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Inswapper_128.onnx Model: How to Download and Use

The inswapper_128 is a one-click face swapper and restoration tool. The inswapper_128.onnx has revolutionized the field of face swapping and restoration, offering unparalleled performance and ease of use. In this article, we will discuss how you can download and use inswapper_128.onnx model using different ways.

Firstly, let’s discuss some of inswapper_128.onnx model key features:

Key Features of inswapper_128.onnx Model

The inswapper_128.onnx is a powerful tool for face swapping and restoration. Here are some of its key features:

1. High-Resolution Processing

The model operates at a resolution of 128×128 pixels, which allows it to handle high-resolution images effectively. This results in a higher quality of face swapping, especially for high-resolution images.

2. Ease of Use

The model is easy to use, requiring minimal setup and configuration. Users can quickly swap faces in their images using the InsWapper tool.

3. Face Restoration

The model supports face restoration, which can enhance the quality of the swapped face. This feature can be particularly useful when swapping faces in low-quality images.

4. Compatibility

The model is compatible with various tools and platforms, including Midjourney and AUTOMATIC1111. This makes it versatile and adaptable to different use cases.

5. ONNX Format

The model is in the ONNX (Open Neural Network Exchange) format, which is a widely used format for AI models. This makes it compatible with various AI frameworks and platforms.

6. Large File Size

The model file size is relatively large, around 554 MB. This ensures that the model has the capacity to handle complex face-swapping tasks.

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| Related: Download inswapper_128.onnx file “inswapper_128.onnx” does not exist!

How to Download and Use inswapper_128.onnx Model

Method 1: The inswapper_128.onnx model download using Google Drive or Hugging Face

Here’s a step-by-step guide on how to use this model:

1. Download the ONNX model

Download the inswapper_128.onnx from the provided Google Drive link. The model is also available to download from Hugging Face.

2. Install the necessary libraries

You’ll need to install the ONNX Runtime library. You can install this library using pip:

pip install onnxruntime

3. Load the ONNX model

You can load the ONNX model using the onnxruntime library. Here’s an example:

import onnxruntime as ort

# Load the ONNX model

sess_options = ort.SessionOptions()

sess_options.intra_op_num_threads = 1

sess = ort.InferenceSession(‘inswapper_128.onnx’, sess_options)

4. Prepare the input data

The input data should be in the same format as the data the model was trained on. You’ll need to preprocess this data before feeding it into the model. The exact preprocessing steps will depend on the model and the data it was trained on.

5. Run the model

Once you have the input data, you can run the model and get the output. Here’s an example:

# Assume `input_data` is your preprocessed input data

input_name = sess.get_inputs()[0].name

result = sess.run(None, {input_name: input_data})

6. Postprocess the output

The model output will also need to be postprocessed to make it useful. Again, the exact postprocessing steps will depend on the model and the task it was trained for.

Method 2: Using Python

To use InsWapper 128, follow these steps:

1. Clone the Repository

Start by cloning the InsWapper repository from GitHub. You can do this by running the following command in your terminal:

git clone https://github.com/haofanwang/inswapper.git 

cd inswapper

2. Create a Python Virtual Environment

Next, create a Python virtual environment to isolate your project’s dependencies. You can do this by running the following commands:

python3 -m venv venv

source venv/bin/activate

3. Install Required Packages

Next, run pip install -r requirements.txt and install the required packages.

Install onnxruntime-gpu

To enable GPU inference, you need to install onnxruntime-gpu manually. If you want to use CPU-only inference, you can install onnxruntime by default.

4. Run the Quick Inference Script

Finally, you can run the quick inference script to swap faces. Here’s an example of how to do it:

from swapper import *

source_img = [Image.open(“./data/man1.jpeg”),Image.open(“./data/man2.jpeg”)]

target_img = Image.open(“./data/mans1.jpeg”)

model = “./checkpoints/inswapper_128.onnx”

result_image = process(source_img,target_img, model)

result_image.save(“result.png”)

5. Improve Face Quality with Face Restoration

To further improve the quality of the swapped face, you can perform face restoration. Here’s an example of how to do it:

python swapper.py \

–source_img=”./data/man1.jpeg;./data/man2.jpeg” \

–target_img “./data/mans1.jpeg” \

–face_restore \

–background_enhance \

–face_upsample \

–upscale=2 \

–codeformer_fidelity=0.5

Method 3: MidJourney (Discord)

To use the inswapper_128.onnx with Midjourney, you need to follow these steps:

1. Access the Insightful API

The inswapper_128.onnx is available via the Insightful API on Midjourney. You need to access this API to use the model.

2. Select the InsWapper App

Once the API is accessed, you can select the InsWapper app to swap faces in your Midjourney images.

3. Choose the Face to Swap

Now, choose the face you want to swap with the one in your Midjourney image. You can do this by selecting the inswapper_128.onnx and entering the name of the face you want to swap.

4. Edit and Test

After swapping the face, you can edit and test the result to ensure it meets your requirements. You can do this by tweaking the settings and testing the result until you are satisfied with it.

The use of InsWapper 128 with Midjourney offers several benefits. It allows you to easily swap faces in your Midjourney images, enhancing the realism and aesthetics of your images. Moreover, it will enable you to experiment with different faces and settings, giving you more control over your images.

| Also Read: DragGAN AI Photo Editing Tool: How To Install and Use

Known Issues and Limitations with inswapper_128.onnx

1. Safety Concerns

One of the potential issues with InsWapper 128 is safety concerns. Some users have raised concerns about the safety of the inswapper_128.onnx, suggesting that it may be unsafe. However, it’s important to note that these concerns are not widely shared, and the model is still widely used and trusted by many users.

2. Model Availability

Another issue is the availability of higher-resolution models like inswapper_256 and inswapper_512. While the inswapper_128 model is available, the higher-resolution models are not publicly accessible. This could limit the quality of the face-swapping results, especially for high-resolution images.

3. Model Performance

Additionally, there are concerns about the performance of the inswapper_128 model. Some users have reported that the 128 model does not perform as well as the Discord version of the model. Moreover, this could affect the quality of the face-swapping results.

4. Accessibility

Lastly, there are issues with accessing the model. Some users have reported difficulties in downloading the inswapper_128.onnx. This could make it difficult for some users to use InsWapper 128.

| Also Read: TheBloke/MythoMax-L2-13B-GPTQ: An Uncensored and NSFW Language Model

Is inswapper_128 an NSFW model?

The inswapper_128.onnx itself is not NSFW (Not Safe For Work). It is a machine-learning model designed for face-swapping and restoration tasks, and it does not contain any explicit or adult content.

However, it’s important to note that the use of this model can potentially lead to the creation of NSFW content if it’s used to swap faces in explicit or adult images. Also, this is a common concern with any AI model that can manipulate images, which users should be aware of when using such models.

| Also Read: Phind V7 Model: Outperforms GPT-4, Delivers Coding Excellence at GPT-3.5 Speed with 16k Context!

How is inswapper_128 different from Roop?

Compared to Roop, InsWapper 128 stands out with its superior performance and additional features. While both tools utilize machine learning models for face swapping, InsWapper 128 offers a more robust and accurate solution. It also provides additional options for face restoration, enhancing the overall quality of the swapped face.

| Also Read: Roop Unleashed Deepfake: New Updates, Features and Bug Fixes

Final Takeaway

The inswapper_128.onnx model is a powerful tool that revolutionizes the field of face swapping and restoration. With its superior performance, additional features, and user-friendly interface, it stands out as a leading solution in this domain. Whether you’re a professional photographer, a hobbyist, or a developer, Inswapper 128 is a must-have tool that can help you achieve stunning results in your projects. 

| Also Read: TheBloke MythoMix and MythoMax AI Models Series: Differences

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Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

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Cohere AI Drops Command A, The AI That’s Smarter, Faster and More Affordable

In this fast-moving world of AI today, a powerful AI system needs tons of expensive computer equipment to run properly. Companies have to spend a fortune on hardware just to keep these advanced AI systems running. So, they are always looking for technology that works great without breaking the bank. They need AI that can do impressive things with minimal computing needs. This balance is tricky to get right. But what if there is an AI model that is just as smart and fast but needs way less computing power? That’s exactly what Cohere AI has accomplished with its newest model, Command A.

Meet Command A by Cohere AI

Command A is the newest and most impressive AI model by Cohere AI. It is super smart, really fast, and more secure than earlier versions, like Command R and Command R+. What makes it special is that it works similar to or even better than famous AI models like GPT-4o and DeepSeek-V3 but doesn’t need nearly as much computing power. This gives businesses powerful AI without the huge electric bills and expensive computer equipment.

Key Features of Command A for Enterprises

This model is designed with businesses in mind. It has several features that make it perfect for companies:

1. Command A’s Chat Capabilities

Out of the box, Command A works as a conversational AI with interactive behavior. This setup is perfect for chatbots and other dialogue applications. The model takes text inputs and creates text outputs using an optimized architecture. It has two safety modes: contextual mode allows wider-ranging interactions while maintaining core protections, and strict mode avoids all sensitive topics.

2. 256k Context Window

Under the hood, it has some impressive specs. It has 111 billion parameters and can handle really long texts – up to 256,000 characters at once. Most competing AIs can only handle half that amount.

3. Advanced RAG Capabilities

Command A comes with “retrieval-augmented generation” (RAG). It can look up information and include references for its answers. People who tested found it better than GPT-4o at this task. Its answers were smoother, more accurate, and more useful.

4. Multilingual Excellence

Global companies need AI that works in many languages. Command A supports 23 languages spoken by most of the world’s population. It consistently answers in any of the 23 languages you ask for. In tests, people preferred it over DeepSeek-V3 across most languages for business tasks.

5. Enhanced Code Generation Capabilities

Command A is much better at coding tasks than previous models, outperforming similar-sized models on business-relevant tasks like SQL generation and code translation. Users can ask for code snippets, explanations, or rewrites and get better results by using certain settings for code-related requests.

6. Enterprise-Grade Security

Command A has strong security features to protect sensitive business information. It can also connect with other business tools and apps, making it a versatile addition to existing systems.

7. Agentic Tool Use

The real magic happens when Command A powers AI agents within a company. It works seamlessly with North, Cohere’s platform for secure AI agents. This lets businesses build custom AI helpers that can work inside their secure systems, connecting to customer databases, inventory systems, and search tools.

How Well Command A Performs

When tested side-by-side with the biggest names in AI, like GPT-4o and DeepSeek-V3, Command A holds its own and often comes out on top. It performed better on business tasks, science problems, and computer coding challenges. 

Cohere AI Drops Command A, The AI That’s Smarter, Faster and More Affordable

The model matches or beats the bigger and slower AI models while working much more efficiently.

  • Command A processes information up to 156 tokens per second – that’s 1.75 times faster than GPT-4o and 2.4 times faster than DeepSeek-V3.
  • It only needs two GPUs to run, while other AIs might need up to 32!

Moreover, this tool does great on standard tests for following instructions, working with other tools, and acting as a helpful assistant.

Cohere AI Drops Command A, The AI That’s Smarter, Faster and More Affordable

How to Get Started With Command A

Command A is available right now through several channels. You can try it chat in the Conhere AI’s playground here. You can also try it out through the Hugging Face Space demo here. Soon, it will be available through major cloud providers. Companies that want to install it on their own servers can contact Cohere’s sales team.

Command A Pricing Structure

Cohere AI has set competitive prices for using Command A:

  • Input tokens: $2.50 per million
  • Output tokens: $10.00 per million

This pricing lets businesses predict costs based on how much they’ll use the system, making budget planning easier.

The Command A Advantage

Cohere AI worked hard to make Command A super efficient. They wanted it to be powerful but not power-hungry. The result? An AI that gives answers much faster than its competitors. For businesses thinking about installing Command A on their own computers instead of using it through the internet, they can save up to 50% on costs compared to paying for each use. What does this mean in real life? Businesses using Command A can:

  • Get answers for customers more quickly
  • Spend less money on fancy computers
  • Grow their AI use without huge cost increases
  • Save money overall

Wrapping Up

As more businesses bring AI into their daily operations, tools like Command A will become more important. In a crowded AI market, its ability to deliver great results with minimal resources addresses one of the biggest challenges in business AI adoption.

By putting efficiency first without sacrificing performance, Cohere AI has created a solution that fits perfectly with what modern businesses actually need. For sure, this practical tool can help businesses stay competitive in our AI-powered world.

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Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

Google Launches Gemma 3, A Powerful Yet Lightweight Family of AI Models

Google has just launched the latest addition to the Gemma family of generative AI models, Gemma 3. It is a collection of lightweight, super-smart AI models based on Gemini 2.0. With a remarkable 100 million downloads within its first year and an impressive community that has crafted over 60,000 variants, Gemma has established itself as a cornerstone in the realm of AI development. Gemma 3 is specially designed to run directly on your devices, including phones, laptops, and desktop computers. This means you don’t need expensive cloud servers to use powerful AI models. 

Gemma 3 AI Models

These models comes in four sizes (1B, 4B, 12B, and 27B) and five precision levels, from full 32-bit down to 4-bit. Bigger models with higher precision generally work better but need more computing power and memory. Smaller models with lower precision use fewer resources but might not be quite as capable. You can pick the one that works best for your device and what you want to do.

The memory needed varies a lot depending on which model you choose. The smallest version (Gemma 3 1B in 4-bit precision) needs only about 861 MB of memory – less than a typical smartphone has! The largest version (Gemma 3 27B in full 32-bit precision) needs about 108 GB – that’s like needing a high-end server.

Key Features of Gemma 3

1. Run on a Single GPU

The Gemma models work better than much bigger models like Llama-405B, DeepSeek-V3, and o3-mini. This means these can run on just one GPU or TPU, making good AI cheaper and more accessible for everyone.

2. Multimodal Capabilities

The models (except the smallest 1B size) can understand both pictures and text. This lets apps do cool things like recognize objects in photos, read text from images, and answer questions about pictures.

3. Expanded Context Window

With a 128k-token context window, Gemma 3 can remember and understand lots of information at once. This is 16 times bigger than older Gemma models! You could feed it several multi-page articles, larger single documents, or hundreds of images in a single prompt.

4. Multilingual Support

The models can speak over 35 languages right out of the box and has been trained on more than 140 languages in total. This lets users build apps that can talk to users in their own language, which opens up their apps to many more people.

5. Function Calling Support

Gemma 3 supports “function calling,” which means it can trigger other programs to do things. This facilitates the automation of complex tasks, enhancing the overall functionality and utility of applications built with it.

6. Quantization Support

The models come in “quantized” versions that use less memory and computing power while still being accurate. These versions range from full 32-bit precision down to tiny 4-bit versions, so developers can choose what works best for their needs.

7. Easy Integration with Existing Tools

It plays nicely with lots of popular development tools like Hugging Face Transformers, Ollama, JAX, Keras, PyTorch, Google AI Edge, UnSloth, vLLM, and Gemma.cpp. 

8. Easy to Customize

It comes with recipes for fine-tuning and running it efficiently. Developers can train and adapt the model using platforms like Google Colab, Vertex AI, or even a gaming GPU. 

9. Works Great on NVIDIA GPUs

NVIDIA has specially optimized these models to work well on all their GPUs, from the small Jetson Nano to their newest Blackwell chips. 

How Gemma 3 Compares to Other AI Models

This family has scored impressively on AI benchmarks. The 27B version scored 1338 on the Chatbot Arena Elo leaderboard, putting it in the same league as much bigger models. What’s really amazing is that while some competing models need up to 32 huge NVIDIA H100 GPUs (which cost thousands of dollars each), the 27B variant needs just one GPU. That’s like getting sports car performance for the price of a compact car!

Real-World Uses for Gemma 3

1. Smart Apps on Your Phone

Gemma 3’s efficiency makes it perfect for creating smart apps that run directly on your phone. Developers can build AI assistants, language translators, content creators, and image analyzers that work quickly without needing to connect to the cloud all the time.

2. Edge Computing

For Internet of Things (IoT) devices and edge computing, it lets AI processing happen right where the data is collected. This reduces the need to send data back and forth, which saves bandwidth and keeps private data local.

3. AI for Small Businesses

Gemma 3 makes advanced AI available to organizations with limited resources. Small and medium businesses can now use sophisticated AI without spending a fortune on cloud computing. They can run its applications on the computers they already have.

4. Educational Tools

Schools and universities can use it to help students learn about AI. Students can experiment with cutting-edge AI on regular school computers, and researchers can innovate without needing super expensive systems.

Getting Started With Gemma 3

Developers can try them instantly in their web browser using Google AI Studio. No complicated setup needed! They can also get an API key from Google AI Studio to use it with Google’s GenAI SDK.

For those who want to adapt it to their specific needs, the models are available for download from Hugging Face, Ollama, or Kaggle. You can easily fine-tune and adapt the model using Hugging Face’s Transformers library or other tools you prefer.

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Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

Alibaba Introduces VACE, The Ultimate AI Model That Takes Video Editing to the Next Level

Alibaba is on fire when it comes to AI. The company keeps dropping one AI model after another, including image generators, video generators, chatbots, and much more. Now, they have introduced VACE, a super cool all-in-one AI model for creating and editing videos. Whether you want to generate new videos, edit existing ones, or manipulate specific parts of a clip, VACE has got you covered. Most AI video tools focus on just one or two tasks, maybe simple editing, image generation, basic animation, or color adjustments. But Alibaba’s VACE does it all in one place. 

Key Features of Alibaba VACE for Video Creation and Editing

VACE comes packed with amazing features that change how we make and edit videos. It handles tasks like reference-to-video generation (R2V), video-to-video editing (V2V), and masked video-to-video editing (MV2V). Moreover, it offers cool features like Move-Anything, Swap-Anything, Reference-Anything, Expand-Anything, and Animate-Anything.

1. Text-to-Video Generation (T2V)

VACE includes an amazing Text-to-Video Generation (T2V) feature, which is one of the most basic yet powerful video creation capabilities. You just provide a text prompt, and the video is generated accordingly.

2. Reference-to-Video Generation Feature

VACE’s Reference-to-Video (R2V) feature lets users generate new videos based on reference images. If you have a certain style or aesthetic in mind, VACE can analyze that and create videos that match it.

2. Video-to-Video Editing Feature

This feature lets users make changes to existing videos. It can help you apply a new visual style, change elements in a scene, or tweak colors. The best part? It does all of this while keeping edits smooth and natural, with no weird jumps or inconsistencies.

3. Masked Video-to-Video Editing Feature

This feature lets you edit specific parts of a video. You can define a specific area in the video and make changes to just that part, leaving the rest untouched. This makes it perfect for everything from fixing mistakes to adding new creative elements.

Alibaba Introduces VACE, The Ultimate AI Model That Takes Video Editing to the Next Level

4. Move-Anything Feature

This feature lets users grab objects in a video and move them around while keeping everything looking smooth and natural. Just select, move, and watch the AI do the heavy lifting. It even understands perspective and occlusions, so objects blend right into their new spots without looking out of place. 

5. Swap-Anything Feature

This feature swaps anything out of a video without it looking fake. Whether it’s changing a person’s outfit, replacing a background, or switching out objects, the AI ensures the new elements match the original’s motion, lighting, and surroundings. This is a game-changer, especially for virtual try-ons.

6. Reference-Anything Feature

This feature takes style transfer to the next level. Instead of just applying a filter, VACE lets users bring in colors, textures, and even composition elements from one video or image and apply them to another.

7. Expand-Anything Feature

This feature helps you adjust a video’s aspect ratio without awkward cropping or stretching. It extends the frame, generating new visuals that match the existing scene. Whether you’re repurposing a landscape video for a vertical format or adjusting a shot to fit different screens, this feature makes sure everything looks natural and cohesive. 

8. Animate-Anything Feature

This feature turns still images into moving visuals. With Animate-Anything, VACE analyzes a static image, figures out what could move naturally, and creates realistic motion sequences. You can add subtle movement or full-blown animations. This is perfect for breathing life into any photo.

Performance Evaluation of VACE

What makes VACE stand out? Most AI models focus on just one or two specific tasks. VACE is being built to unify multiple video-editing functions within a single framework. To test its performance, researchers developed the VACE-Benchmark, a framework designed to evaluate video generation quality across multiple factors. 

Compared to task-specific models like I2VGenXL, CogVideoX-I2V, ProPainter, and Control-A-Video, VACE has demonstrated competitive or even superior results in human and automated evaluations. The model showed impressive performance across aesthetic quality, background consistency, dynamic degree, imaging quality, motion smoothness, overall consistency, subject consistency, and temporal flickering, marking it as the best all-in-one tool.

Alibaba Introduces VACE, The Ultimate AI Model That Takes Video Editing to the Next Level

Potential Applications of VACE

VACE has the potential to shake up multiple creative fields. Here’s how it could be used:

1. Film and Video Production

It can help streamline post-production workflows by enabling seamless editing and video generation.

2. Advertising

The Alibaba VACE can create high-quality video ads with specific reference materials and controlled stylistic elements.

3. Gaming and Animation

It can generate animated sequences or game cinematics based on reference imagery or existing footage.

4. Social Media Content

This video model can help creators quickly produce and edit high-quality videos for various platforms.

5. Virtual Reality

It can expand the possibilities for creating immersive visual experiences.

By combining multiple video editing and generation tools into one model, VACE could become a go-to solution for industries that need speed, quality, and creative flexibility

Accessibility and Availability

While VACE has been introduced, it’s not publicly available yet. But, the model and code are expected to be released soon, along with support for ComfyUI workflow, VACE-Benchmark, Wan-VACE Model Inference, and LTX-VACE Model Inference. If the early tests are any indication, this could be one of the biggest leaps in AI-driven video editing yet. Stay tuned for updates!

For more technical details, you can check the model paper.

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Picture of Faizan Ali Naqvi
Faizan Ali Naqvi

Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!

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