Stable Diffusion is a powerful tool for generating images, but it often produces predictable and standard results. This guide aims to introduce a method for creating more intriguing and dynamic images, departing from the usual outcomes of Txt2Img. By following the steps outlined below, users can produce visually striking and unique images, departing from the conventional forms generated by Stable Diffusion. This has been inspired by one of the users at reddit

Step 1: Selecting the Appropriate Stable Diffusion Model

Begin by selecting an older version of Stable Diffusion, such as Stable Diffusion 1.1, which possesses less coherence and a higher degree of randomness. This choice of model serves as the initial step in creating images that are more abstract and visually engaging.

Step 2: Generating the Underpainting

Next, generate a series of initial images, referred to as the ‘underpainting’. Utilize a range of abstract and random keywords, while keeping in mind the desired final image. Emphasize lengthier prompts, as Stable Diffusion will amalgamate keywords randomly when unable to combine them into a single image. The content of these initial images is not significant, allowing for the inclusion of elements that may appear visually discordant or anatomically inaccurate. The emphasis here is on creativity and abstraction. For instance, consider a prompt bodybuilder blue hair smoke japanese inspired imagery solarization blink like:

“An elegant, fit, shirtless male mage casting a spell of water explosion, with elements such as water butterflies, spiral and circular compositions, water splashes, and flowers made of water. Include terms such as hyperrealistic art, fantasy concept art, abstract, surreal, extravagant, vivid, vibrant, detailed art, flowing water, water magic, bubbles, drops, splashes, waves, colorful, dramatic, dynamic, and character concept art.”

Step 3: Creating the Final Image

To create the final image, utilize a more advanced and coherent model, such as Airfuck’s Wild Mix. Proceed to the Img2Img interface, and input one of the ‘underpainting’ images. Leave the main prompt field blank, and employ a well-crafted Negative Prompt. Choose a suitable sampler, and set the denoising strength to a moderate value (e.g., DPM++ 2M Karras, 30 Steps, and Denoising at 0.4). Allow Stable Diffusion to process the abstract image and generate a more cohesive and meaningful final result.

Enhancing the Result

Further improvements can be made by iteratively applying the denoising process or inputting specific keywords into the Positive Prompt to guide Stable Diffusion towards the desired outcome. Experimentation with these parameters can lead to even more compelling and refined images.

Final Thoughts

The method outlined above presents a creative approach to image generation, offering a departure from the conventional and often predictable outcomes of Stable Diffusion. By leveraging older models of Stable Diffusion with lower coherence and increased randomness, users can initiate the process with a unique foundation, setting the stage for the creation of visually striking and dynamic images.

The emphasis on generating an ‘underpainting’ through the use of abstract and random keywords allows for a departure from traditional image creation norms. The resulting images may initially appear discordant or visually abstract, but they serve as the basis for the subsequent transformation into more coherent and meaningful final images. This process encourages a departure from rigid constraints and invites a more open and imaginative approach to image generation.

Through the utilization of more advanced and coherent models like Airfuck’s Wild Mix in the final stages, users can effectively refine and enhance the initial abstract images. By carefully selecting parameters such as the sampler and denoising strength, the generated images undergo a process of transformation, resulting in visually captivating and thought-provoking compositions. The method encourages users to experiment with different parameters, allowing for a personalized and iterative approach to image refinement.

Furthermore, the approach’s flexibility and creative potential are highlighted by the ability to iteratively refine the images through multiple denoising passes or by providing specific keywords in the Positive Prompt. This aspect of the process empowers users to fine-tune the generated images, enabling the manifestation of specific artistic visions or conceptualizations. The iterative refinement process encourages users to actively engage with the creative process, fostering a sense of exploration and experimentation.

Overall, the method not only facilitates the generation of visually engaging and dynamic images but also encourages users to embrace creativity and artistic expression. By stepping away from conventional constraints and embracing a more abstract and experimental approach, users can unlock new avenues for artistic exploration. The resulting images are characterized by their unique and captivating qualities, showcasing the potential of combining AI capabilities with human creativity.

Ultimately, the method serves as an invitation to explore the boundaries of image generation and to discover new possibilities in the realm of digital art. By embracing the unpredictability and abstract nature of the process, users can unlock a world of creative potential, leading to the production of visually stunning and conceptually rich artworks. Embrace the journey of exploration and experimentation, and uncover the endless possibilities of AI-assisted creative expression.