From 45680ae127e48913d669271fb2e9f289bfba904a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=88=B4=E7=A6=8F=E7=94=9F?= <37627578+aarsxx@users.noreply.github.com> Date: Tue, 28 Jan 2025 00:49:28 +0700 Subject: [PATCH] Fix: correct typos and formatting issues in README - Removed duplicate closing tag in the introduction section. - Fixed extra space in the link reference to VLMEvalKit. - Cleaned up redundant hash symbols in code comments. - Added missing periods at the end of sentences for consistency. --- README.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index a77a938..cd590b3 100755 --- a/README.md +++ b/README.md @@ -17,7 +17,6 @@ Homepage - Hugging Face @@ -66,7 +65,7 @@ **2024.11.13**: JanusFlow is released, a new unified model with rectified flow for image generation. See [paper](https://arxiv.org/abs/2411.07975), [demo](https://huggingface.co/spaces/deepseek-ai/JanusFlow-1.3B) and [usage](https://github.com/deepseek-ai/Janus?tab=readme-ov-file#janusflow). -**2024.10.23**: Evaluation code for reproducing the multimodal understanding results from the paper has been added to VLMEvalKit. Please refer to [this link]( https://github.com/open-compass/VLMEvalKit/pull/541). +**2024.10.23**: Evaluation code for reproducing the multimodal understanding results from the paper has been added to VLMEvalKit. Please refer to [this link](https://github.com/open-compass/VLMEvalKit/pull/541). **2024.10.20**: (1) Fix a bug in [tokenizer_config.json](https://huggingface.co/deepseek-ai/Janus-1.3B/blob/main/tokenizer_config.json). The previous version caused classifier-free guidance to not function properly, resulting in relatively poor visual generation quality. (2) Release Gradio demo ([online demo](https://huggingface.co/spaces/deepseek-ai/Janus-1.3B) and [local](#gradio-demo)). @@ -165,10 +164,10 @@ prepare_inputs = vl_chat_processor( conversations=conversation, images=pil_images, force_batchify=True ).to(vl_gpt.device) -# # run image encoder to get the image embeddings +# run image encoder to get the image embeddings inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs) -# # run the model to get the response +# run the model to get the response outputs = vl_gpt.language_model.generate( inputs_embeds=inputs_embeds, attention_mask=prepare_inputs.attention_mask,