Summary of Reddit Comments on Flux

Flux Overview

• Flux is a new open-source image generator that is as good as Midjourney.
• It offers different versions like FLUX.1 [pro], FLUX.1 [dev], and FLUX.1 [schnell], with varying capabilities and restrictions.
• There are mentions of generating images exceeding expectations, being god-like tier, and having unmatched quality.
• Users appreciate the accuracy of details, like hands and anatomy, in the generated images.

Pros and Cons of Flux

Pros

• Pros:
  • Provides excellent sharpness and quality in image generation.
  • Accurate anatomy and details like hands are impressive.
  • Offers a good balance between accessibility and model capabilities.
  • Can generate believable and realistic images.

Cons

• Cons:
  • Some users experienced blurry outputs with Flux, especially when using specific models.
  • There are restrictions on certain models, limiting commercial use.
  • Training with Flux models might require significant VRAM, potentially limiting accessibility for users with older GPUs.

User Experiences with Flux

• Users praise the model for its creativity and consistency, enabling possibilities for various applications, including children's book illustration.
• Some users mention challenges with render times and erratic performance while generating images.
• There are requests for features like style references and consistent characters across multiple images.
• Concerns were raised regarding the need for significant VRAM requirements while training certain LoRAs.

Additional Comments on Flux

• There are discussions on incorporating NSFW content and experimenting with prompts to achieve desired outputs.
• Users share experiences with different models within Flux, like Dev, Schnell, and Pro.
• The dynamic nature of Flux prompts creativity and offers a new level of image generation capabilities.

Based on Reddit users' comments, Flux appears to be a powerful image generator with the potential for various creative applications, though users may encounter limitations based on hardware requirements and model restrictions.