV→RGB math	Float (`swscale`)	Fixed-point (bit-shift)
CPU vectorization	Partial	Full (AVX2/NEON)
ML/AI ready	No (slow)	Yes (optimized)

Hardware Requirements

Requirement	Details
GPU	NVIDIA Ada Lovelace (RTX 40X0) or Blackwell (RTX 50X0) and newer
Driver	NVIDIA driver with NVENC AV1 support (≥ 570.0 on Windows, ≥ 570.0 on Linux)
OS	Linux (x86_64) or Windows (x64)

Note:

Older NVIDIA architectures (Turing, Ampere) support NVENC for H.264/HEVC but do not support AV1 encoding. The is_supported() function lets you check at runtime.

No compatible GPU? No problem. nvavif_py automatically falls back to its built-in rav1e CPU encoder — zero configuration, zero extra dependencies.

Decoding Performance

While nvavif_py is famous for GPU-accelerated encoding, it also includes the fastest AVIF decoder available in the Python ecosystem.

Why CPU Decoding?

For single-frame decoding (the common case for images), CPU-based dav1d is faster than GPU (NVDEC) because:

No session initialization overhead — GPU decoders expect video streams and have setup costs.
Lower latency — Direct memory access without PCIe transfers.
Better CPU utilization — Modern CPUs handle single-image decoding in microseconds.

Decoder Architecture

Component	Implementation
Demuxer	`libavformat` — minimal probing, zero-overhead container parsing.
AV1 Decoder	`dav1d` — the world's fastest AV1 decoder, written in hand-tuned C.
YUV→RGB	Custom Rust converter with fixed-point math (`i32` bit-shifts, no `f32`).
Parallelization	`rayon` — parallel row processing across all CPU cores.
Output	Direct `numpy.ndarray` (zero-copy via `frombuffer`).

Performance Comparison

Decoding 5184×3456px AVIF to numpy.ndarray:

Library	Time	Notes
nvavif_py	~80ms	Includes disk I/O + decode + YUV→RGB
Pillow (pillow-avif-plugin)	~360ms	Uses libaom (slow) + extra memory copies
OpenCV (`cv2.imread`)	~280ms	Uses libavcodec + swscale (float math)
imageio + av	~400ms	Python overhead + PyAV wrapper

Speedup: nvavif_py is 3–4× faster than standard tools.

Why It's Fast

Fixed-Point Math: YUV→RGB conversion uses integer arithmetic with bit-shifts (>> 10) instead of floating-point multiplications. This enables CPU vectorization (AVX2/NEON).
Parallel Decoding: dav1d uses SIMD and multi-threading internally. The threads parameter lets you control CPU core usage (set to 1 for DataLoader workers to avoid thrashing).
Zero-Copy Pipeline: Decoded YUV planes → Rust converter → NumPy buffer. No intermediate PIL Image or Python lists.
Minimal Probing: libavformat demuxer is configured with probesize=4096 and analyzeduration=0 to skip unnecessary format detection.

Ideal for ML/AI Training Pipelines

When training neural networks, the DataLoader is often a bottleneck. AVIF offers 30–70% smaller datasets than PNG/JPEG, but only if decoding is fast enough to saturate GPU training.

nvavif_py's decoder is purpose-built for this:

from torch.utils.data import Dataset
import nvavif_py
import torch

class AVIFDataset(Dataset):
    def __init__(self, image_paths):
        self.paths = image_paths
    
    def __getitem__(self, idx):
        # Decode AVIF in 1–2 threads (avoid CPU contention with other workers)
        img = nvavif_py.decode_file(self.paths[idx], threads=2)
        return torch.from_numpy(img).permute(2, 0, 1).float() / 255.0
    
    def __len__(self):
        return len(self.paths)

Result: You can use AVIF datasets without sacrificing training speed — in many cases, decoding is faster than PNG due to smaller file sizes (less disk I/O).

Installation

uv add nvavif_py

or

pip install nvavif_py

Pre-built wheels bundle all required libraries (NVENC, dav1d, rav1e) — no system-level installation needed.

Quick Start

import nvavif_py

# GPU is preferred automatically; CPU rav1e is used if NVENC is unavailable
print("GPU available:", nvavif_py.is_supported())

# Encode from a file path
avif_bytes = nvavif_py.encode_file("photo.png")

# Save the result
with open("photo.avif", "wb") as f:
    f.write(avif_bytes)

Pillow Plugin (Zero-Config, works with CPU fallback too)

nvavif_py always registers itself as a Pillow save plugin regardless of GPU availability. No extra imports or setup required:

from PIL import Image

img = Image.open("photo.png")

# Just save as .avif — nvavif_py handles the rest
img.save("photo.avif")

# With Pillow-style quality (0–100 scale, enables auto_cq automatically)
img.save("photo.avif", quality=80)

# With nvavif_py-native parameters
from nvavif_py import Chroma, ColorDepth, Device
img.save("photo.avif", cq=18, chroma=Chroma.YUV444, depth=ColorDepth.TEN_BIT)

# Force CPU encoding (skip NVENC even if available)
img.save("photo.avif", cq=20, device=Device.CPU)

The plugin maps Pillow's quality parameter (0–100) to nvavif_py's target_ssim scale and enables auto_cq=True automatically for perceptual quality targeting.

API Reference

`is_supported() → bool`

Checks whether the current system has a compatible GPU and driver for AV1 hardware encoding.

if nvavif_py.is_supported():
    print("NVENC AV1 is ready")
else:
    print("NVENC unavailable — using built-in rav1e CPU encoder")

`encode_file(input_source, ...) → bytes`

Encodes an image from a variety of sources into AVIF format. Automatically selects the GPU (NVENC) or CPU (rav1e) encoder based on availability and the device parameter.

nvavif_py.encode_file(
    input_source,            # See "Accepted Input Types" below
    cq=20,                   # Color quality: 0 (best) – 51 (smallest)
    auto_cq=False,           # Enable SSIM-guided automatic CQ selection
    target_quality=80.0,     # Target quality (0–100 scale or raw SSIM ≤ 1.0)
    alpha_cq=None,           # Alpha quality (auto-calculated if None)
    preset=NvencPreset.P7_MAX_QUALITY,  # quality tradeoff / gpu load
    depth=ColorDepth.EIGHT_BIT,         # 8-bit or 10-bit
    chroma=Chroma.YUV420,               # Chroma subsampling
    matrix=ColorMatrix.BT709,           # Color matrix for YUV conversion
    exif=None,                           # Raw EXIF bytes to embed in AVIF
    device=Device.AUTO,                  # "auto", "gpu", or "cpu"
)

Accepted Input Types

Type	Description
`str` / `Path` / `os.PathLike`	File path to an image on disk
`bytes`	Raw image file bytes (PNG, JPEG, etc.)
`io.BytesIO`	In-memory binary stream
`PIL.Image.Image`	Pillow image object (any mode — auto-converted)
`numpy.ndarray`	NumPy array of shape `(H, W, C)` (any numeric dtype)
PyTorch `Tensor` (GPU/CPU)	Auto-detached and moved to CPU via `.detach().cpu()`
CuPy `ndarray`	Auto-converted via `.get()`

Parameters

Parameter	Type	Default	Description
`cq`	`int`	`20`	Constant quality level. Lower = higher quality, larger file. Clamped 0–51.
`auto_cq`	`bool`	`False`	When `True`, ignores `cq` and automatically selects the quantizer to hit `target_quality`.
`target_quality`	`float`	`80.0`	Quality target. Values > 1.0 use a 0–100 scale; values ≤ 1.0 are treated as raw SSIM (e.g. `0.985`).
`alpha_cq`	`int \| None`	`None`	Quality for alpha channel. If `None`, defaults to `cq - 4` (slightly better than color plane).
`preset`	`NvencPreset`	`P7_MAX_QUALITY`	NVENC preset (P1–P7). Higher = better compression, slightly slower. Also maps to rav1e speed on CPU.
`depth`	`ColorDepth`	`EIGHT_BIT`	Bit depth per channel: `EIGHT_BIT` or `TEN_BIT`.
`chroma`	`Chroma`	`YUV420`	Chroma subsampling: `YUV420` or `YUV444`.
`matrix`	`ColorMatrix`	`BT709`	YUV color matrix: `BT709` (HD), `BT601` (SD/legacy), or `BT2020` (wide gamut/HDR).
`exif`	`bytes \| None`	`None`	Raw EXIF metadata bytes to embed in the AVIF container.
`device`	`Device \| str`	`Device.AUTO`	`"auto"` = prefer GPU; `"gpu"` = force GPU (raises if unavailable); `"cpu"` = force rav1e CPU encoder.

Automatic Preprocessing

When the input is a PIL Image or a file path/bytes, the following preprocessing is applied automatically:

Step	Description
ICC → sRGB	Images with embedded ICC profiles are converted to sRGB.
EXIF orientation	EXIF rotation/flip tags are applied and the image is normalized.
Mode conversion	Palette (P), Grayscale (L/LA), Integer (I), Float (F) → RGB/RGBA.
Even dimensions	Odd width/height is auto-cropped by 1 pixel (NVENC hardware requirement).
dtype normalization	`uint8` → native; `uint16` → native 16-bit path; `float32` → ACES tone-mapped 32-bit path; other → `uint8`.
GPU tensor migration	PyTorch/CuPy GPU tensors are automatically moved to CPU NumPy before encoding.

Quality Guidelines (`cq`)

Range	Use Case
0–10	Archival / near-lossless. Large files.
11–18	Visually indistinguishable from the original.
20–30	High efficiency — optimal for web delivery.
31–51	Aggressive compression.

Note: NVENC requires image dimensions to be even numbers.

Odd-dimensioned images are automatically cropped by 1 pixel on the right or bottom edge.

Enumerations

`ColorDepth`

from nvavif_py import ColorDepth

ColorDepth.EIGHT_BIT   # Standard 8-bit (sRGB content)
ColorDepth.TEN_BIT     # 10-bit (HDR, wide gamut, banding reduction)

`Chroma`

from nvavif_py import Chroma

Chroma.YUV420  # 4:2:0 — Best compression. Ideal for photos and video frames.
Chroma.YUV444  # 4:4:4 — Full chroma resolution. Ideal for text, graphics, UI screenshots.

`ColorMatrix`

from nvavif_py import ColorMatrix

ColorMatrix.BT709   # Rec. 709 — Standard for HD/modern content (recommended default)
ColorMatrix.BT601   # Rec. 601 — Standard for SD/legacy content
ColorMatrix.BT2020  # Rec. 2020 — Wide color gamut, HDR/UHDTV content

The selected matrix affects both the RGB → YUV conversion math and the CICP metadata written into the AVIF container (color primaries + matrix coefficients), ensuring decoders interpret colors correctly.

`NvencPreset`

from nvavif_py import NvencPreset

NvencPreset.P1_LOW_QUALITY    # Minimal GPU usage, low efficiency
NvencPreset.P2_MEDIUM_LOW
NvencPreset.P3_MEDIUM
NvencPreset.P4_MEDIUM_HIGH
NvencPreset.P5_HIGH
NvencPreset.P6_VERY_HIGH
NvencPreset.P7_MAX_QUALITY    # Best compression (recommended for images)

On the CPU path (rav1e), the preset maps to encoder speed: P7 → speed 4, P1 → speed 10. Lower speed = better compression, more CPU time.

`Device`

from nvavif_py import Device

Device.AUTO   # Prefer GPU if NVENC is available, otherwise use CPU rav1e (default)
Device.GPU    # Force GPU encoding; raises ValueError if NVENC is not supported
Device.CUDA   # Alias for Device.GPU
Device.CPU    # Force CPU encoding via built-in rav1e, regardless of GPU availability

`DataType`

from nvavif_py import DataType

DataType.U8   # 'u8'  — unsigned 8-bit integer NumPy arrays
DataType.U16  # 'u16' — unsigned 16-bit integer NumPy arrays (wider dynamic range)
DataType.F32  # 'f32' — 32-bit float NumPy arrays (HDR; ACES tone-mapping applied)

DataType is inferred automatically by encode_file() from the NumPy array dtype — you only need it when calling the low-level _nvavif_py.encode_avif() directly.

`decode_file(path, threads=0) → np.ndarray`

Decodes an AVIF file directly into a NumPy array using the ultra-fast dav1d decoder.

import nvavif_py

img_array = nvavif_py.decode_file("photo.avif", threads=0)
# Returns: numpy.ndarray with shape (H, W, C), dtype=uint8

Parameters

Parameter	Type	Default	Description
`path`	`str \| Path`	—	Path to the `.avif` file.
`threads`	`int`	`0`	Number of decoder threads. `0` = auto (all cores). Set `1–2` for DataLoader workers to avoid CPU contention.

Returns

Type	Description
`numpy.ndarray`	Decoded image with shape `(height, width, channels)`, dtype `uint8`. Channels: 3 (RGB) or 4 (RGBA).

Threading Recommendations

Use Case	Recommended `threads`	Reason
Single-image decoding	`0` (auto)	Use all cores for maximum speed.
PyTorch/TF DataLoader workers	`1` or `2`	Avoid CPU thrashing when multiple workers run in parallel.
Batch processing (loop)	`0` (auto)	Each iteration uses full CPU, then releases.
Batch processing (parallel)	`1` or `2`	Let outer parallelism (ThreadPoolExecutor) manage cores.

Supported Formats

The decoder handles all standard AVIF/AV1 output configurations automatically and converts to 8-bit RGB(A) NumPy arrays:

Pixel Format	Chroma	Bit Depth	Alpha	Notes
YUV420P	4:2:0	8-bit	✅	Most common (photos, web images)
YUV422P	4:2:2	8-bit	✅	Intermediate quality
YUV444P	4:4:4	8-bit	✅	Full chroma (graphics, text)
YUV420P10LE	4:2:0	10-bit	❌	HDR content
YUV422P10LE	4:2:2	10-bit	❌	Professional video
YUV444P10LE	4:4:4	10-bit	❌	High-fidelity graphics
YUVA444P10LE	4:4:4	10-bit	✅	10-bit with alpha channel

Note: 10-bit output is automatically downscaled to 8-bit (uint8) for compatibility with standard image processing libraries.

Usage Examples

Basic File Conversion

import nvavif_py

avif_data = nvavif_py.encode_file("input.png", cq=22)
with open("output.avif", "wb") as f:
    f.write(avif_data)

High-Quality with Alpha Transparency

avif_data = nvavif_py.encode_file(
    "logo_transparent.png",
    cq=12,
    alpha_cq=8,   # Preserve alpha with higher fidelity
)

10-bit HDR Encoding

from nvavif_py import ColorDepth

avif_data = nvavif_py.encode_file(
    "hdr_photo.png",
    cq=18,
    depth=ColorDepth.TEN_BIT,
)

YUV444 for Screenshots and Graphics

from nvavif_py import Chroma

avif_data = nvavif_py.encode_file(
    "screenshot.png",
    cq=16,
    chroma=Chroma.YUV444,  # Preserves sharp text and color edges
)

Color Matrix Selection

from nvavif_py import ColorMatrix, ColorDepth

# Modern HD content (default)
avif_data = nvavif_py.encode_file("photo.png", matrix=ColorMatrix.BT709)

# Legacy SD content
avif_data = nvavif_py.encode_file("old_video_frame.png", matrix=ColorMatrix.BT601)

# Wide color gamut / HDR (BT.2020)
avif_data = nvavif_py.encode_file("hdr_photo.png", matrix=ColorMatrix.BT2020, depth=ColorDepth.TEN_BIT)

Auto-CQ: SSIM-Guided Quality

auto_cq=True automatically finds the best quantizer to hit a target perceptual quality, without trial-and-error:

import nvavif_py

# Target quality on 0–100 scale (maps to SSIM internally)
avif_data = nvavif_py.encode_file(
    "photo.png",
    auto_cq=True,
    target_quality=85.0,  # 85/100 quality
)

# Or pass raw SSIM directly (value ≤ 1.0)
avif_data = nvavif_py.encode_file(
    "photo.png",
    auto_cq=True,
    target_quality=0.985,  # raw SSIM target
)

How it works: Two trial encodings of a 512×512 mosaic patch are performed (one at CQ 28, one at CQ 16 or 44 depending on target direction). A secant approximation estimates the CQ value that will hit the target SSIM. The final CQ is clamped to 0–51. A safeguard prevents runaway bitrate on noisy sources (e.g., heavy JPEG artifacts).

Device Selection

from nvavif_py import Device

# Default: GPU if available, CPU otherwise
avif_data = nvavif_py.encode_file("photo.png", device=Device.AUTO)

# Force GPU — raises ValueError if NVENC is not supported
avif_data = nvavif_py.encode_file("photo.png", device=Device.GPU)

# Force CPU (rav1e) — useful for benchmarking or CI environments
avif_data = nvavif_py.encode_file("photo.png", device=Device.CPU)

HDR Float32 Input (ACES Tone-Mapping)

Float32 arrays with values outside the [0, 1] range (e.g. HDR render outputs, EXR data) are automatically tone-mapped using the ACES Filmic operator before encoding:

import numpy as np
import nvavif_py
from nvavif_py import ColorDepth, ColorMatrix

# Simulate HDR data with super-bright highlights (values > 1.0)
hdr_data = np.random.rand(1080, 1920, 3).astype(np.float32) * 5.0

avif_data = nvavif_py.encode_file(
    hdr_data,
    depth=ColorDepth.TEN_BIT,
    matrix=ColorMatrix.BT2020,
    cq=16,
)

ACES Filmic formula: f(x) = (x*(2.51x + 0.03)) / (x*(2.43x + 0.59) + 0.14), clamped to [0, 1]. Preserves highlight detail without hard clipping.

PyTorch / CuPy GPU Tensor Input

import torch
import nvavif_py

# Works with GPU tensors directly — no manual .cpu() call needed
tensor = torch.rand(1080, 1920, 3, dtype=torch.float32).cuda()
avif_data = nvavif_py.encode_file(tensor, cq=20)

# Also works with CPU tensors
tensor_cpu = torch.rand(1080, 1920, 3, dtype=torch.uint8)
avif_data = nvavif_py.encode_file(tensor_cpu, cq=20)

EXIF Embedding

from PIL import Image
import nvavif_py

img = Image.open("photo.jpg")

# Extract raw EXIF from source image
exif_bytes = img.info.get("exif", b"")

avif_data = nvavif_py.encode_file(img, cq=20, exif=exif_bytes)
with open("photo_with_exif.avif", "wb") as f:
    f.write(avif_data)

encode_file() also extracts EXIF from PIL Images automatically if they carry it in img.info["exif"] — no manual extraction needed in most cases.

Pillow Plugin — Save Directly

from PIL import Image

img = Image.open("photo.jpg")
img = img.resize((1920, 1080))

# Option 1: Pillow-style quality (0–100)
img.save("output.avif", quality=85)

# Option 2: nvavif_py-native parameters
from nvavif_py import Chroma, ColorDepth
img.save("output.avif", cq=18, depth=ColorDepth.TEN_BIT, chroma=Chroma.YUV444)

# Force CPU encoder via Pillow plugin
from nvavif_py import Device
img.save("output.avif", cq=20, device=Device.CPU)

From a NumPy Array

import numpy as np
import nvavif_py
from nvavif_py import ColorDepth

# Synthetic gradient image (H, W, C)
arr = np.zeros((1080, 1920, 3), dtype=np.uint8)
arr[:, :, 0] = np.linspace(0, 255, 1920, dtype=np.uint8)  # Red gradient
avif_data = nvavif_py.encode_file(arr, cq=20)

# uint16 arrays — 16-bit precision path (no downscaling to uint8)
arr_u16 = np.random.randint(0, 65535, (1080, 1920, 3), dtype=np.uint16)
avif_data = nvavif_py.encode_file(arr_u16, cq=20, depth=ColorDepth.TEN_BIT)

# Float arrays — ACES tone-mapping applied automatically
arr_float = np.random.rand(1080, 1920, 3).astype(np.float32) * 2.0
avif_data = nvavif_py.encode_file(arr_float, cq=20)

From In-Memory Bytes

import nvavif_py

# e.g., downloaded from a network request
image_bytes = download_image_from_url("https://example.com/photo.jpg")
avif_data = nvavif_py.encode_file(image_bytes, cq=25)

Batch Conversion (Sequential)

from pathlib import Path
import nvavif_py

input_dir = Path("images/")
output_dir = Path("avif_output/")
output_dir.mkdir(exist_ok=True)

for img_path in input_dir.glob("*.png"):
    avif_data = nvavif_py.encode_file(img_path, cq=22)
    (output_dir / img_path.with_suffix(".avif").name).write_bytes(avif_data)
    print(f"Converted {img_path.name}")

Multi-Threaded Batch Conversion

nvavif_py releases Python's GIL during the GPU encoding phase. This means ThreadPoolExecutor achieves true parallelism — multiple NVENC sessions run simultaneously on the GPU hardware.

RTX 4090/5090 supports up to 8 concurrent NVENC sessions and has 2 physical AV1 encoder chips, so multiple threads can saturate the hardware:

from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
import nvavif_py

input_dir = Path("images/")
output_dir = Path("avif_output/")
output_dir.mkdir(exist_ok=True)

image_paths = list(input_dir.glob("*.png"))

def convert_one(img_path: Path) -> str:
    avif_data = nvavif_py.encode_file(img_path, cq=22)
    out_path = output_dir / img_path.with_suffix(".avif").name
    out_path.write_bytes(avif_data)
    return img_path.name

# Entry-level and mid-range RTX GPUs have a single encoder chip and support up to 4 parallel NVENC sessions.
# High-end RTX GPUs have up to 8 parallel NVENC sessions:
with ThreadPoolExecutor(max_workers=8) as pool:
    futures = {pool.submit(convert_one, p): p for p in image_paths}
    for future in as_completed(futures):
        print(f"Converted {future.result()}")

How it works: Each thread calls encode_file(), which prepares YUV data on the CPU (with GIL held), then releases the GIL and submits the frame to an independent NVENC session on the GPU. While the GPU is encoding, other Python threads are free to prepare and submit their own frames.

Web Server Integration (FastAPI)

from fastapi import FastAPI, UploadFile
from fastapi.responses import Response
import nvavif_py

app = FastAPI()

@app.post("/convert")
async def convert_to_avif(file: UploadFile):
    image_bytes = await file.read()
    avif_data = nvavif_py.encode_file(image_bytes, cq=24)
    return Response(content=avif_data, media_type="image/avif")

Decoding AVIF Files

Basic Decoding

import nvavif_py
import numpy as np

# Decode AVIF to NumPy array (auto-threading)
img = nvavif_py.decode_file("photo.avif")
print(img.shape, img.dtype)  # (1080, 1920, 3) uint8

# Display with matplotlib
import matplotlib.pyplot as plt
plt.imshow(img)
plt.show()

Save to PNG

from PIL import Image
import nvavif_py

img = nvavif_py.decode_file("photo.avif")
Image.fromarray(img).save("photo.png")

Batch Decoding (Sequential)

from pathlib import Path
import nvavif_py

avif_dir = Path("dataset/")
for avif_path in avif_dir.glob("*.avif"):
    img = nvavif_py.decode_file(avif_path)
    print(f"Loaded {avif_path.name}: {img.shape}")

Batch Decoding (Parallel)

from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
import nvavif_py

avif_paths = list(Path("dataset/").glob("*.avif"))

def decode_one(path):
    # Use 1-2 threads per worker to avoid CPU contention
    return nvavif_py.decode_file(path, threads=2)

with ThreadPoolExecutor(max_workers=8) as pool:
    images = list(pool.map(decode_one, avif_paths))

print(f"Decoded {len(images)} images")

PyTorch DataLoader Integration

from torch.utils.data import Dataset, DataLoader
import nvavif_py
import torch

class AVIFImageDataset(Dataset):
    def __init__(self, image_paths, transform=None):
        self.paths = image_paths
        self.transform = transform
    
    def __len__(self):
        return len(self.paths)
    
    def __getitem__(self, idx):
        # Decode with 1-2 threads (DataLoader uses multiple workers)
        img = nvavif_py.decode_file(self.paths[idx], threads=1)
        
        # Convert to PyTorch tensor (HWC → CHW)
        img = torch.from_numpy(img).permute(2, 0, 1).float() / 255.0
        
        if self.transform:
            img = self.transform(img)
        
        return img

# Usage
from pathlib import Path
paths = list(Path("dataset/").glob("*.avif"))
dataset = AVIFImageDataset(paths)
loader = DataLoader(dataset, batch_size=32, num_workers=4, shuffle=True)

for batch in loader:
    print(batch.shape)  # torch.Size([32, 3, H, W])
    break

TensorFlow/Keras Data Pipeline

import tensorflow as tf
import nvavif_py
import numpy as np

def load_avif(path):
    path_str = path.numpy().decode('utf-8')
    img = nvavif_py.decode_file(path_str, threads=2)
    return img.astype(np.float32) / 255.0

def tf_load_avif(path):
    img = tf.py_function(load_avif, [path], tf.float32)
    img.set_shape([None, None, 3])  # (H, W, 3)
    return img

# Create dataset
file_paths = tf.data.Dataset.list_files("dataset/*.avif")
dataset = file_paths.map(tf_load_avif, num_parallel_calls=tf.data.AUTOTUNE)
dataset = dataset.batch(32).prefetch(tf.data.AUTOTUNE)

for batch in dataset.take(1):
    print(batch.shape)  # (32, H, W, 3)

CPU Encoding (Built-In Fallback)

nvavif_py includes a built-in multi-threaded CPU encoder based on rav1e. It activates automatically when:

No compatible NVIDIA GPU is detected (device="auto")
You explicitly request it (device="cpu" / Device.CPU)
GPU encoding is forced but NVENC initialization fails

No additional packages are required. The CPU path is always available on all supported platforms.

GPU vs Built-In CPU Comparison

Feature	NVENC (GPU)	rav1e (Built-In CPU)
Encoder	NVIDIA NVENC hardware	rav1e software (Rust)
Speed	⚡ Milliseconds per image	~1–30 seconds per image
Hardware required	NVIDIA RTX 40xx/50xx	Any CPU
CPU load during encode	Near zero	100% across all cores
Compression efficiency	Good	Excellent (rav1e)
Best for	Throughput, real-time	Quality, portability, CI
Threading control	Up to 8 concurrent sessions	`preset` maps to rav1e speed

import nvavif_py
from nvavif_py import Device

# These are equivalent on a machine without NVENC:
nvavif_py.encode_file("photo.png", cq=20)                      # auto-detects, uses CPU
nvavif_py.encode_file("photo.png", cq=20, device=Device.CPU)   # explicit CPU

Use Cases

🧠 ML/AI Training Pipelines

Compress dataset images or model output visualizations on-the-fly using GPU resources that would otherwise sit idle during data preprocessing. Encode with GPU, decode with dav1d — both operations leave the GIL free for DataLoader parallelism.

Use AVIF datasets to reduce storage and disk I/O by 30–70% without sacrificing training speed. nvavif_py.decode_file() is optimized for DataLoader workflows — configure threads=1 per worker to avoid CPU contention, and enjoy faster-than-PNG decoding due to smaller file sizes. Perfect for ImageNet-scale datasets, satellite imagery, medical imaging, and generative AI training data.

Why AVIF for training?

Smaller datasets → Faster download, less cloud storage cost.
Faster disk I/O → Smaller files = less time reading from SSD/NVMe.
No quality loss → Visually lossless at CQ 12–18.
Decode speed → nvavif_py is 3–4× faster than Pillow, matching or exceeding PNG decode times.
CPU-only environments → The built-in rav1e encoder covers dataset preparation even without a GPU.

🗄️ Storage Optimization

Reduce cloud storage costs by converting image libraries from legacy formats to AVIF, achieving 30–70% storage savings.

🔥 Massive Image Processing

If you are converting millions of images for a web CDN, the GPU can process them in a fraction of the time required by a CPU cluster. With multi-threaded encoding, saturate all available NVENC sessions for maximum throughput.

🌐 Web Asset Pipelines

Integrate into your static site generator or CDN origin to serve AVIF images at a fraction of the size of JPEG/PNG, improving page load times and Core Web Vitals.

🖼️ Image Processing Services

Build high-throughput image conversion microservices that handle thousands of uploads per minute without saturating CPU resources.

📸 Photography Workflows

Batch-convert RAW/TIFF exports to AVIF for archival or web galleries, preserving quality with 10-bit depth and YUV444 chroma.

License

This project is licensed under the MIT License.

Under the hood, this library utilizes for hardware interaction ffmpeg-next (WTFPL), FFmpeg NVENC headers (LGPL/GPL), dav1d (BSD 2-Clause "Simplified"), and rav1e (BSD 2-Clause) to deliver a fully bundled processing pipeline.

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.github/workflows		.github/workflows
nvavif_py		nvavif_py
src		src
.gitignore		.gitignore
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE		LICENSE
README.md		README.md
build.rs		build.rs
pyproject.toml		pyproject.toml

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nvavif_py

Key Features

Why Hardware Encoding? Why Fast Decoding?

Decoding Performance

Hardware Requirements

Decoding Performance

Why CPU Decoding?

Decoder Architecture

Performance Comparison

Why It's Fast

Ideal for ML/AI Training Pipelines

Installation

Quick Start

Pillow Plugin (Zero-Config, works with CPU fallback too)

API Reference

is_supported() → bool

encode_file(input_source, ...) → bytes

Accepted Input Types

Parameters

Automatic Preprocessing

Quality Guidelines (cq)

Enumerations

ColorDepth

Chroma

ColorMatrix

NvencPreset

Device

DataType

decode_file(path, threads=0) → np.ndarray

Parameters

Returns

Threading Recommendations

Supported Formats

Usage Examples

Basic File Conversion

High-Quality with Alpha Transparency

10-bit HDR Encoding

YUV444 for Screenshots and Graphics

Color Matrix Selection

Auto-CQ: SSIM-Guided Quality

Device Selection

HDR Float32 Input (ACES Tone-Mapping)

PyTorch / CuPy GPU Tensor Input

EXIF Embedding

Pillow Plugin — Save Directly

From a NumPy Array

From In-Memory Bytes

Batch Conversion (Sequential)

Multi-Threaded Batch Conversion

Web Server Integration (FastAPI)

Decoding AVIF Files

Basic Decoding

Save to PNG

Batch Decoding (Sequential)

Batch Decoding (Parallel)

PyTorch DataLoader Integration

TensorFlow/Keras Data Pipeline

CPU Encoding (Built-In Fallback)

GPU vs Built-In CPU Comparison

Use Cases

🧠 ML/AI Training Pipelines

🗄️ Storage Optimization

🔥 Massive Image Processing

🌐 Web Asset Pipelines

🖼️ Image Processing Services

📸 Photography Workflows

License

About

Topics

Resources

License

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Stars

Watchers

Forks

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`is_supported() → bool`

`encode_file(input_source, ...) → bytes`

Quality Guidelines (`cq`)

`ColorDepth`

`Chroma`

`ColorMatrix`

`NvencPreset`

`Device`

`DataType`

`decode_file(path, threads=0) → np.ndarray`