The following AI generations use the Stable Diffusion installed on my laptop (windows os, nvidia 3060). My Stable Diffusion settings are 20 Sampling Steps, and each image employs a different Sampling Method (noted in the alternative text). This experiment provides insights into how various sampling methods generate images related to specific visual themes and features. I used the prompts “portrait of a super cute hybrid fox rabbit in a snowy pine forest” and “a super cute fox rabbit in a snowy pine forest,” which are also listed in each image’s alternative text. Previous articles on this website explain this technique and my preference for starting with the DDIM Fast sampling method for new text prompts; you can find those details in the Text Prompt Tips category.

AIGeneration.blog allows you to use Stable Diffusion via a web interface for unlimited AI-generated image experiments. Contact monigarr@MoniGarr.com to get your own custom AI Generation web interface.

Stable Diffusion Checkpoint Inkpunk-Diffusion-v2.ckpt

Stable Diffusion Checkpoint blueprint.safetensors

Text Prompt: portrait of a super cute hybrid fox rabbit in a snowy pine forest

Stable Diffusion Checkpoint dreamlike-diffusion-1.0.safetensors

Text Prompt: portrait of a super cute hybrid fox rabbit in a snowy pine forest

Stable Diffusion Checkpoint future-diffusion-v1.ckpt

Text Prompt: portrait of a super cute hybrid fox rabbit in a snowy pine forest

Stable Diffusion Checkpoint robo-diffusion-v1.ckpt

Text Prompt: portrait of a super cute hybrid fox rabbit in a snowy pine forest

Stable Diffusion Checkpoint v1-5-pruned-emaonly.ckpt

Text Prompt: portrait of a super cute hybrid fox rabbit in a snowy pine forest

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REFERENCES:

Abraham, T. M. (2022, August 31). Sampler vs Steps. Retrieved February 5, 2023, from https://twitter.com/iScienceLuvr/status/1564847717066559488

Dahlquist, Germund (1963), A special stability problem for linear multistep methods. Retrieved February 5, 2023 from BIT, 3: 27–43, doi:10.1007/BF01963532, ISSN 0006-3835, S2CID 120241743.

Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., & Zhu, J. (2022, October 13). DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. Arxiv.org. Retrieved February 5, 2023, from https://arxiv.org/abs/2206.00927

Mlynarczyk, A. (2022, December 7). Stable Diffusion and the Samplers Mystery. Weights and Biases. Retrieved February 5, 2023, from https://wandb.ai/agatamlyn/basic-intro/reports/Stable-Diffusion-and-the-Samplers-Mystery–VmlldzoyNTc4MDky

P. (2022, October 1). How to get images that don’t suck: A Beginner/Intermediate Guide to Getting Cool Images from Stable Diffusion. Retrieved February 5, 2023, from https://www.reddit.com/r/StableDiffusion/comments/x41n87/how_to_get_images_that_dont_suck_a/

Rombach, Robin and Blattmann, Andreas and Lorenz, Domink and Esser, Patrick and Ommer, Bj. (2022, June). Stable Diffusion Version 1.5. Retrieved February 5, 2023 from https://huggingface.co/runwayml/stable-diffusion-v1-5

Karras, T., Aittala, M., Aila, T., & Laine, S. (2022, June 1). Elucidating the Design Space of Diffusion-Based Generative Models. Arxiv.org. Retrieved February 5, 2023, from https://arxiv.org/abs/2206.00364