Image generation has become a fascinating field in AI, offering tools to create astounding visuals with minimal effort. Flux AI image generation model, an open-source model developed by Black Forest Labs, has quickly gained attention for its ability to produce high-quality, creative visuals crafted for specific requirements. Powered by an impressive 12 billion parameters, Flux AI image generator competes and surpasses other leading image generation models like SD3 Ultra, Midjourney V6.0, and DALL-E 3 HD.
This article is designed for AI enthusiasts and beginners looking to simplify their image-generation process.

By the end, you’ll know how to quickly generate optimal images for various use cases-whether it’s a YouTube thumbnail or professional-grade visual content-without the trial-and-error hassle of finding the right parameters.
We will not only walk you through the process but also provide the exact code needed to produce realistic, eye-catching images efficiently.

1. About Flux Models
2. Key Components of Flux Pipeline
3. Inferencing with Flux-Dev AI Model
   1. UI Images
   2. Youtube Thumbnails
   3. Product Photography
   4. Movie Posters
   5. Human Face
   6. Fashion Designs
   7. Portraits
   8. Various Art Styles
   9. Unique Prompts
4. Flux Image Generation: A Closer Look at Different Use Cases
   1. UI Images: Fast, but not always Perfect
   2. Product Photography: The Importance of Detail
   3. Movie Posters: Adjusting Tone and Text
   4. Fashion Design: Real-Life and Animated
   5. Portraits: Balancing Detail and Realism
   6. Various Art Styles: Struggles with Accuracy
5. Flux Tools
   1. Benchmark Results and Comparison with other SOTA
6. Key Takeaways
7. Conclusion
8. References

About Flux AI Image Generation Models

Before getting familiar with Flux, its essential to understand the foundation upon which its built. Diffusion Models generate images by iteratively refining a noisy image, eventually producing a clean, high-quality result. This process of denoising enables diffusion models to create more coherent and realistic images as diffusion is a multiple step process unlike previous Generative models like GAN(Generative Adversarial Networks) or VAE(Variational Autoencoder) . Flux AI Image Generation model uses this approach with significant improvements by introducing the concepts like Flow Matching and Timestamp Sampling, providing a unique set of features that enhance both image quality and generation speed. Flux architecture has MMDiT like architecture at its core. 

Model Variants:

Flux 1.1 Pro Ultra: Flux1.1 Pro is designed for creating high-resolution images, making it ideal for tasks that require fine details and sharp visuals.

This version is optimized for scenarios where image clarity and precision are critical, such as advertisements, print media, and detailed concept art.

Flux .1 Pro: This  is also a flagship model offered by the Black Forest Labs. Both of the pro models are available for use through their APIs only and the weights are hosted at platforms like Replicate, Fal AI and Mystic AI.

Flux .1 Dev: This model is quite useful for people belonging to the research community or developer community as well as people in the design industry. Unlike the previous pro models, this one is open-sourced under a non-commercial license available at HuggingFace. 

Flux .1 Schnell: This variant is the fastest among all the other variants with great sample quality generation under 5 timestamps. Also open-sourced and available at HuggingFace under the Apache 2.0 License. Quite useful for those who want to perform Generative AI Experiments on their local machines.

In the above Image the term Cost refers to the computational cost as well as financial cost which one needs to pay in order to get the access of the model or in order to generate images as per their requirements.

Key Components of Flux Pipeline

The Flux Image Generation Pipeline consists of a chain of models which collaboratively generate an image based on the prompt provided by the user. 

Let us have a look at what are those models:

CLIP model: CLIP model is included in the Flux architecture in order to better understand the user prompt and increase the prompt adherence. By understanding both images and text in a shared space, CLIP helps Flux like diffusion models to generate images that are contextually aligned with the user input. It uses a ViT-large-patch14 architecture with 12 encoder layers, 12 attention heads, vocab size of 49408 and a hidden size of 768 dimensions. This text encoder can process a maximum sequence length of 77 tokens, beyond which the tokens are automatically truncated. This helps in powerful multimodal representation by encoding the text prompt as vector representations that captures the essence of the given prompt within the latent space.

T5 Encoder: A secondary T5-XXL encodes the prompt with 24 encoder and 24 decoder layers, each having 64 attention heads. The hidden size (d_model) is 4096, suited to handle complex language tasks with a vocabulary size of 32,128. This is particularly useful for processing longer and more intricate prompts, providing richer context for image generation.

FluxTransformer2DModel: This model processes the spatial relationships within images, ensuring that the generated output maintains a consistent, realistic layout. The main diffusion model is a Conditional Transformer (MMDiT) architecture to denoise the encoded image latents with 19 layers and 24 attentions per layer. The model processes 64 channels of input data with hidden dimension of 768 to reduce the dimensionality for downstream tasks. In Flux Schnell the guidance embed is set to False as it doesn’t need any sort of guidance scale to improve or diversify the generation quality.

VAE: Finally a VAE is used for reconstructing the compact latent representation output from the FluxTransformer2DModel to pixel space. This uses DownEncoderBlock2d for encoding and UpDecoderBlock2D with sample size of 1024×1024.

For those who want to get a more comprehensive understanding of concepts like what is diffusion or what is flow matching or even what is timeStamp sampling. Then, you should visit our previous blog on Stable Diffusion: Paper Explanation and Inference. It also talks about MMDiT Architecture(which is quite similar to Flux Architecture) thoroughly.

Click here to download the source code to this post

Before moving any further let us first have a look at the code snippet of Flux Image Generation Pipeline, in order to better understand all the things that we will be learning as we walk through this article:

from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype = torch.bfloat16)
pipe.to("cuda")

And the script given below tells you about the parameters that Flux model takes as an input like prompt, height and width for the generated image, guidance_scale etc. :

prompt = """ Generate an oil painting of a tranquil lakeside at sunset. 
The scene includes mountains in the background, reflections on the water, and a small wooden boat near the shore. 
Emphasize warm colors like orange, pink, and purple."""
image = pipe(
    prompt,
    height=1024,
    width=1024,
    guidance_scale=1.0,
    num_inference_steps=30,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("flux-dev-water_color_Painting.png")

Complete Inferencing code is available at our LearnOpenCV github repo. Do visit it and try experimenting with it on a cloud platform, in order to get accustomed with the parameters tweaking that we are about to go through. Enjoy Inferencing!

Inferencing with Flux.1-Dev AI Model 

Here’s the interesting part, as all are curious how the Flux AI Image Generator performs for various prompts and configuration settings.

As Flux.1-Dev is quite a big model, we will need at least 38GB vRAM. So the generated images shown are inference with an A6000 48GB vRAM GPU with full precision.

Guidance Scale(GS): Guidance scale means how much the model has to adhere to the user prompt which is also referred to as Prompt Adherence.  A higher Guidance Scale means the model will try to generate an image that follows the prompt more closely. While a lower Guidance Scale means the model will be more creative and artistic on its generations. It can also be thought of as Prompt Strength.

Number of Inference Steps(NIS): Typically Diffusion Modelgenerates images starting from a complete pure noise and what type of noise we want to start with is defined through another parameter called generator. Now, the model continues to denoise this noise image over and over again in single steps in the direction which adheres to the prompt given by the user, ultimately generating the desired target image. Inference Steps refers to those steps that the model takes to generate our desired result. The higher the value, the more inference steps are taken by the model to produce the image and thereby resulting in more time.

To understand the importance of GS and NIS better, let’s take a look at the generated image samples obtained under various guidance scales and inference steps. 

Prompt = “Generate an oil painting of a tranquil lakeside at sunset. The scene includes mountains in the background, reflections on the water, and a small wooden boat near the shore. Emphasize warm colors like orange, pink, and purple.”

For all the images in the grid below:

NIS = 30 and Resolution= (1024,1024)

Now, we can deduce few points from the above grid of images showing the different kinds of image generation produced by Flux.1-Dev Model with various Guidance Scale:

1. GS = 1.0 or GS = 1.5 is usually very poor and not be used if a good quality output is desired.
2. GS = 2.0 to GS = 3.0 are the best of all ( my personal favourite is the one with GS = 3.0) and reflects all the qualities of an oil painting. Now, it just depends on the user choice. 
3. GS = 3.5 to GS = 4.5 are too smooth and the colors give  a plastic like texture to the image which makes the images too synthetic and not realistic hand made painting. 

Let us have a look at another example with varying Guidance Scale. This time about a human subject:

Prompt = “Black-and-white street photography of an old woman sitting alone on a street bench, as people walking past her are blurred due to a slow shutter speed, emphasising her loneliness and isolation from society. Captured with a Nikon D850, a wide-angle lens, an aperture of f/4, and soft natural light, this candid moment of street life has been preserved.”

For all the images in the grid below:

NIS = 30(For all the below Images in the Grid) and Resolution = (1024,1024)

As we discussed earlier, we can see how the generation quality differs for different GS values.

We got an idea of how the GS and inference steps affect the overall image generation quality. Next we will focus on the practical use cases where Flux AI image generator model can help you.

Time to Flex with Flux,

We see that there is a quick surge in adapting image generation models into their existing workflow to increase productivity. If you could translate your thoughts into what you want into a meaningful prompt, Flux AI image generator can produce really amazing images for your specific use case.

UI Images

Won’t it be a great help to be able to generate a nice looking modern homepage for your product’s website within minutes with few lines of prompts only. As, it’s the time to flex with flux, let’s begin with generating UI images, one of the most exciting aspect that came out of all image generation models.

Some relevant prompts you can try :

Music UI Prompt- “Design a sleek and modern homepage UI for a music streaming app. Include: A top header with the app’s logo, a search bar, and icons for profile, settings, and notifications. A ‘Now Playing’ bar at the bottom with album art, song title, playback controls, and volume slider. Highlighted sections: ‘Recommended for You,’ ‘Top Charts,’ and ‘Recently Played,’ each in a scrollable horizontal carousel. Use vibrant colors, gradients, and high-quality album art for visual appeal, ensuring the design is responsive and user-friendly”
E-Commerce UI Prompt- “e-commerce website UI image.”
Food UI Prompt- “Imagine Food Delivery app, User Interface, Figma, Behance, HQ, 4k, Clean UI”
Map UI Prompt- “The design of the user interface of the mobile application tourist routes, a simple green and brown color palette with blue details”
Mental Health UI Prompt- “mobile mental health apps interface with minimalistic designs and dark golden color”

Youtube Thumbnails

Tired of searching the web and getting the most relevant stock images for your Youtube content? Now you can generate as many assets you want for the video without much effort.

Guidance Scale = 2.5, Number of Inference Steps = 30, Resolution = (1024, 1024)

Prompts: 

1. Tech Review Video: “Design a dynamic thumbnail for a tech review video featuring the latest smartphone. Include the phone prominently in the center with a glowing effect. Add bold text saying ‘MUST BUY?’ in a futuristic font. Use a vibrant blue and orange color scheme.”
2. Lifestyle Vlog: “Design a thumbnail for a travel vlog titled ‘Exploring Bali’s Hidden Gems.’ Include a serene beach with turquoise water, a traveler holding a map, and text saying ‘Paradise Found!’ in a bold, tropical-themed font.”
3. Movie Review: “Design a cinematic thumbnail for a movie review video of ‘Avatar: The Way of Water’ Feature a collage of characters with dramatic lighting, a glowing ‘Review’ stamp, and bold text saying ‘Epic or Meh?”
4. Food Recipe Video: “Design a mouthwatering thumbnail for a video titled ‘The Perfect Cheesecake Recipe.’ Include a close-up of a creamy cheesecake topped with strawberries. Add text saying ‘So Easy!’ in a fun, handwritten font.”
5. Tutorial Content: “Generate a clean and professional thumbnail for a tutorial on ‘How to Code in Python.’ Include a laptop with Python code on the screen, a glowing keyboard, and text reading ‘Python Made Easy!’ in white over a gradient blue background.”
6. Fitness Video: “Create a high-energy thumbnail for a fitness workout video. Include a muscular individual mid-exercise, bold text reading ’30-Day Transformation!’ and a background of a modern gym with a red and black theme.”
7. Science Video: “Design a captivating thumbnail for a science video titled ‘The Solar System Explained.’ Include a glowing sun in the center, orbiting planets, and bold text saying ‘Learn Space!’ in a futuristic white font over a dark blue starry background.”
8. Gaming Content: “Create a thumbnail for a gaming video showcasing an epic battle scene. Include a character from the game mid-action with glowing weapons. Use intense colors like red, yellow, and black, with bold text reading ‘ULTIMATE WIN!'”
9. Unboxing Video: “Create an exciting thumbnail for an unboxing video featuring a mystery box. Include a glowing box with sparkles coming out, text saying ‘What’s Inside?!’ and a surprised face in the corner.”

Product Photography

Product Photography is a important domain of e-commerce, advertising, and branding. It involves factors such as lighting, angles, and composition to highlight the product’s features effectively.

Image Generation Models like Flux AI image generator and its variants offer a nice alternative for traditional product photography techniques by using AI(particularly diffusion models) to create high-quality, realistic images digitally.

Prompts:-

Lotion and Soap Product Prompt- “Showcase natural skincare products against a soft, mint green background. A white ‘Salus’ hydrating hand wash bottle stands tall with a sleek, minimalist design, alongside two 60g Botanicals soaps-one in peach (Mandarin with Rosemary & Cream) and one in cream (Wild Mint & Myrtle)-displayed on a simple pink pedestal. A fresh grapefruit adds a pop of color, while eucalyptus sprigs frame the scene, highlighting the organic, botanical nature of the products. The natural lighting casts soft shadows, creating a clean and pure composition that emphasizes the simplicity and freshness of the skincare items.”

Azzaro Perfume Prompt- “In soft, atmospheric lighting with a focus on elegance. At the center of the scene a matte green perfume bottle, surrounded by swirling, delicate green smoke, gently wrapping around it, creating a mysterious, ethereal vibe. To the left, closer to the foreground the Azzaro logo is clearly visible on the bottle, catching subtle highlights. In the background a dark, gradient backdrop blending into deep shadows, emphasizing the glow of the smoke and the smooth texture of the Azzaro bottle.”

Fig 7: Flux AI Image Generation-Product Images 2

Movie Posters

Movie posters are a key part of film promotion, designed to grab attention. Traditional poster creation involves graphic designers, creative brainstorming, and multiple iterations, which can take time and resources.
Image generation models, like Flux AI image generator, simplifies the process of creating high-quality movie posters, as can be seen through the generated images below.

Prompt:- “A majestic lion stands on a rocky outcrop, gazing down at a curious cub, framed by a golden sunset. The warm orange and yellow hues create a striking silhouette, with soft clouds glowing in the background. The lion’s powerful frame contrasts with the cub innocence, symbolizing protection and wisdom. Long shadows and the fur catching the last rays of the sun add depth to the scene. The text The Lion King appears in bold, golden, glowing letters, seamlessly blending with the sunset, evoking a sense of timeless grandeur.”

Fig 8: Flux AI Image Generation-Movie Posters

Some more good samples: All below images in the Grid are Generated with the following setting- Guidance Scale = 3.0 and Inference Steps = 30 with Resolution = (1024,1024)

Human Face 

Human faces are often required in design projects, such as for profile images, marketing materials, or character creation in art. Traditionally, creating realistic faces involves photography, portrait drawing, or using stock images, all of which can be time-consuming and expensive.
Image generation models, like Flux, comes for our help here, just have a look at how beautiful images it generated:

Prompt:- “selfie webcam pic of an attractive woman smiling.Potato quality. Indoors, night, Low light, no natural light. Compressed. Low quality.”

The Image Generation with the Guidance Scale setting set to 2.0 generates quite good facial features for both NIS = 30 and NIS = 50. But as we tend to decrease the Guidance Scale our FLUX AI image generation model finds it difficult to mimic eye and teeth features properly. Finally, when we set the Guidance Scale to 1.0, the generated image turns out very poor, with a lot of graininess, as shown below. This issue is not affected by the number of Inference Steps. If you look closely at the image, you will notice that the eyes are misaligned, and there is an irregularity with the smile and cheeks

Below are some good examples of generating human close up objects with Flux AI image generation with Guidance scale set to 2.7 along with Number of Inference Steps to 50:

Fig 12: Flux AI Image Generation-Human Face Images

Fashion Design

The fashion design industry relies heavily on visual creativity, with designers regularly creating new collections. Traditionally, this requires a combination of sketches, prototypes, and photoshoots.
Flux, can significantly streamline this process of creating fashion designs by generating stunning and elegant fashion designs with very little prompts. This can be seen in the below examples:

Prompt(Real Life):-
1. “Elegant evening gown with intricate details, luxurious”
2. “Casual streetwear look with comfortable and cool vibe”

Prompt(Animated):- “Create an elegant evening gown inspired by celestial motifs, featuring shimmering metallic fabrics and star-shaped embroidery. The design should incorporate a modern silhouette with a flowing train and intricate beadwork. Complement the outfit with statement jewelry, like a diamond-encrusted choker, and silver stiletto heels. Present the gown on a runway model in a glamorous studio setting with soft spotlighting. Use a high-fashion illustration art style with bold lines, vibrant colors, and attention to texture to emphasize sophistication and creativity.”

Fig 14: Flux AI Image Generation-Fashion Design Images 2

Portraits

Prompt:- “A captivating black-and-white photograph of Albert Einstein in his study, deep in thought. His iconic wild hair is slightly tousled, and he is wearing his familiar tweed jacket. He sits at a cluttered desk filled with handwritten notes, open books, and scientific instruments. Sunlight streams through a nearby window, casting soft shadows across the room, giving the image a nostalgic, timeless quality. Einstein expressive face, with a hint of a thoughtful smile, reflects both his genius and curiosity. The photo perfectly captures the essence of a brilliant mind at work, surrounded by the tools of discovery.”

Fig 15: Flux AI Image Generation-Human Portraits 1

Fig 16: Flux AI Image Generation-Human Portraits 2

Various Art Styles

“Picture a Christmas scene made just for you-thanks to Flux AI image generator! Whether it is Santa flying across the sky, a cozy cabin covered in snow, or reindeer in a snowy field, or the decorated streets brightening the night sky with warm lights and many gift shops, Flux AI image generator can turn your idea into a unique image. Just describe what you want, adjust a few settings, and get ready to flex with Flux.”

Below are some really nice varied art style image generations through Flux AI image generator, along with the Guidance Scale (GS) and Inference Steps (NIS) setting mentioned side-by-side:

Fig 18: Flux AI Image Generation-Art Styles GS comparison

From these images it can be seen how Guidance Scale value influences the generation of images in varied art styles. With GS between 2.0 and 2.5 generations are nice with a realistic kind of look. But as we increase GS gradually the images become more smooth and less hand-drawn. The number of Inference steps did not have much impact on the quality of output but in some cases where the model is struggling with repetitive parts like fingers or with facial hairs, their NIS helps and it also makes sense to invest more time and resource consumption so that we can produce a better oriented and eye-pleasing image.

Prompts:-
pencil drawing:- “A pencil drawing of a chef chopping an onion on a cutting board”
pastel color:- “A pastel color drawing of a chef chopping an onion on a cutting board”
oil painting:- “An oil painting of a chef chopping an onion on a cutting board”
water color:- “A watercolor painting of a chef chopping an onion on a cutting board”
hyperrealistic:- “A hyperrealistic image of a chef chopping an onion on a cutting board”

Unique Prompts

Prompts:-
Family:- “family_2364.png”, “family_9273.tiff”
Car:- “car_2364.png”, “car_9273.tiff”
Hot_air_baloon:- “hot_air_baloon_2364.png”, “hot_air_baloon_9273.tiff”
Fisherman:- “fishing_man_9273.tiff”
Mount_fuji:- “mount_fuji_9273.tiff”
Superman:- “superman_9273.tiff”, “superman_2364.png”

The prompts written here generated the images shown in the above grid. These kinds of prompts are working quite well, as observed in the grid above, with various Flux models variants. A speculative reasoning for this could be contributed to the fact that the diffusion model might be overfitting on the training data or the model contains meta data files with similar kind of naming used in it. 

Flux AI Image Generation: A Closer Look at Different Use Cases

Flux AI image generator is a powerful tool for generating images across various domains, but its performance can vary depending on the specific task at hand. Below, we will see how Flux handles different scenarios, highlighting its strengths, limitations, and tips for optimizing results.

UI Images: Fast, But Not Always Perfect

Flux’s.1-Dev model excels at quickly generating beautiful UI images with just 20-30 inference steps. It produces designs efficiently, making it ideal for fast prototyping.

Tips for Better Results:

• Guidance Scale (GS): Setting the GS to 3.5 helps Flux understand longer prompts, leading to more detailed and accurate UI elements.

Some challenges:

• Text Clarity: Text in generated UI images can sometimes be unclear or look like random gibberish (e.g., “Dimetcrapy Iblel Eellop”).
• Alignment Issues: Some buttons and elements might appear misaligned, which can affect the overall polish.
• Higher Inference Steps Don’t Always Help: Increasing inference steps (e.g., 70) does not always fix these issues and cost us more generation time.

Product Photography: The Importance of Detail

For high-quality product photography, detailed prompts are key. Missing crucial details like product names or lighting conditions can result in blurry or unfinished images.

Using Inference Steps to Improve Quality:

• Increasing the number of inference steps helps refine lighting, shadows, and overall realistic image generation.

Challenges:

• Text on Products: Even with detailed prompts, text on products may appear gibbrish or unclear.
• Lower GS: These can lead to blurry images or poorly lit photos with inconsistent object body.

Movie Posters: Adjusting Tone and Text

Creating movie posters with Flux AI image generator requires careful tuning to get the right tone and text accuracy.

• Tone Changes: In some cases, like the “Lion King” example, the image tone changes diagonally, shifting from lighter to darker hues with increased contrast.

Different Settings for Different Results:

• With GS=2.0, NIS=30, the image will have a smoother tone but with fewer details. Text like “DISNEY” may appear distorted or replaced with random noise.
• Increasing Inference Steps to NIS=50 enhances the image with sharper contrast and more accurate text.

However, even with higher GS (e.g., 5.0), the model struggles with text generation, and randomness can occur.

Fashion Design: Real-Life and Animated

Flux AI image generator works well for generating both real-life and animated fashion designs.

Key Insights:

• GS=2.7: For real-life fashion designs, this setting helps produce high-quality results.
• Inference Steps (30-50): For real-life images, increasing inference steps does not seem to improve results much, indicating that lower steps are sufficient.

For animated or anime-style fashion designs, increasing inference steps significantly improves the quality, leading to more vibrant and detailed images.

Portraits: Balancing Detail and Realism

When generating portraits, Flux results can sometimes lean too heavily into unnecessary details or look overly “AI-like.”

• Effect of Inference Steps: As the number of inference steps increases, the image may become darker, with excessive details, like extra wrinkles or facial features that don’t match the prompt.
• Large GS Values: Even with a high GS of 10, the image often becomes overly detailed and less realistic, straying from a true black-and-white portrait.

Various Art Styles: Struggles with Accuracy

Flux AI image generator tries to mimic different art styles, but some common issues occur in generating images from art styles like pencil drawings, oil paintings, and hyperrealistic art.

Issues with Common Art Styles:

• Pencil Drawings: Flux AI image generator fails to capture the fine, light lines and texture of real pencil sketches, instead producing more of a digital drawing look.
• Pastel Colors: The soft, blended hues typical of pastel art are often replaced by harsh colors that do not blend smoothly.
• Oil Paintings: The rich, textured brushstrokes of oil paintings are missing, and the result appears flat and digital.
• Watercolor: The flowing, transparent colors of watercolor paintings are not captured. Although with GS = 2.0 or 2.7 with NIS = 30 you can generate the desired output as the model won’t overx saturate the whole thing.

Flux Tools

Recently,  the people of Black Forest Labs, announced the release of Flux.1 Tools which are a suite of models designed to add control to their base text-to-image model FLUX.1, enabling the modification and re-creation of real and generated images. Some Key Features of these models are:

1. Cutting-edge output quality.
2. Blends impressive prompt following with completing the structure of the source image.
3. Trained using guidance distillation.
4. Open weights to drive new scientific research, and empower artists to develop innovative workflows.
5. Generated outputs can be used for personal, scientific, and commercial purposes as described in the FLUX.1 [dev] Non-Commercial License.

The Tools:

FLUX.1 Fill: State-of-the-art inpainting and outpainting models, enabling editing and expansion of real and generated images given a text description and a binary mask.

FLUX.1 Depth: Models trained to enable structural guidance based on a depth map extracted from an input image and a text prompt.

FLUX.1 Canny: Models trained to enable structural guidance based on canny edges extracted from an input image and a text prompt.

FLUX.1 Redux: An adapter that allows mixing and recreating input images and text prompts.

Benchmark Results and Comparison with other SOTA

Key Takeaways

Flux AI image generator has proved its importance when it comes to generating images in varied fields of interests like youtube Thumbnails, UI images, Fashion Designs, Product Photography etc.

1. As you have seen the generated images, it is clear that providing the relevant Guidance Scale and Inference Steps to your use case becomes very important.
2. Flux is just an AI model afterall, so it is bound to make mistakes in some scenarios like producing the in-image text which is necessary in generating product images.
3. As, in our previous blog post we get a hands-on experience with Stable DIffusion Model, we are now at the stage of selecting our personal favourite out of the two Image Generation models: Flux and Stable Diffusion.In terms of the speed and quality of images generated, it has to be Flux. If text inside image is not your primary concern, I’ll recommend you guys to try out Flux by downloading the code available in this article and following the guidelines mentioned their.

Conclusion

Flux, with its many features, including different versions and powerful tools like CLIP and T5, gives users a lot of control and flexibility. Various Images shown in this article prove Flux’s capabilities in divergent scenarios like Product Photography, UI images as well as Youtube Thumbnails. Having so many practical use cases makes Flux a good choice for a lot of people ranging from AI enthusiasts who want to get hands-on experience with Image Generation models to the people who want to get an idea for their UI designs or Thumbnails.

References

• Black Forest Labs
• Official Github Repository
• Huge thanks to Gizem AkdaÄŸ for providing detailed and refined image generation prompts.
• I would like to recommend all the readers to checkout MayorkingAI’s twitter page where he has written some very artistic and innovative image generation prompts.
• UI images Prompt
• Scaling Diffusion
• Stable Diffusion: Paper Explanation and Inference