A Professional's Review of FLUX: A Comprehensive Look
TLDRIn this review, professional photographer and AI enthusiast Andrea Baioni explores Flux, a new image generation model by Black Forest Labs. He discusses the model's capabilities, different versions, and integration into professional workflows. Despite Flux's impressive image quality and potential, Baioni points out the current limitations in documentation, control, and licensing, which make it challenging for professionals to adopt fully. He also provides a detailed guide on installing Flux and creating workflows with it, highlighting the model's strengths in scale, human anatomy, and typography.
Takeaways
- ๐ Flux is a new image generation model developed by Black Forest Labs, gaining popularity in the generative AI field.
- ๐ Andre Baglioni, a fashion photographer with experience in Generative AI, reviews Flux from a professional perspective.
- ๐ ๏ธ Flux comes in different versions: Schnell (fast), Dev (full model), and Pro (accessible via API), each serving different needs and hardware capabilities.
- ๐ค Flux may not yet be fully ready for professional use due to uncertainties in its development and lack of certain features like an IP adapter.
- ๐ Flux shows promise with great base models, but improvements in control mechanisms like control nets and LORos are needed for professional integration.
- ๐ Flux has specific requirements for image dimensions, which can be restrictive and may lead to unpredictable results if not adhered to.
- ๐ Documentation for Flux is lacking, causing frustration among users who are accustomed to more detailed guidance from previous models.
- ๐ The license for Flux's Schnell version is Apache 2.0, allowing for broad use, while the Dev version has a non-commercial license.
- ๐ป Installing Flux involves different processes depending on the model version, with quantized versions requiring less VRAM but sacrificing precision.
- ๐ ๏ธ For advanced users, Flux can be combined with other models and pipelines to create more complex workflows, despite its current limitations.
- ๐จ Flux delivers high-quality image results, with strengths in areas like scale, human anatomy, and typography, indicating its potential for future development.
Q & A
What is Flux and who developed it?
-Flux is a new image generation model developed by Black Forest Labs, which includes some of the engineers behind Stable Diffusion and Runway ML.
What are the different versions of Flux mentioned in the script?
-There are three different versions of Flux: Schnell (meaning fast), Dev (the full model), and Pro (only accessible through API).
Why does the speaker believe Flux is not quite ready for professional use yet?
-The speaker believes Flux is not ready for professional use due to the lack of control over the base model, limited availability of control nets and IP adapters, and the uncertainty created by different development paths for the various Flux versions.
What does the speaker prioritize when looking for a generative AI model for professional use?
-The speaker prioritizes having a great base model with control over its behavior, including control nets, LOROs, IP adapters, fine tunes, and custom nodes.
What are the issues with the Flux model's documentation according to the speaker?
-The speaker finds the Flux model's documentation lacking, as it does not provide necessary information about settings like the optimal Flux guidance value, leading to trial and error for users.
What is the significance of the license for the different versions of Flux?
-The Schnell version is released under an Apache 2.0 license, allowing free use, while the Dev version is released under a non-commercial license, which restricts commercial use of the model itself but not the generated images.
How does the speaker describe the process of installing Flux on ConfUI?
-The speaker outlines the process of downloading and placing the appropriate model files in specific directories within the ConfUI setup, depending on whether it's the original Black Forest Labs model or a quantized version.
What is the main difference between the non-quantized and quantized versions of Flux when running on ConfUI?
-The main difference is that the non-quantized versions require loading the VAE and clip models separately, while the quantized versions have an all-in-one checkpoint with the model, clip, and VAE embedded together.
How does the speaker suggest combining Flux with other models or pipelines despite compatibility issues?
-The speaker suggests creating more complex pipelines that can work independently of Flux, allowing users to combine the strengths of different models or pipelines to achieve desired results.
What are the speaker's first impressions of Flux in terms of image generation capabilities?
-The speaker is impressed with Flux's ability to create stunning images, noting its strengths in scale, human anatomy, and typography, despite some frustrations with its fussiness and limitations.
Outlines
๐ Introduction to Flux AI Model
Andre Baglioni, a fashion photographer and experienced user of Generative AI, introduces Flux, a new image generation model by Black Forest Labs. Flux is a base model that has generated significant interest in the AI field. The video will explore whether Flux lives up to the hype and its suitability for professional use. Black Forest Labs is known for its involvement in Stable Diffusion and Runway ML. Flux comes in different versions: Schnell (fast), dev (full), and pro (API access only), each with potential implications for professional workflows.
๐ค Flux's Professional Integration Challenges
Baglioni discusses the professional requirements for generative AI models, emphasizing the need for control over model behavior through features like control nets, LOROs, and IP adapters. While Flux's base models are promising, the current availability of control nets for Flux is limited and inconsistent, causing uncertainty for professionals who prefer reliable workflows. The video also touches on the different development paths for Flux versions and the challenges they present for professionals seeking a standardized model.
๐ Technical Aspects and Installation of Flux
The paragraph delves into the technical aspects of Flux, including the installation process for different versions on ConfUI. It explains the differences between the Schnell and dev versions and how to obtain and install them, including the necessary VAE and CLIP models. The summary also covers the trade-offs between using quantized versions for lower VRAM requirements versus full precision models and the implications for hardware configurations and workflow complexity.
๐ ๏ธ Workflow Creation with Flux on ConfUI
This section provides a detailed explanation of how to set up and run a basic Flux workflow on ConfUI, including the loading of models, text prompts, and the use of flux guidance nodes. It highlights the importance of certain settings like CFG and the challenges of working with Flux's specific image dimensions. The summary also mentions the use of different samplers and the necessity of using the correct model versions for certain features like Loras.
๐จ Combining Flux with Other Models for Enhanced Workflows
Baglioni explores the potential for combining Flux with other models and workflows to overcome current limitations. He demonstrates how Flux can be integrated with existing pipelines for further refinement of generated images. The summary emphasizes the flexibility of Flux when combined with other tools and the creative possibilities it offers, despite the need for more advanced control features in the future.
๐ Flux's Potential and Future Outlook
In the concluding section, Baglioni reflects on his initial impressions of Flux, praising its ability to generate high-quality images with excellent scale and human anatomy. He acknowledges the frustrations and limitations of the current Flux model but expresses optimism about its potential as a foundation for future workflows. The summary also mentions the importance of community development and the anticipation of additional tools and features to enhance Flux's capabilities.
Mindmap
Keywords
๐กFlux
๐กBlack Forest Labs
๐กGenerative AI
๐กControl Nets
๐กLoRAs (Low-Rank Adaptations)
๐กIP Adapter
๐กSchnell
๐กDev Version
๐กPro Version
๐กWorkflow
๐กConfuY
๐กFine-Tuning
Highlights
Flux is a new image generation model developed by Black Forest Labs, gaining popularity in the generative AI field.
Andre Baglioni, a fashion photographer, evaluates Flux from a professional perspective, focusing on its integration into professional settings.
Flux has multiple versions, including Schnell (fast), Dev (full model), and Pro (accessible via API), catering to different hardware needs and professional interests.
Black Forest Labs is known for developing Stable Diffusion and Runway ML, giving Flux a strong foundation.
Flux's different versions have varying development paths, causing uncertainty for professionals seeking consistent results.
Professionals require control over generative AI models, such as control nets and IP adapters, which Flux currently lacks in some versions.
Flux's documentation is incomplete, leading to trial and error for users trying to understand its parameters and capabilities.
Flux has shown impressive results in image generation, despite its current limitations.
The model's training has led to a narrow acceptance of input parameters, affecting the flexibility in image dimensions and settings.
Flux's licensing differs between versions, with the Schnell version being more freely usable commercially.
The installation process for Flux varies depending on the version, with quantized versions requiring less VRAM but sacrificing precision.
Flux's workflow in ConfUI involves loading different models and settings, with the potential for complex image generation.
Flux's performance is superior in terms of scale and human anatomy compared to previous models like 1.5 or SDXL.
Despite the current limitations, Flux shows great potential for future workflows once it includes control net adapt and IP adapter support.
Flux's compatibility with existing pipelines allows for the combination of its strengths with other models for enhanced results.
Andre Baglioni expresses excitement for Flux's future and plans to incorporate it into his professional workflows once it matures.