FLUX 2 GGUF For LOW VRAM! | Workflow Tutorial

REBEL AI

25 Nov 202510:33

TLDRIn this video, Rebel walks viewers through a workflow tutorial for the Flux 2 GGUF models, tailored for low VRAM users. The tutorial highlights the setup process, including selecting text encoders and using reference images. Rebel explains how to adjust the Flux guidance scale for optimal text generation and shares tips for avoiding artifacts. He compares results at different guidance scales (4, 8, 11) and provides examples of successful text incorporation into images. The video concludes with encouragement to download the workflow and start generating imagesFlux 2 GGUF workflow immediately.

Takeaways

🧠This video demonstrates a streamlined GGUF workflow for Flux 2 AI that's optimized for systems with limited VRAM.
📸 The workflow supports up to eight reference images, though each input can be bypassed if not needed.
📝 Users can choose between FP8, FP16, or GGUF text encoders depending on VRAM limitations.
🔧 The new Flux 2 workflow uses a single clip file instead of a dual clip loader from Flux 1.
🪄 The Flux 2 VAE is a new required file, replacing the older AE.safetensor.
🎚️ The Flux Guidance Scale significantly affects text accuracy—4 is weak, 8 is balanced, and 11 offers the best text clarity but may add artifacts.
🎲 A seed generator is included; the node labeled 'random noise' is actually the seed.
🖼️ 50 sampling steps provided the best quality in testing, with minimal noise and no bloating.
📉 The workflow is VRAM-intensive, with Q2 quantization using around 12GB VRAM, though it surprisingly runs well on systems with 8GB VRAM + 16GB RAM.
⚠️ The DIP node was tested but produced noisy and bloated results, making it unsuitable for this workflow.
🆚 Comparisons show that higher guidance scales (especially 11) produce significantly better text rendering in images.
🎨Flux 2 GGUF workflow Example outputs demonstrate improved text generation and overall image quality compared to Flux 1.

Q & A

What is the main topic of theFlux 2 GGUF tutorial video?
-The video focuses on the Flux 2 GGUF workflow, a process for using GGUF models with text encoders, particularly aimed at users with low VRAM.
What variations of the text encoder are available for the GGUF models?
-There are three variations of the text encoder for the GGUF models: FP8, FP16, and GGUF.
How many reference image inputs are implemented in the workflow?
-The workflow includes eight reference image inputs, though the model can technically support up to 20. The user can bypass unneeded ones.
What happens if you bypass certain nodes in the workflow?
-Bypassing nodes like 'load image,' 'VAE encode,' and 'reference latent' allows for flexibility in the workflow and reduces unnecessary processing.
What is the role of the Flux guidance node, and what scale is recommended for text generation?
-The Flux guidance node controls how closely the generated image follows the prompt. The recommended scale for text generation is between 8 and 11, with 11 providing the bestJSON code correction results but possibly introducing some artifacts.
How does the Flux 2 workflow perform on low VRAM systems?
-Despite being a VRAM-intensive workflow, the Flux 2 model works well on systems with 8GB of VRAM and 16GB of RAM without significant errors.
What is the ideal step count for generating high-quality images in the Flux 2 workflow?
-The ideal step count for high-quality images is 50 steps, as it reduces noise and artifacting. Higher steps may improve quality further, but they haven't been tested extensively.
Why is the dip node not recommended for low VRAM users?
-The dip node causes noise and bloating in images, and it doesn't adhere well to prompts. For low VRAM users, the dip node is less effective and should be avoided. However, Flux 2 offers a more efficient alternative for better results.
What alternative is suggested for upscaling images for low VRAM users?
-Instead of using the dip node for upscaling, users are advised to post-generate upscale with tools like Hirez Fix or Real ESR.
What does the comparison between Flux guidance scales (4, 8, 11) show about text accuracy in images?
-The comparison shows that as the Flux guidance scale increases, the text accuracy improves. At a scale of 4, text can be jumbled or missing, while at scale 8, the text is clearer with minimal artifacts, and at scale 11, the text is most accurate but with a slight increase in artifacts.

Outlines

00:00

🔧 Overview of Flux 2 GGUF Workflow and Model Setup

In this section, the presenter introduces the Flux 2 GGUF workflow for generating images. They explain that the workflow includes a choice of text encoders (FP8, FP16, and GUF) depending on the system's capabilities. The workflow is designed to integrate with the text encoders, and the speaker outlines how to handle reference images, noting that eight is a good number for most uses. The importance of bypassing certain nodes is highlighted to avoid unnecessary processing. The workflow also includes a seed generator and a 'Laura loader' for loading a model, though its functionality is untested. Additional features like the clip node, VAE file, and Flux guidance node are discussed, emphasizing the importance of the guidance node for ensuring high-quality outputs. The section closes with a note on VRAM and memory usage considerations.

05:02

🧑‍💻 Detailed Setup of Nodes and Configuration Tips

This paragraph delves deeper into the specific nodes in the Flux 2 workflow. The speaker discusses the K sampler node and tests with different step counts (20, 30, 40, 50) to optimize the image generation process. They found thatFlux 2 GGUF workflow 50 steps yielded the best results in terms of image quality, with no artifacts or bloating. The use of the dip node for enhancing images is discouraged due to the resulting noise and poor adherence to the prompt. The paragraph also covers the resource-heavy nature of the workflow, with special mention of the IP node being time-consuming and less practical for low VRAM users. Suggestions for using external upscaling methods like 'hireers fix' are provided. The speaker points out that alternative workflows with less resource demand, such as those using guff models, exist.

10:05

📸 Flux Guidance Scale Testing and Results

In this section, the speaker compares the performance of the Flux 2 model at different flux guidance scales: 4, 8, and 11. The guidance scale directly influences the accuracy of the text in the image, with the speaker noting that a scale of 4 resulted in missing or jumbled text, while a scale of 8 produced some artifacts but was generally more accurate. At a scale of 11, the text and image quality were at their best, with proper incorporation of the prompt's text and improved image clarity. The comparison is illustrated with examples, including a range of generated images with varying levels of text integration and accuracy, showing how the guidance scale impacts the final output.

🎨 Image Examples and Final Thoughts

The speaker wraps up by showcasing various examples of images generated using the Flux 2 workflow. These include a cup of hot drink, a watercolor owl, a hot air balloon, a dragon with a crystal, a gremlin-like creature, a Pokémon, a Disney-style lion, and examples of effective text integration into images. The speaker highlights that Flux 2 excels in generating text-based images, particularly at higher guidance scales. The presentation concludes with a call to action for viewers to download the workflow and models, start generating images, and engage with the content by liking, commenting, and subscribing to the channel.

Mindmap

Keywords

💡Flux 2

Flux 2 is the specific generative model or model family the video is focused on — a successor/variant to 'Flux 1' mentioned in the script. The tutorial revolves around building a workflow for running Flux 2 GGUF models under low-VRAM conditions, comparing guidance behavior and practical settings (for example, the host contrasts Flux 1's dual CLIP loader with Flux 2's single baked CLIP file).

💡GGUF

GGUF is the model file/container format the presenter uses for Flux 2; it commonly stores weights and model metadata in a compact form. In the video the creator describes a condensed workflow specifically for GGUF models ("the Flux 2, GGUF workflow") and links to model downloads in the workflow description, so GGUF is central to loading and running the model.

💡Text encoder (FP8 / FP16 / GUF)

A text encoder converts prompt text into the internal embeddings the model uses to guide image generation; different numeric precisions (FP8, FP16) or encoder variants affect memory use and compatibility. The script explains there are multiple text encoder variations you can choose — FP8, FP16, or a 'GUFFlux 2 GGUF tutorial' encoder — and suggests choosing a lighter encoder if you cannot hold the full text encoder weights in memory.

💡Reference images

Reference images are example images fed into the workflow so the model can reuse or imitate visual content and structure; the host implemented eight reference image inputs in the workflow. The presenter notes Flux 2 can call for many references (native model can accept up to 20) but chose eight for practicality and explains how to bypass unused reference nodes (load image, image scale, VAE encode, reference latent) when you don't need them.

💡VAE (Variational Autoencoder)

The VAE is a component used to encode and decode image latents — in Flux 2 the host references a new Flux 2 VAE file rather than the older 'AE.safe tensor'. This VAE stage appears as specific nodes in the workflow (VAE file, VAE encode) and is part of the pipeline when reference images or latents are processed.

💡CLIP / clip node

CLIP is the model that links text and image spaces; Flux 2 uses a CLIP checkpoint to understand prompts. The creator points out that whereas Flux 1 used a dual CLIP loader, Flux 2 has the CLIP baked into a single clip file so you only need one clip node in the workflow, simplifying prompt-to-image alignment.

💡Flux guidance scale

Flux guidance scale is the multiplier controlling how strongly the model follows the text prompt during generation (analogous to guidance scale in other diffusion setups). The video tests guidance values of 4, 8, and 11: 4 produced weaker text adherence, 8 was a good middle ground with fewer artifacts, and 11 gave the best text rendering (especially for banners and in-image text) but could introduce artifacting when too strong.

💡Seed / random noise

The seed is the pseudo-random initialization that determines reproducibility of the generated image; in the workflow the 'random noise' node is actually the seed generator. The presenter emphasizes testing with the same seed, prompt, and steps when comparing guidance scales so differences are attributable to guidance, not randomness.

💡K sampler (Euler / Euler a / 'Uler')

A sampler determines the numerical process used to step the reverse diffusion; the video uses a K sampler (specifically the Euler-family sampler, spelled 'Uler' in the transcript) for generation. The host tested different step counts with this sampler (20, 30, 40, 50 steps) and found 50 steps produced clean, high-quality images without artifacting in their tests.

💡Steps (20–50+)

Steps are the number of denoising iterations the sampler runs; increasing steps usually improves image quality at the cost of time and compute. The tutorial author reports testing 20 through 50 steps and finding 50 steps a good high-quality setting; they also mention they haven't tested 60–100 but expect further quality improvements with more steps.

💡VRAM / low VRAM workflow (Q2, Q4)

VRAM refers to GPU memory, and the video is targeted at users with limited VRAM — the author calls this a 'LOW VRAM' workflow and comments on quantization levels (Q2, Q4) that reduce memory. The presenter notes the Q2 model already used about 12 GB in some tests (problematic for 8 GB GPUs), yet they report the workflow can run cleanly on 8 GB VRAM with 16 GB system RAM in their setup, emphasizing practical tips for low-VRAM users.

💡Batch size

Batch size is how many images are generated at once and directly affects memory use; the host strongly recommends not touching the batch size node because the workflow is already VRAM intensive. Changing batch size upward would increase memory demands and likely cause failures on low-VRAM machines.

💡Guider node / flux guidance node

The guider or flux guidance node is the pipeline element that applies the guidance multiplier and influences how text conditioning steers the output. The video spends time on this node because adjusting it (e.g., to 8 or 11) materially changes how well text appears in images — the host found 11 handled text best but could produce artifacting, so 8–10 is a safer middle ground.

💡DIP node (and IP upscaling)

The DIP node (likely referring to a denoising or internal processing block) and the IP upscaler are optional components the creator experimented with; the DIP node produced more noise and bloat in their tests and did not preserve prompt adherence, so it's not recommended. For upscaling, the author suggests post-generation upscalers like HighResFix or Real-ESRGAN (referred to in the script as 'hireers fix' and 'real estr') rather than the time-consuming in-pipeline IP upscaler.

💡VAE encode / reference latent

VAE encode converts loaded reference images into latent representations; those latents are then used by the model to condition generation (the script references nodes named VAE encode and reference latent). The presenter instructs users to bypass these nodes when they aren't providing reference images so the workflow won't process unnecessary inputs.

💡HighResFix / Real-ESRGAN (post-generation upscaling)

HighResFix and Real-ESRGAN are external post-processing upscalers the speaker recommends for improving resolution after generation because the pipeline's IP upscaler is slow and VRAM-heavy. The host specifically suggests using those tools (or the seedbr/GGUF workflows on Civitai they mention) to upscale generated images without the heavy in-pipeline cost.

Highlights

Rebel introduces a condensed workflow for GGUF models designed for low VRAM users.

The tutorial covers different variations of text encoders: FP8, FP16, and GGUF.

Users can input up to 8 reference images, though the native model can handle up to 20.

Reference images can be bypassed if not needed for editing, allowing for flexibility in the workflow.

The workflow includes a seed generator that ensures consistent random noise.

A Laura loader is included, but it has not been tested thoroughly with Flux 2 models.

Flux 2 now includes a baked-in clip loader, replacing the need for dual clip loaders from Flux 1.

The Flux 2 VAE replaces the AE.safe tensor from Flux 1, offering improved performance.

The Flux guidance scale is crucial for generating images that follow the text prompt accurately.

Testing shows that increasing the Flux guidance scale to 8 or 11 improves text incorporation, reducing artifacts.

Flux guidance at 11 produces the best text accuracy with minimal artifacts.

Recommended guidance scale for text-heavy images is around 8 to 10 for better text generation with minimal noise.

The batch size node should not be modified as the workflow is already VRAM intensive.

The workflow performs well with 8GB VRAM and 16GB RAM withoutFlux 2 GGUF workflow errors, even with the Q2 model.

The Uler sampler node is recommended for best results, as other samplers like dip introduce noise.

The tutorial offers a comparison of Flux guidance scales (4, 8, and 11), with examples of text integration in images.

Examples in the tutorial include creative images like a cup of hot drink, a watercolor owl, a dragon, and text incorporated into an image.

Rebel emphasizes that Flux 2 excels in handling text prompts compared to Flux 1, especially with higher guidance scales.

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