Stable Diffusion将文本提示转换为图像。 扩散模型(Diffusion model) Stable Diffusion属于一类称为扩散模型(diffusion model)的深度学习模型。它们是生成模型,这意味着它们的目的是生成类似于它们训练数据的新数据。对于Stable Diffusion来说,数据就是图像。 为什么叫扩散模型?因为它的数学看起来很像物理学中的扩散。让我们...
model,preprocess= clip.load("ViT-L/14") # used by stable diffusion v1model.cuda().eval() input_resolution = model.visual.input_resolution context_length = model.context_length vocab_size = model.vocab_size print("Model parameters:", f"{np.sum([int(np.prod(p.shape)) for p in model...
但是现在的diffusion过程还是太繁琐:每一个time_step都要做一次采样,等我后续做denoise过程去预测噪声,传播梯度的时候,参数 \theta 不仅在这个time_step有,在之前的一系列time_steps中都有,这不是给我计算梯度造成困扰了吗?注意到在diffusion过程中,随着time_step的增加,图片中含有的噪声是越来越多的,那我能不能设...
Learn how to use Stable Diffusion, an advanced open-source deep learning model that generates high-quality images from text descriptions. This tutorial covers the basics of how the model works and step-by-step instructions for running Stable Diffusion online and locally. ...
, // 必要，基础模型名称，关联队列分发或模型切换 "sd_model_checkpoint": "v1-5-pruned-...
We introduce a stochastic equation for the microscopic motion of a tagged particle in the single-file model. This equation provides a compact representatio... A Taloni,MA Lomholt - 《Physical Review E Statistical Nonlinear & Soft Matter Physics》 被引量: 58发表: 2008年 Single-file diffusion of...
Run this command to log in with your HF Hub token if you haven't before (you can skip this step if you prefer to run the model locally, followthisinstead) huggingface-cli login We recommend using the model inhalf-precision (fp16)as it gives almost always the same results as full preci...
GLIDEPipeline to support OpenAI's GLIDE model Grad-TTS for text to audio generation / conditional audio generation We want diffusers to be a toolbox useful for diffusers models in general; if you find yourself limited in any way by the current API, or would like to see additional models, ...
By doing this at every timestep, the model learns to predict specific characteristics like the average value and spread of the data at each point in time. Additionally, by tuning the model to focus on each specific time step, it gets better at estimating these characteristics. This way, it...
(g**2 * step_size)[:, None, None, None] * paddle.randn(x.shape) # 在最后一步中,我们不添加任何噪声 return x_mean sample_img = pc_sampler(model, marginal_prob_std_fn, diffusion_coeff_fn, batch_size=4, num_steps=num_steps, snr=signal_to_noise_ratio, eps=1e-3) display_images...