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Releases: roboflow/inference

v0.34.0

17 Jan 17:19
@grzegorz-roboflow grzegorz-roboflow
v0.34.0
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🚀 Added

Introducing Stability AI Image Generation Block v1 🖌️✨

Your gateway to limitless creativity with Stability AI! Block leverages Stability AI’s robust API with an easy-to-use interface. Just plug in your Stability AI API key, and you’re ready to go!

Main features:

  • Text-to-Image Magic - Generate entirely new images from text prompts in seconds.
  • Image Variations Made Easy - Start with an image and let the block transform it into captivating variations. Adjust the influence of your input image with precise control (strength parameter).
  • Positive Prompts: Describe what you want to see.
  • Negative Prompts: Specify what you don’t want to include.
  • Model Selection: Choose from cutting-edge models (core, ultra, sd3) to best suit your creative needs.

💪 Improved

@hansent contributions enhancing Inference documentation!

⚡ Support for Roboflow Instant Models

Roboflow Instant Models are now supported in Inference! While this feature is part of the broader Roboflow Instant Models initiative, Inference now includes the ability to load these models seamlessly.
Roboflow Instant Models leverage the power of box prompting, utilizing your entire dataset as prompts during inference for enhanced performance and smarter predictions.

Other changes

New Contributors

Full Changelog: v0.33.0...v0.34.0

Contributors

shantanubala, hansent, and 4 other contributors
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v0.33.0

10 Jan 22:35
@grzegorz-roboflow grzegorz-roboflow
v0.33.0
46621f8
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v0.33.0

🚀 Added

Llama Vision 3.2 🤝 other VLMs supported in Workflows

We welcome new block bringing Llama Vision 3.2 to workflows ecosystem!

Llama 3.2 is a new generation of vision and lightweight models that fit on edge devices, tailored for use cases that require more private and personalized AI experiences.

Brought by @AHB102 in #866

Related changes:

MQTT Writer Enterprise Workflow Block (added in #930)

This block enables our enterprise users to publish messages to an MQTT broker through Workflows.

MQTT (Message Queuing Telemetry Transport) is a lightweight messaging protocol designed for low-bandwidth, high-latency, or unreliable networks. It's widely used in applications where devices need to communicate with minimal overhead, such as the Internet of Things (IoT).

With this change workflows can communicate with the world through MQTT!

Change introduced by @chandlersupple

Plc EthernetIP Enterprise Workflow Block (added in #905)

This block enables our enterprise users to interface with PLC.
A Programmable Logic Controller (PLC) is an industrial computer specifically designed to automate machinery and processes in manufacturing and other industries. It monitors inputs (e.g., sensors), processes data based on a programmed logic, and controls outputs (e.g., actuators) to perform tasks.
This block is utilizing pylogix library over Ethernet/IP. Block supports three modes of operation:

  • read: Reads specified tags from the PLC.
  • write: Writes specified values to the PLC tags.
  • read_and_write: Performs both read and write operations in a single execution.

This change brings vision capabilities into real-world industrial plants!

Change introduced by @reedajohns

💪 Improved

Documentation improvements

@yeldarby transforms Inference docs with streamlined navigation, styling, and instant rendering!

More contributions enhancing Inference documentation:

Improvements to CI by @alexnorell

Other changes

New Contributors

Full Changelog: v0.32.0...v0.33.0

Contributors

hansent, yeldarby, and 7 other contributors
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v0.32.0

20 Dec 15:49
@PawelPeczek-Roboflow PawelPeczek-Roboflow
v0.32.0
03494f6
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v0.32.0

🚀 Added

👀 Gaze detection in Workflows

@joaomarcoscrs (as part of hist first contribution 🏅) introduced Gaze detection model into Workflows.

Don't know what Gaze Detection is?

Gaze detection is a method to determine where a person is looking by analyzing their eye movements and gaze direction. It typically uses cameras or sensors to track eye position and orientation, identifying the point of focus in real time.

It is commonly used in areas like:

  • Human-Computer Interaction: Controlling devices with eye movements.
  • Behavioral Analysis: Understanding attention and interest.
  • Marketing Research: Measuring what catches a person's attention.

Now - you can apply Workflows in such use-cases. Check out Gaze Detection block 📖 documentation to find more information.

Note

The block is currently not supported on Roboflow Hosted Platform. Check out how to send requests to localhost inference server

🏋️‍♂️ New experimental Workflows blocks enabling new capabilities

@yeldarby prepared whole series of blocks to open-up new capabilities for Workflows, including:

💪 Improved

Florence 2 runs now up to 3x faster

🧙‍♂️ @isaacrob-roboflow did some magic 🪄 and now, all of the sudden Florence2 models deployed in inference could run up to 3x faster 🤯 ❗
See details in #885

🔧 Fixed

Security vulnerability in landing page

We've fixed security issue in inference server landing page: #890

Issue description

If a Next.js application is performing authorization in middleware based on pathname, it was possible for this authorization to be bypassed.
This issue was patched in Next.js 14.2.15 and later.

Caution

We advise all users of older versions of inference server to migrate to version 0.32.0

Other fixes

🚧 What's Changed

🏅 New Contributors

Full Changelog: v0.31.1...v0.32.0

Contributors

yeldarby, probicheaux, and 6 other contributors
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v0.31.1

13 Dec 18:52
@PawelPeczek-Roboflow PawelPeczek-Roboflow
v0.31.1
be906b2
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v0.31.1

🔧 Fixed

Full Changelog: v0.31.0...v0.31.1

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PawelPeczek-Roboflow
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v0.31.0

13 Dec 18:27
@PawelPeczek-Roboflow PawelPeczek-Roboflow
v0.31.0
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v0.31.0

🚀 Added

📏 Easily create embeddings and compare them in Workflows

Thanks to @yeldarby, we have Clip Embedding and Cosine Similarity Workflows blocks. Just take a look what would now be possible.

💡 Application ideas

  • Visual Search: Match text queries (e.g., "red shoes") to the most relevant images without training a custom model.
  • Image Deduplication: Identify similar or duplicate images by calculating embeddings and measuring cosine similarity.
  • Zero-Shot Classification: Classify images into categories by comparing their embeddings to pre-defined text labels (e.g., "cat," "dog").
  • Outliers detection: Check which images do not match to general trend

✨ gemini-2.0-flash 🤝 Workflows

Check out model card and start using new model, simply pointing new model type in Google Gemini Workflow block 😄 All thanks to @EmilyGavrilenko

🔥 Recent supervision versions are now supported

For a long time we had issue with not supporting up-to-date supervision releases. This is no longer the case thanks to @LinasKo and his contribution #881 🙏

🐕‍🦺 React on changes in Workflows

We have new Delta Filter block that optimizes workflows by triggering downstream steps only when input values change, reducing redundant processing.

📊 Key Features:

  • Value Changes Detection: Triggers actions only on value changes.
  • Flexibility: Hooks up to changes in numbers, strings, and more.
  • Per-Video Caching: Tracks changes using - changes for each video stream or batch element would be traced separately

💡 Use Case:

  • Detect changes (e.g., people count) in video analysis and trigger downstream actions efficiently.

🔧 Fixed

  • confidence threshold was not applied for multi-label classification models. @grzegorz-roboflow fixed the problem in #873
  • Active Learning Data collection finally works for multi-label classification models - see @grzegorz-roboflow work in #874
  • Fixed model_id bug with InferenceAggregator block by @robiscoding in #876
  • Security issue: nanoid from 3.3.7 to 3.3.8 - see #878
  • Fix measurement logic for segmentations in measurement block by @NickHerrig in #872

🚧 Changed

New Contributors

Full Changelog: v0.30.0...v0.31.0

Contributors

yeldarby, LinasKo, and 6 other contributors
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v0.30.0

11 Dec 13:06
@grzegorz-roboflow grzegorz-roboflow
v0.30.0
25aa233
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v0.30.0

🚀 Added

✨ Paligemma2 support!

Enhanced model support: We’re excited to introduce Paligemma2 integration, a next-generation model designed for more flexible and efficient inference. This upgrade facilitates smoother handling of multi-modal inputs like images and captions, offering better versatility in machine learning applications. Check out the implementation details and examples in this script to see how to get started.

Change added by @probicheaux in #864

Remaining changes

Full Changelog: v0.29.2...v0.30.0

Contributors

LinasKo, probicheaux, and 3 other contributors
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v0.29.2

05 Dec 18:00
@grzegorz-roboflow grzegorz-roboflow
v0.29.2
6abcb3c
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v0.29.2

ultralytics security issue fixed

Caution

Ultralytics maintainers notified the community, that code in the ultralytics wheel 8.3.41 is not what's in GitHub and appears to invoke mining. Users of ultralytics who install 8.3.41 will unknowingly execute an xmrig miner.
Please see this issue for more details

Remaining fixes

Full Changelog: v0.29.1...v0.29.2

Contributors

hansent and bigbitbus
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v0.29.1

03 Dec 14:25
@PawelPeczek-Roboflow PawelPeczek-Roboflow
v0.29.1
53f84a1
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v0.29.1

🛠️ Fixed

python-multipart security issue fixed

Caution

We are removing the following vulnerability detected recently in python-multipart library.

Issue summary
When parsing form data, python-multipart skips line breaks (CR \r or LF \n) in front of the first boundary and any tailing bytes after the last boundary. This happens one byte at a time and emits a log event each time, which may cause excessive logging for certain inputs.

An attacker could abuse this by sending a malicious request with lots of data before the first or after the last boundary, causing high CPU load and stalling the processing thread for a significant amount of time. In case of ASGI application, this could stall the event loop and prevent other requests from being processed, resulting in a denial of service (DoS).

Impact
Applications that use python-multipart to parse form data (or use frameworks that do so) are affected.

Next steps
We advise all inference clients to migrate to version 0.29.1, especially when inference docker image is in use. Clients using
older versions of Python package may also upgrade the vulnerable dependency in their environment:

pip install  "python-multipart==0.0.19"

Details of the change: #855

Remaining fixes

Full Changelog: v0.29.0...v0.29.1

Contributors

iurisilvio and PawelPeczek-Roboflow
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v0.29.0

29 Nov 17:15
@PawelPeczek-Roboflow PawelPeczek-Roboflow
v0.29.0
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v0.29.0

🚀 Added

📧 Slack and Twilio notifications in Workflows

We've just added two notification blocks to Worfklows ecosystem - Slack and Twilio. Now, there is nothing that can stop you from sending notifications from your Workflows!

slack_notification.mp4

inference-cli 🤝 Workflows

We are happy to share that inference-cli has now a new command - inference workflows that make it possible to process data with Workflows without any additional Python scripts needed 😄

🎥 Video files processing

  • Input a video path, specify an output directory, and run any workflow.
  • Frame-by-frame results saved as CSV or JSONL.
  • Your Workflow outputs images? Get an output video out from them if you wanted

🖼️ Process images and directories of images 📂

  • Outputs stored in subdirectories with JSONL/CSV aggregation available.
  • Fault-tollerant processing:
    • ✅ Resume after failure (tracked in logs).
    • 🔄 Option to force re-processing.

Review our 📖 docs to discover all options!

👉 Try the command

To try the command, simply run:

pip install inference

inference workflows process-images-directory \
    -i {your_input_directory} \
    -o {your_output_directory} \
    --workspace_name {your-roboflow-workspace-url} \
    --workflow_id {your-workflow-id} \
    --api-key {your_roboflow_api_key}
Screen.Recording.2024-11-26.at.18.19.23.mov

🔑 Secrets provider block in Workflows

Many Workflows blocks require credential to work correctly, but so far, the ecosystem only provided one secure option for passing those credentials - using workflow parameters, forcing client applications to manipulate secret values.

Since this is not handy solution, we decided to create Environment Secrets Store block which is capable of fetching credentials from environmental variables of inference server. Thanks to that, admins can now set up the server and client's code do not need to handle secrets ✨

⚠️ Security Notice:

For enhanced security, always use secret providers or Workflow parameters to handle credentials. Hardcoding secrets into your Workflows is strongly discouraged.

🔒 Limitations:

This block is designed for self-hosted inference servers only. Due to security concerns, exporting environment variables is not supported on the hosted Roboflow Platform.

🌐 OPC Workflow block 📡

The OPC Writer block provides a versatile set of integration options that enable enterprises to seamlessly connect with OPC-compliant systems and incorporate real-time data transfer into their workflows. Here’s how you can leverage the block’s flexibility for various integration scenarios that industry-class solutions require.

✨ Key features

  • Seamless OPC Integration: Easily send data to OPC servers, whether on local networks or cloud environments, ensuring your workflows can interface with industrial control systems, IoT devices, and SCADA systems.
  • Cross-Platform Connectivity: Built with asyncua, the block enables smooth communication across multiple platforms, enabling integration with existing infrastructure and ensuring compatibility with a wide range of OPC standards.

Important

This Workflow block is released under Roboflow Enterprise License and is not available by default on Roboflow Hosted Platform.
Anyone interested in integrating Workflows with industry systems through OPC - please contact Roboflow Sales

See @grzegorz-roboflow's change in #842

🛠️ Fixed

Workflows Execution Engine v1.4.0

  • New Kind: A secret kind for credentials is now available. No action needed for existing blocks, but future blocks should use it for secret parameters.

  • Serialization Fix: Fixed a bug where non-batch outputs weren't being serialized in v1.3.0.

  • Execution Engine Fix: Resolved an issue with empty inputs being passed to downstream blocks. This update ensures smoother workflow execution and may fix previous issues without any changes needed.

See full changelog for more details.

🚧 Changed

Open Workflows on Roboflow Platform

We are moving towards shareable Workflow Definitions on Roboflow Platform - to reflect that @yeldarby made the api_key optional in Workflows Run requests in #843

⛑️ Maintenance

Full Changelog: v0.28.2...v0.29.0

Contributors

yeldarby, alexnorell, and grzegorz-roboflow
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v0.28.2

27 Nov 14:02
@PawelPeczek-Roboflow PawelPeczek-Roboflow
v0.28.2
3d15bcb
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v0.28.2

🔧 Fixed issue with inference package installation

26.11.2024 there was a release 0.20.4 of tokenizers library which is dependency of inference dependencies introducing breaking change for those inference clients who use Python 3.8 - causing the following errors while installation of recent (and older) versions of inference:

👉 MacOS 
Downloading tokenizers-0.20.4.tar.gz (343 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... error
error: subprocess-exited-with-error

× Preparing metadata (pyproject.toml) did not run successfully.
│ exit code: 1
╰─> [6 lines of output]

    Cargo, the Rust package manager, is not installed or is not on PATH.
    This package requires Rust and Cargo to compile extensions. Install it through
    the system's package manager or via https://rustup.rs/

    Checking for Rust toolchain....
    [end of output]

note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details
👉 Linux

After installation, the following error was presented

/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/transformers/utils/import_utils.py:1778: in _get_module
  return importlib.import_module("." + module_name, self.__name__)
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/importlib/__init__.py:127: in import_module
  return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:[101](https://github.com/roboflow/inference/actions/runs/12049175470/job/33595408508#step:7:102)4: in _gcd_import
  ???
<frozen importlib._bootstrap>:991: in _find_and_load
  ???
<frozen importlib._bootstrap>:961: in _find_and_load_unlocked
  ???
<frozen importlib._bootstrap>:219: in _call_with_frames_removed
  ???
<frozen importlib._bootstrap>:1014: in _gcd_import
  ???
<frozen importlib._bootstrap>:991: in _find_and_load
  ???
<frozen importlib._bootstrap>:975: in _find_and_load_unlocked
  ???
<frozen importlib._bootstrap>:671: in _load_unlocked
  ???
<frozen importlib._bootstrap_external>:843: in exec_module
  ???
<frozen importlib._bootstrap>:219: in _call_with_frames_removed
  ???
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/transformers/models/__init__.py:15: in <module>
  from . import (
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/transformers/models/mt5/__init__.py:36: in <module>
  from ..t5.tokenization_t5_fast import T5TokenizerFast
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/transformers/models/t5/tokenization_t5_fast.py:23: in <module>
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/transformers/tokenization_utils_fast.py:26: in <module>
  import tokenizers.pre_tokenizers as pre_tokenizers_fast
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tokenizers/__init__.py:78: in <module>
  from .tokenizers import (
E   ImportError: /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tokenizers/tokenizers.abi3.so: undefined symbol: PyInterpreterState_Get

The above exception was the direct cause of the following exception:
tests/inference/models_predictions_tests/test_owlv2.py:4: in <module>
  from inference.models.owlv2.owlv2 import OwlV2
inference/models/owlv2/owlv2.py:11: in <module>
  from transformers import Owlv2ForObjectDetection, Owlv2Processor
<frozen importlib._bootstrap>:[103](https://github.com/roboflow/inference/actions/runs/12049175470/job/33595408508#step:7:104)9: in _handle_fromlist
  ???
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/transformers/utils/import_utils.py:1766: in __getattr__
  module = self._get_module(self._class_to_module[name])
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/transformers/utils/import_utils.py:1780: in _get_module
  raise RuntimeError(
E   RuntimeError: Failed to import transformers.models.owlv2 because of the following error (look up to see its traceback):
E   /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tokenizers/tokenizers.abi3.so: undefined symbol: PyInterpreterState_Get

Caution

We are fixing the problem in inference 0.28.2, but it is not possible to be fixed older releases - for those who need to fix that
in their environments, please modify the build such that installing inference you also install tokenizers<=0.20.3.

pip install inference "tokenizers<=0.20.3"

🔧 Fixed issue with CUDA and stream management API

While running inference server and using stream management API to run Workflows against video inside docker container, it was not possible to use CUDA due to bug present from the very start of the feature. We are fixing it now.

Full Changelog: v0.28.1...v0.28.2

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