DMD2 | 1 CFG SCALE | Fewer Steps - V5 | DTLVVTT_DMD2 (LITE)

Hello, you’re probably wondering: why so many versions?

Well… I’d be asking the same thing if I were in your place. The reason is simple: it’s designed this way to offer more control, since, unlike normal LoRAs, DMD2 works best at its maximum strength.

For example:

• HD 1 CFG Scale has “diluted” strength, so it requires the help of triggers or manually increasing its LoRA strength. This makes it very useful for combining with PDXL LoRAs in Illustrious, since you can simply raise the strength without losing details.

• DPM A1 and DPM A15 already come with boosted strength and detail, so they don’t require triggers. A1 is the standard strength, while A15 adds an extra +15%.

• V4 is an experiment to generate images in 2 steps. It was created in the opposite way to HD 1 CFG: instead of reducing strength to improve stability, V4 increases strength by 1.35 ratios (20 more than DPM A15).

• V5 Enhancing checkpoint details, emphasizing styles, and making prompts more effective without changing your model.

In short: it depends on your taste and goal. For example, V4 will produce more “noise” (details) and may sacrifice some realism unless you use it with a realistic checkpoint.

But what is this for?

This LoRA is based on the architecture and style of DMD2, a well-known approach for optimizing diffusion models by focusing on reducing the number of generation steps without compromising visual quality.

So... What is DMD2?

DMD2 (Denoising Diffusion Probabilistic Model 2) is a variant of probabilistic diffusion models, designed to generate high-quality images from noise through an iterative denoising process.
According to the literature (e.g., Ho et al., 2020, Denoising Diffusion Probabilistic Models), DMD2 optimizes the denoising process by reducing the number of steps required to achieve a quality level comparable to traditional models like DDPM.

DMD2 uses an improved parameterization of the reverse diffusion process, adjusting variance weights and denoising terms to accelerate convergence.
In the context of LoRAs, DMD2 serves as the base for training Low-Rank Adaptation modules that fine-tune a pretrained model (such as Stable Diffusion) for specific tasks, minimizing computational cost while preserving visual quality.

In conclusion:

The LoRAs described here (HD_DMD2_1_CFG-SCALE, DPM_4STEPS_A1, DPM_4STEPS_A15 and V4) are adaptations leveraging the DMD2 structure to operate with a CFG scale of 1.

This is particularly interesting because normally a higher CFG scale is needed to maintain the same quality, but these LoRAs can reduce the step count to 4, 6, 8, or 10 (10 being the minimum allowed on Civitai) while achieving impressive results—cutting generation times from minutes to just a few seconds.

Key Features

• Optimized for fast generation: Designed to produce high-quality images with a very low number of inference steps (4, 6, or 8), enabling quick and efficient generation.

• Low effective CFG scale: Works optimally around a CFG scale of 1, providing an ideal balance between creativity and fidelity without overfitting.

• Three variants for different needs: Includes versions tailored for 8, 6, and 4 steps, offering flexibility depending on speed and detail requirements.

• Robust visual quality: Maintains strong detail in colors, textures, and composition even with reduced steps—perfect for applications requiring both speed and quality.

• Wide applicability: Suitable for users aiming to optimize generation time without sacrificing definition in their images.

Usage Instructions & Recommendations

• If the LoRA you’re using requires more steps to achieve a good result, you can increase the LoRA strength or add positive prompts with keywords like "hdr" to improve lighting and detail, and negative prompts like "flat color" to control saturation and shadows.

• Alternatively, you can lower the LoRA strength, which allows you to use higher CFG scales without oversaturating the image. However, since this LoRA is primarily designed for CFG scale 1, the ideal strength may vary depending on your specific use case.

• Experiment with both strength and CFG scale to find the optimal balance for your workflow and desired style.