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CyberRealisticXL_Desire

**CyberRealistic XL Desire** is a photorealistic image generation model based on **Stable Diffusion XL 1.0 (SDXL)**. It is designed to produce highly realistic, cinematic-quality outputs with minimal prompt engineering. This checkpoint is optimized for portraits, fashion, and editorial-style scenes, but also handles a wide range of creative prompts with remarkable fidelity.

stable-diffusion-1.5-fp16

controlnet-depth-sdxl-1.0

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - controlnet inference: false --- These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with depth conditioning. You can find some example images in the following. For more details, check out the official documentation of `StableDiffusionXLControlNetPipeline`.

stable-diffusion-v1-4

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 🤗's Stable Diffusion with 🧨Diffusers blog. The **Stable-Diffusion-v1-4** checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. This weights here are intended to be used with the 🧨 Diffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, come here

Ar4ikov_-_gpt2-medium-stable-diffusion-prompt-generator-gguf

tiny-stable-diffusion-torch

stable-diffusion-3-medium

cyberrealistic-v32

stable-diffusion-3.5-medium-tensorrt

control_v11e_sd15_shuffle

This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } Controlnet was proposed in *Adding Conditional Control to Text-to-Image Diffusion Models* by Lvmin Zhang, Maneesh Agrawala.