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SemiRealMix is an AI model created by robotjung that aims to generate semi-realistic human images. It is the result of many merges to improve the quality of semi-realistic human generation. This model can be compared to similar models like Ekmix-Diffusion and dreamlike-photoreal-2.0, which also focus on producing photorealistic images. Model inputs and outputs Inputs Prompt:** The model accepts a text prompt to guide the image generation, such as "delicate, masterpiece, best shadow, (1 girl:1.3), (korean girl:1.2), (from side:1.2), (from below:0.5), (photorealistic:1.5), extremely detailed skin, studio, beige background, warm soft light, low contrast, head tilt". Negative Prompt:** The model also accepts a negative prompt to avoid certain unwanted elements, such as "worst quality, low quality, nsfw, nude, (loli, child, infant, baby:1.5), jewely, (hard light:1.5), back light, spot light, hight contrast, (eyelid:1.3), outdoor, monochrome". Outputs Images:** The primary output of the SemiRealMix model is photorealistic human images, as shown in the examples provided. Capabilities The SemiRealMix model is capable of generating semi-realistic human images with a high level of detail and quality. The examples demonstrate the model's ability to create realistic-looking portraits, with natural-looking skin, hair, and facial features. The model can also handle a variety of poses and angles, as well as different lighting conditions. What can I use it for? The SemiRealMix model could be useful for a variety of applications, such as creating photorealistic character designs, concept art, or promotional images. The model's ability to generate semi-realistic human images could be particularly valuable for industries like advertising, entertainment, or gaming, where high-quality visual assets are in demand. Things to try One interesting aspect of the SemiRealMix model is its ability to handle detailed prompts with specific instructions, such as the use of modifiers like "(1 girl:1.3)" or "(photorealistic:1.5)". Users can experiment with different prompt variations to see how the model responds and potentially create more tailored or specialized outputs.

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Updated 5/17/2024