Fix Deformed Generations
- Prevent deformities by using negative prompts (such as negatively weighted terms) to de-emphasize them.
What are Deformed Generations?
Deformed generations, particularly on human body parts (e.g. hands, feet), are a common issue with many models. This can be dealt with to some extent with good negative prompts. The following example is adapted from this Reddit post.
An Example of Fixing Deformed Generations
Using Stable Diffusion v1.5 and the following prompt, we generate a nice image of Brad Pitt, except for his hands of course!

Prompt
Using a robust negative prompt, we can generate much more convincing hands.

Prompt
Using a similar negative prompt can help with other body parts as well. Unfortunately, this technique is not consistent, so you may need to attempt multiple generations before getting a good result. In the future, this type of prompting should be unnecessary since models will improve. However, currently, it is a very useful technique.
Conclusion
AI models are prone to producing deformed generations, especially when it comes to human body parts. Fortunately, we can include negative prompts like the one in the example above to fix these bad outputs.
FAQ
Why might I want to include negative prompts in my generative AI inputs?
Using negative prompts with techniques such as weighted terms can improve image generations, explicitly instructing the AI to de-emphasize deformed aspects.
Will fixing deformed generations always be necessary?
It's useful to note that as generative models get better, it will likely be less necessary to use the techniques described in this article.
Notes
Improved models such as Protogen are often better with hands, feet, etc.
Sander Schulhoff
Sander Schulhoff is the CEO of HackAPrompt and Learn Prompting. He created the first Prompt Engineering guide on the internet, two months before ChatGPT was released, which has taught 3 million people how to prompt ChatGPT. He also partnered with OpenAI to run the first AI Red Teaming competition, HackAPrompt, which was 2x larger than the White House's subsequent AI Red Teaming competition. Today, HackAPrompt partners with the Frontier AI labs to produce research that makes their models more secure. Sander's background is in Natural Language Processing and deep reinforcement learning. He recently led the team behind The Prompt Report, the most comprehensive study of prompt engineering ever done. This 76-page survey, co-authored with OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions, analyzed 1,500+ academic papers and covered 200+ prompting techniques.
Footnotes
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Blake. (2022). With the right prompt, Stable Diffusion 2.0 can do hands. https://www.reddit.com/r/StableDiffusion/comments/z7salo/with_the_right_prompt_stable_diffusion_20_can_do/ β©