SD v2同样有一个一句话定义:Stable Diffusion v2 refers to a specific configuration of the model architecture that uses a downsampling-factor 8 autoencoder with an 865M UNet and OpenCLIP ViT-H/14 text encoder for the diffusion model. The SD 2-v model produces 768x768 px outputs. 有三个变...
https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.ckpt 下载配置文件 下载会得到的v2-inference-v.yaml请重命名为v2-1_768-ema-pruned.yaml https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml(直接用...
SD v2同样有一个一句话定义:Stable Diffusion v2 refers to a specific configuration of the model architecture that uses a downsampling-factor 8 autoencoder with an 865M UNet and OpenCLIP ViT-H/14 text encoder for the diffusion model. The SD 2-v model produces 768x768 px outputs. 有三个变...
(MindSpore) [ma-user stablediffusionv2]$bash scripts/infer.sh workspace /home/ma-user/work/stablediffusion/code/minddiffusion-main/vision/stablediffusionv2 WORK DIR:/home/ma-user/work/stablediffusion/code/minddiffusion-main/vision/stablediffusionv2 Loading model from models/stablediffusionv2_512.ckpt ...
New stable diffusion model (Stable Diffusion 2.0-v) at 768x768 resolution. Same number of parameters in the U-Net as 1.5, but uses OpenCLIP-ViT/H as the text encoder and is trained from scratch. SD 2.0-v is a so-called v-prediction model. The above model is finetuned from SD ...
比如我们采用开源的dreamlike-diffusion-1.0模型(基于SD v1.5精调的),其生成的图像效果在变尺寸上就好很多: 另外一个参数是num_inference_steps,它是指推理过程中的去噪步数或者采样步数。SD在训练过程采用的是步数为1000的noise scheduler,但是在推理时往往采用速度更快的scheduler:只需要少量的采样步数就能生成不错...
Model: StableDiffusion v-1.5 Precision: Float16 Pretrained ckpt path: models/sd_v1.5-d0ab7146.ckpt Lora ckpt path: None Textual Inversion ckpt path: None Sampler: dpm_solver_pp Sampling steps: 20 Uncondition guidance scale: 7.5 Target image size (H, W): (512, 512) ...
tokenizer = CLIPTokenizer.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="tokenizer") # 初始化UNet unet = UNet2DConditionModel(**model_config)# model_config为模型参数配置 # 定义scheduler noise_scheduler = DDPMScheduler( beta_start=0.00085,...
在diffusers中,我们可以使用StableDiffusionImg2ImgPipeline来实现文生图,具体代码如下所示: import torchfrom diffusers import StableDiffusionImg2ImgPipelinefrom PIL import Image# 加载图生图pipelinemodel_id = "runwayml/stable-diffusion-v1-5"pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torc...
The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-calledv-objective, seehttps://ar...