LCM-LoRA 代表潜在一致性(Latent Consistency Model)模型 – 潜在残差适配器(Latent Residual Adapters)。这项技术可以通过将 LDM 蒸馏成更小、更快的模型来加速 LDM,而不会牺牲图像质量。 LCM-LoRA 的核心思想是训练少量适配器(称为 LoRA 层),而不是完整模型。 LoRA 层插入到 LDM 的卷积块之间,并学习模仿原始...
模型:majicMIX realistic v6提示词:Best quality,masterpiece,ultra high res,(photorealistic:1.4),raw photo,1girl,silver hair,shiny skin,dramatic lighting,<lora:LCM_LoRA_Weights_SD15_v1.0#2567_4104_4823@8f90d840e0:1>, // 替换成自己下载的 LoRA采样方法:Euler尺寸:512x512步数:4CFG:1.2 需要注意...
我们可以通过卸掉 LoRA 权重并切换回默认调度器来将流水线快速恢复为标准 SDXL 流水线: from diffusers import EulerDiscreteScheduler pipe.unload_lora_weights() pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) 然后,我们可以像往常一样对 SDXL 进行推理。我们使用不同的步数并观察其结...
LCM_LoRA_Weights_SD15.safetensors LCM_LoRA_Weights_SDXL.safetensors lcm-lora-ssd-1b.safetensors vae LCM_Dreamshaper_v7_vae.safetensors vae_decoder LCM_Dreamshaper_v7_decoder.onnx vae_encoder LCM_Dreamshaper_v7_encoder.onnx Weights LCM_Dreamshaper_v7_4k.safetensors lcm-sdxl-Weights.safe...
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我们可以通过卸掉 LoRA 权重并切换回默认调度器来将流水线快速恢复为标准 SDXL 流水线: fromdiffusersimportEulerDiscreteScheduler pipe.unload_lora_weights() pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) 然后,我们可以像往常一样对 SDXL 进行推理。我们使用不同的步数并观察其结果:...
Portability: an LCM-LoRA can be applied to ANY Stable Diffusion checkpoint models. LCM-LoRA for Stable Diffusion v1.5 and SDXL models are available. Essentially, you speed up a model when you apply the LoRA. Faster training: LoRA has a smaller number of weights to train. So it is faster...
1、支持SVD动画,SD作画中图生图选择参考图,动画设置里选择SVD,目前宽高设置、队列选择多个参考图、种子、步数、总帧数、显存占用模式这几个参数可以设置; 2、SD、CN新增LCM采样器,此采样器5步就能出好图,最好配合网盘里的lora模型pytorch_lora_weights.safetensors,加载这个lora占比1,描述相关度改成0,或者1-2的...
LCM 模型 通过将原始模型蒸馏为另一个需要更少步数 (4 到 8 步,而不是原来的 25 到 50 步) 的版本以减少用 Stable Diffusion (或 SDXL) 生成图像所需的步数...
Quality vs base SDXL How does this compare against the standard SDXL pipeline, in terms of quality? Let’s see an example! We can quickly revert our pipeline to a standard SDXL pipeline by unloading the LoRA weights and switching to the default scheduler: from diffusers import E...