Super Fast Image Generation in stable diffusion using LCM LoRA

#lcm #stablediffusion #aiimages #a1111 #aiupdates #LoRA #art Update: LCM sampler is available in A1111 now. 00:00:00 Introduction and sample results LCM Latent Consistency Models,This new LoRA Model requires only 4 to 8 sampling steps instead of the usual 25 to 50 steps, to produce complete detailed images. 00:00:43 Downloading LCM LoRAs where to download them from 00:01:31 using LCM in A1111 00:02:55 LCM Settings for CFG and Sampling steps LCM is used at 4-8 sampling steps, and 1-2 CFG scale, can be used with Euler a, but best with LCM Sampler (manual installation can be done for this, but it is not yet official in A1111) 00:04:53 example for using LCM with after detailer and controlnets test 00:05:32 LCM Dreamshaper_v7 checkpoint 00:06:12 conclusion message Computer Specs: Laptop: Legion 5 Pro Processor :AMD Ryzen 7 5800H , 3201 Mhz System RAM: 16.0 GB Graphics GPU: NVIDIA GeForce RTX 3070 Laptop GPU 8GB LCM LoRA doesn’t require a special plugin or installation, it can be used like a normal LoRA model, The Latent Consistency Models or LCM are trained in a different way than normal LoRA models, which allows to infer images faster. for model downloads and more reading please check the links bellow. Hugging face page https://huggingface.co/latent-consistency SD 1.5 LoRA model https://huggingface.co/latent-consistency/lcm-lora-sdv1-5/tree/main SDXL LoRA model https://huggingface.co/latent-consistency/lcm-lora-sdxl/tree/main Dreamshaper v7 LCM https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7/tree/main More reading https://huggingface.co/blog/lcm_lora Note: for Hires fix, I found it to work best at higher denoising strength such as 0.7, producing very reliable results with hires steps of 5 for example when using LCM LoRA. Note: for img2img, you should double check the actual number of steps being used, for instance, if you didnt enable an option in the User Interface settings that allows using same number of steps for img2img then 15 steps would only ren