Flux.1 Schnell and Pro - New AI Image Model like Midjourney
TLDRIn this video, the presenter introduces Flux.1 Schnell and Pro, a new AI image model similar to Midjourney. Flux.1 Schnell is an open-source model with a 12 billion parameter capacity, designed for mid to high-level GPUs. It uses a rectified flow transformer to generate high-quality images from text. The model is available in three versions: open-source Schnell, non-commercial Flux Dev, and API-based Flux Pro. The video provides a detailed guide on installing and using the model locally and demonstrates its impressive image generation capabilities. Viewers can also access these models via API from various providers.
Takeaways
- 😀 Flux is a new AI image model that resembles Midjourney in its capabilities.
- 🔍 Flux is an open-source, 12 billion parameter model that can run on most mid to high-level GPUs.
- 📚 Flux utilizes rectified flow Transformer technology for high-quality image generation from text descriptions.
- 🎨 The model comes in three versions: Flux.1 Chanel, Flux.1 Dev, and Flux.1 Pro, each with different licensing and access methods.
- 🔑 Flux.1 Chanel is open-source under the Apache 2 license, suitable for local development and personal use.
- 🚫 Flux.1 Dev is a distilled model for non-commercial applications, available on Hugging Face, and requires contacting Black Forest Lab for commercial use.
- 💼 Flux.1 Pro offers state-of-the-art performance and is available only through an API from providers like Hugging Face and Fall.
- 💻 The video demonstrates how to install Flux.1 Chanel on a local system and generate an image using it.
- 🎉 M Compute is sponsoring the GPU used in the video, and a discount code is provided for renting GPUs.
- 🛠️ The video includes instructions for setting up a Python environment and installing necessary prerequisites for Flux.
- 🌐 Flux models are based on a hybrid architecture and have improved upon previous diffusion models with techniques like rotary positional embeddings and parallel attention layers.
Q & A
What is the new AI image model discussed in the video?
-The new AI image model discussed in the video is called Flux.1 Schnell, developed by Fall.
How does Flux.1 Schnell compare to Midjourney?
-Flux.1 Schnell is similar to Midjourney but has the advantage of being open-sourced and capable of both text-to-image and image-to-image generation.
What are the key features of the Flux.1 Schnell model?
-Flux.1 Schnell is a 12 billion parameter model that can run on mid to high-level GPUs, uses rectified flow Transformer for high-quality image generation from text descriptions, and comes in three flavors: Chanel, Flux Dev, and Flux Pro.
What are the differences between the three flavors of Flux.1 Schnell?
-Chanel is open-source with an Apache 2.0 license, Flux Dev is for non-commercial use and is similar in quality to Flux Pro but more efficient, and Flux Pro is only available via API for commercial use and offers state-of-the-art performance.
What are the hardware requirements for running Flux.1 Schnell locally?
-To run Flux.1 Schnell locally, a GPU with at least 80 GB of VRAM is recommended, as the model size is around 44.5 GB.
How can one install Flux.1 Schnell on a local system?
-To install Flux.1 Schnell locally, one needs to create a Python 3.10 environment, install prerequisites like torch and transformers, clone the repo from Black Forest Lab, install additional requirements from the repo, and run the Streamlit demo to launch the model in a browser.
Where can the weights for the Flux Dev model be found?
-The weights for the Flux Dev model are available on Hugging Face and can be tried out directly on Replicate and Fall.
What is the cost of using Flux Pro via API?
-Using Flux Pro via API costs around $0.05 per megapixel, allowing approximately 20 uses for $1.
What upcoming feature is mentioned for the Flux models?
-An upcoming feature for the Flux models is a text-to-video model, which will require a GPU with at least 80 GB of VRAM to run.
What technological advancements have been incorporated into Flux models to improve performance?
-Flux models incorporate hybrid architecture of multimodal and parallel diffusion Transformer blocks, flow matching for training, rotary positional embeddings, and parallel attention layers to enhance performance and hardware efficiency.
Outlines
🖥️ Introduction to Fall's New Open-Source Model
The video starts with a welcome message and introduces the new text-to-image and image-to-image model from Fall, called 'schel'. This model, reminiscent of Mid Journey, is open-sourced and boasts 12 billion parameters, making it compatible with mid to high-level GPUs. The narrator highlights its features, such as the rectified flow transformer, and demonstrates the high-quality images it can generate. The video will guide viewers through installing and using this model locally. Additionally, there's a shoutout to M Compute for sponsoring the video and offering a discount on GPU rentals.
🔍 Overview of Different Versions of the Model
The narrator explains the three versions of the model: Chanel (open-source with Apache 2 license), Flux Dev (non-commercial license), and Flux Pro (API-only, available from Fall and other providers). The video aims to demonstrate installing Chanel locally and generating images. The narrator sets up the environment using Python 3.10, installs necessary prerequisites like Torch and Transformers, and clones the Flux repository from Black Forest Lab. The installation process is detailed, with the model's download size being 44.5 GB, which requires a high-capacity GPU.
⚙️ Setting Up and Running the Model Locally
After setting up the environment and installing prerequisites, the narrator runs the model using Streamlit, demonstrating how to launch it in a browser. They provide a detailed overview of each model version, including their use cases and availability. The narrator attempts to run the model locally but faces limitations due to GPU memory constraints. They provide insights into the model's architecture, highlighting its state-of-the-art performance and efficiency improvements. The video concludes with a demonstration of the model's capabilities through API, showcasing its impressive image generation from text prompts.
🖼️ Demonstration of Image Generation with Prompts
The video continues with a demonstration of the model's image generation capabilities using various prompts. The narrator provides detailed descriptions of the generated images, praising the model's vividness, crispness, and ethereal quality. They emphasize the model's efficiency and cost-effectiveness, noting that it can generate high-quality images for a minimal cost. The narrator showcases another prompt, resulting in a hyper-realistic image, and reiterates the model's potential for local installation and use. The segment highlights the model's versatility and advanced features, encouraging viewers to explore its capabilities.
🎨 Conclusion and Call to Action
In the final part of the video, the narrator summarizes the capabilities of the Flux models and their impressive image generation quality. They emphasize the benefits of installing the model locally for unlimited use and encourage viewers to try it out via the provided links. The narrator ends with a call to action, asking viewers to subscribe to the channel and share the video if they find the content valuable. The video concludes with a note of gratitude for the viewers' support.
Mindmap
Keywords
💡Flux.1
💡Midjourney
💡Open-sourced
💡12 billion parameter model
💡Rectified flow Transformer
💡Flux Dev
💡Flux Pro
💡Apache 2 License
💡Hybrid architecture
💡Rotary positional embeddings
💡Parallel attention layers
Highlights
Introduction to Flux.1 Schnell and Pro as a new AI image model similar to MidJourney.
Flux.1 Schnell is open-sourced and can be run on mid to high-level GPUs.
The model uses a rectified flow transformer for generating high-quality images from text descriptions.
Three flavors of the model: Schnell (open-source with Apache 2.0 license), Flux Dev (non-commercial license), and Flux Pro (API access).
Flux Pro is available through API from providers like Replicate and Fall.
The video tutorial covers installing Schnell on a local system and generating an image.
Sponsored mention of M Compute for renting GPUs at good prices.
Detailed steps on setting up a conda environment and installing prerequisites like Torch and Transformers.
Cloning the Flux repository provided by Black Forest Lab for easy use.
Running the Streamlit demo to launch the model in the browser.
Overview of the three models: Flux Pro (state-of-the-art performance), Flux Dev (open-weight guidance distilled model), and Schnell (fastest model for local development).
Model sizes and hardware requirements: Pro model needs at least 80GB VRAM for optimal performance.
Explanation of the hybrid architecture of Flux models using multimodal and parallel diffusion transformer blocks.
Highlight of the model's ability to generate highly detailed and visually appealing images from text prompts.
Encouragement to try out the models via API if local installation is not feasible.
The cost of using the API for generating images, with examples of the output quality.
The potential of the models for various applications, including a forthcoming text-to-video model.
Demonstration of the model generating images based on detailed text prompts, showcasing its capabilities.
Closing remarks encouraging viewers to explore the models and subscribe to the channel for more content.