Stable Diffusion is a game-changing AI tool for image generation, enabling you to create stunning artwork with code. However, mastering it requires an understanding of the underlying concepts and techniques. This book guides you through unlocking the full potential of Stable Diffusion with Python. S...
Using Stable Diffusion with Python : Leverage Python to control and automate high-quality AI image generation using Stable Diffusion Andrew Zhu (Shudong Zhu) $44.99 4.8 (5 Ratings) Paperback Jun 2024 352 pages 1st Edition eBook $31.99 $35.99 Paperback $44.99 Subscription Free Trial Ren...
This book offers a comprehensive, Python-based approach to mastering Stable Diffusion for AI image generation. Unlike other resources on this topic that focus mainly on using web interfaces, Using Stable Diffusion with Python delves into the technical aspects of controlling Stable Diffusion programmatica...
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Textual inversion in Stable Diffusion creates a new word token associated with distinct features of a set of images. This allows the model to recreate those features when generating new images. The first step is to collect data that embodies the desired concept. ...
Deploy Stable Diffusion-XL using Inferless: Deployment of Stable Diffusion-XL model usingDiffusers. By using the Diffusers, you can expect an average latency of 2.506 sec. Prerequisites Git. You would need git installed on your system if you wish to customize the repo after forking. ...
Full Usage - Python # Initialize diffusion generatorfromcgdimportclip_guided_diffusionimportcgd_utilcgd_generator=clip_guided_diffusion(prompts=["an image of a fox in a forest"],image_prompts=["image_to_compare_with_clip.png"],batch_size=1,clip_guidance_scale=1500,sat_scale=0,tv_scale=150,...
Predicting wildfire spread behavior is an extremely important task for many countries. On a small scale, it is possible to ensure constant monitoring of the natural landscape through ground means. However, on the scale of large countries, this becomes pr
从数据集分析,flower 或者 cub的描述通常只是对单一目标进行详细描述,这样但前的几个任务生成的效果是很好的, 但是,在COCO数据集中,由于存在多个目标,而且在一个描述中不包含所有目标的前景背景的细节描述。 像这样的图片,相同的COCO描述,完全不同的图片,缺少更多细节描述 ...
Solving one-dimensonal SDEs du = f(u,t)dt + g(u,t)dW_t is like an ODE except with an extra function for the diffusion (randomness or noise) term. The steps follow the SDE tutorial.def f(u,p,t): return 1.01*u def g(u,p,t): return 0.87*u u0 = 0.5 tspan = (0.0,1.0)...