From a25818cf7ee2c0ed5c862dff214dc46a30211671 Mon Sep 17 00:00:00 2001 From: atalman Date: Tue, 28 Oct 2025 18:58:24 +0000 Subject: [PATCH] Fix image display on pypi project description section (#166404) Fixes https://github.com/pytorch/pytorch/issues/165559 Pull Request resolved: https://github.com/pytorch/pytorch/pull/166404 Approved by: https://github.com/malfet, https://github.com/Skylion007, https://github.com/Camyll --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 61b4447ddf4..a0c9b54c95a 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -![PyTorch Logo](https://github.com/pytorch/pytorch/blob/9708fcf92db88b80b9010c68662d634434da3106/docs/source/_static/img/pytorch-logo-dark.png) +![PyTorch Logo](https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/pytorch-logo-dark.png) -------------------------------------------------------------------------------- @@ -72,7 +72,7 @@ Elaborating Further: If you use NumPy, then you have used Tensors (a.k.a. ndarray). -![Tensor illustration](https://github.com/pytorch/pytorch/blob/9708fcf92db88b80b9010c68662d634434da3106/docs/source/_static/img/tensor_illustration.png) +![Tensor illustration](https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/tensor_illustration.png) PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the computation by a huge amount. @@ -99,7 +99,7 @@ from several research papers on this topic, as well as current and past work suc While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy research. -![Dynamic graph](https://github.com/pytorch/pytorch/blob/9708fcf92db88b80b9010c68662d634434da3106/docs/source/_static/img/dynamic_graph.gif) +![Dynamic graph](https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/dynamic_graph.gif) ### Python First