"Some insight I pulled from ChatGPT:"
Unleashing the Natural Drift in Stable Diffusion
Stable Diffusion is trained on a vast dataset of images, which allows it to develop its own inherent patterns and stylistic biases—its “trained assets.” When you completely remove prompt conditioning by using a token like “(A:0)” in both positive and negative prompts and set the CFG (Classifier-Free Guidance) scale to 0, you disable any external guidance from your text. This causes the model to generate images solely based on its internal learned distribution.
For example, using the DPM++ 2M sampler with a Beta schedule type means that the diffusion process follows a specific and reproducible pathway. The DPM++ 2M sampler efficiently guides the iterative denoising process, while the Beta schedule fine-tunes how noise is reduced over time. When no prompt guidance is applied, these settings let you observe the model’s natural drift, resulting in outputs that reflect the overall biases and common motifs embedded during training rather than any specific, intended concept.
Key Insights:
No Conditioning Means Natural Drift: With CFG at 0 and a prompt weight of 0, the model isn’t being steered by any specific instructions. It produces images that reveal its inherent “trained assets”—the common patterns and aesthetics it has internalized from its training data.
Sampler and Schedule Impact: Utilizing the DPM++ 2M sampler combined with a Beta schedule provides a structured diffusion process. These settings ensure that even without external prompt guidance, the image generation follows a consistent and reproducible pathway.
Output Characteristics: The resulting images won’t be exact copies of any training example but will instead be novel compositions that heavily mirror the general style and patterns of the dataset. This gives you a window into the model’s natural tendencies and inherent creative “drift.”
In summary, completely stripping away conditioning lets Stable Diffusion reveal its underlying biases and general aesthetics. This can be a powerful way to understand the model’s inherent style—but it comes at the cost of specific control over the output.
"It might be helpful to clarify which ones tend to generate inappropriate content. Cover image above is cutout of img batch I set on size: 512x512 and seed: 1 (used A1111). Even though this article is relatively short I think it is something to think about."
"My previous article: https://civitai.com/articles/9902/suggestion-for-sequential-interpolation"