Improve docs

docs/source/en/_toctree.yml

@@ -520,12 +520,8 @@
         title: QDQBert
       - local: model_doc/qwen2
         title: Qwen2
-      - local: model_doc/qwen2_audio
-        title: Qwen2Audio
       - local: model_doc/qwen2_moe
         title: Qwen2MoE
-      - local: model_doc/qwen2_vl
-        title: Qwen2VL
       - local: model_doc/rag
         title: RAG
       - local: model_doc/realm
@@ -860,6 +856,10 @@
         title: Perceiver
       - local: model_doc/pix2struct
         title: Pix2Struct
+      - local: model_doc/qwen2_audio
+        title: Qwen2Audio
+      - local: model_doc/qwen2_vl
+        title: Qwen2VL
       - local: model_doc/sam
         title: Segment Anything
       - local: model_doc/siglip

docs/source/en/model_doc/llava.md

@@ -40,7 +40,9 @@ The original code can be found [here](https://github.com/haotian-liu/LLaVA/tree/
 
 - Note the model has not been explicitly trained to process multiple images in the same prompt, although this is technically possible, you may experience inaccurate results.
 
-- For better results, we recommend users to use the processor's `apply_chat_template()` method to format your prompt correctly. For that you need to construct a conversation history, passing in a plain string will not format your prompt. Each message in the conversation history for chat templates is a dictionary with keys "role" and "content". The "content" should be a list of dictionaries, for "text" and "image" modalities, as follows:
+### Single image inference
+
+For best results, we recommend users to use the processor's `apply_chat_template()` method to format your prompt correctly. For that you need to construct a conversation history, passing in a plain string will not format your prompt. Each message in the conversation history for chat templates is a dictionary with keys "role" and "content". The "content" should be a list of dictionaries, for "text" and "image" modalities, as follows:
 
 ```python
 from transformers import AutoProcessor
@@ -75,6 +77,60 @@ print(text_prompt)
 >>> "USER: <image>\n<What’s shown in this image? ASSISTANT: This image shows a red stop sign.</s>USER: Describe the image in more details. ASSISTANT:"
 ```
 
+### Batched inference
+
+LLaVa also supports batched inference. Here is how you can do it:
+
+```python
+import requests
+from PIL import Image
+import torch
+from transformers import AutoProcessor, LLavaForConditionalGeneration
+
+# Load the model in half-precision
+model = LLavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf", torch_dtype=torch.float16, device_map="auto")
+processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
+
+# Get two different images
+url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+image_stop = Image.open(requests.get(url, stream=True).raw)
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image_cats = Image.open(requests.get(url, stream=True).raw)
+
+# Prepare a batch of two prompts
+conversation_1 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "What is shown in this image?"},
+            ],
+    },
+]
+
+conversation_2 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "What is shown in this image?"},
+            ],
+    },
+]
+
+prompt_1 = processor.apply_chat_template(conversation_1, add_generation_prompt=True)
+prompt_2 = processor.apply_chat_template(conversation_2, add_generation_prompt=True)
+prompts = [prompt_1, prompt_2]
+
+# We can simply feed images in the order they have to be used in the text prompt
+inputs = processor(images=[image_stop, image_cats, image_snowman], text=prompts, padding=True, return_tensors="pt").to(model.device, torch.float16)
+
+# Generate
+generate_ids = model.generate(**inputs, max_new_tokens=30)
+processor.batch_decode(generate_ids, skip_special_tokens=True)
+```
+
 - If you want to construct a chat prompt yourself, below is a list of prompt formats accepted by each llava checkpoint:
 
 [llava-interleave models](https://huggingface.co/collections/llava-hf/llava-interleave-668e19a97da0036aad4a2f19) requires the following format:
@@ -99,7 +155,6 @@ For multiple turns conversation:
 "USER: <image>\n<prompt1> ASSISTANT: <answer1></s>USER: <prompt2> ASSISTANT: <answer2></s>USER: <prompt3> ASSISTANT:"
 ```
 
-
 ### Using Flash Attention 2
 
 Flash Attention 2 is an even faster, optimized version of the previous optimization, please refer to the [Flash Attention 2 section of performance docs](https://huggingface.co/docs/transformers/perf_infer_gpu_one).

docs/source/en/model_doc/llava_onevision.md

@@ -14,13 +14,13 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# LLaVA-Onevision
+# LLaVA-OneVision
 
 ## Overview
 
-The LLaVA-Onevision model was proposed in [LLaVA-OneVision: Easy Visual Task Transfer](https://arxiv.org/abs/2408.03326) by <Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li
+The LLaVA-OneVision model was proposed in [LLaVA-OneVision: Easy Visual Task Transfer](https://arxiv.org/abs/2408.03326) by <Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li
 
-LLaVA-Onevision is a Vision-Language Model that can generate text conditioned on one or several images/videos. The model consists of SigLIP vision encoder and a Qwen2 language backbone. The images are processed with anyres-9 technique where the image is split into 9 patches to better process high resolution images and capture as much details as possible. However, videos are pooled to a total sequence length of 196 tokens each frame for more memory efficient computation. LLaVA-Onevision is available in three sizes: 0.5B, 7B and 72B and achieves remarkable performance on benchmark evaluations.
+LLaVA-OneVision is a Vision-Language Model that can generate text conditioned on one or several images/videos. The model consists of SigLIP vision encoder and a Qwen2 language backbone. The images are processed with anyres-9 technique where the image is split into 9 patches to better process high resolution images and capture as much details as possible. However, videos are pooled to a total sequence length of 196 tokens each frame for more memory efficient computation. LLaVA-OneVision is available in three sizes: 0.5B, 7B and 72B and achieves remarkable performance on benchmark evaluations.
 
 The abstract from the paper is the following:
 
@@ -32,19 +32,18 @@ yielding new emerging capabilities. In particular, strong video understanding an
 cross-scenario capabilities are demonstrated through task transfer from images to
 videos.*
 
-
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/llava-ov-acrhitecture.png"
 alt="drawing" width="600"/>
 
-<small> LLaVA=Onevision architecture. Taken from the <a href="https://arxiv.org/abs/2408.03326">original paper.</a> </small>
+<small> LLaVA-OneVision architecture. Taken from the <a href="https://arxiv.org/abs/2408.03326">original paper.</a> </small>
 
 Tips:
 
 - We advise users to use `padding_side="left"` when computing batched generation as it leads to more accurate results. Simply make sure to call `processor.tokenizer.padding_side = "left"` before generating.
 
 <Tip warning={true}>
 
-- Llava-Onevision uses different number of patches for images and thus has to pad the inputs inside modeling code, aside from the padding done when processing the inputs. The default setting is "left-padding" if model is in `eval()` mode, otherwise "right-padding".
+- Llava-OneVision uses different number of patches for images and thus has to pad the inputs inside modeling code, aside from the padding done when processing the inputs. The default setting is "left-padding" if model is in `eval()` mode, otherwise "right-padding".
 
 </Tip>
 
@@ -129,7 +128,7 @@ print(processor.decode(output[0], skip_special_tokens=True))
 
 ### Multi image inference
 
-LLaVa-Onevision can perform inference with multiple images as input, where images either belong to the same prompt or different prompts (in batched inference). For that you have to use checkpoints with an "ov" suffix. Here is how you can do it:
+LLaVa-OneVision can perform inference with multiple images as input, where images either belong to the same prompt or different prompts (in batched inference). For that you have to use checkpoints with an "ov" suffix. Here is how you can do it:
 
 ```python
 import requests
@@ -200,7 +199,7 @@ processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokeniza
 
 ### Video inference
 
-LLaVa-Onevision also can perform inference with videos as input, where video frames are treated as multiple images. Here is how you can do it:
+LLaVa-OneVision also can perform inference with videos as input, where video frames are treated as multiple images. Here is how you can do it:
 
 ```python
 import av

docs/source/en/model_doc/qwen2_vl.md

@@ -14,17 +14,22 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# Qwen2_VL
-
+# Qwen2-VL
 
 ## Overview
 
-The [Qwen2_VL](https://qwenlm.github.io/blog/qwen2-vl/) is a major update to our [Qwen-VL](https://arxiv.org/pdf/2308.12966) model from the Qwen team. 
+The [Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/) model is a major update to [Qwen-VL](https://arxiv.org/pdf/2308.12966) from the Qwen team at Alibaba Research. 
 
 The abstract from the blog is the following:
 
 *This blog introduces Qwen2-VL, an advanced version of the Qwen-VL model that has undergone significant enhancements over the past year. Key improvements include enhanced image comprehension, advanced video understanding, integrated visual agent functionality, and expanded multilingual support. The model architecture has been optimized for handling arbitrary image resolutions through Naive Dynamic Resolution support and utilizes Multimodal Rotary Position Embedding (M-ROPE) to effectively process both 1D textual and multi-dimensional visual data. This updated model demonstrates competitive performance against leading AI systems like GPT-4o and Claude 3.5 Sonnet in vision-related tasks and ranks highly among open-source models in text capabilities. These advancements make Qwen2-VL a versatile tool for various applications requiring robust multimodal processing and reasoning abilities.*
 
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/qwen2_vl_architecture.jpeg"
+alt="drawing" width="600"/>
+
+<small> Qwen2-VL architecture. Taken from the <a href="https://qwenlm.github.io/blog/qwen2-vl/">blog post.</a> </small>
+
+This model was contributed by [simonJJJ](https://huggingface.co/simonJJJ).
 
 ## Usage example
 
@@ -78,8 +83,6 @@ generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(in
 output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
 print(output_text)
 
-
-
 # Video
 def fetch_video(ele: Dict, nframe_factor=2):
     if isinstance(ele['video'], str):
@@ -130,16 +133,13 @@ output_ids = model.generate(**inputs, max_new_tokens=128)
 generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
 output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
 print(output_text)
-
 ```
 
-
 ### Batch Mixed Media Inference
 
 The model can batch inputs composed of mixed samples of various types such as images, videos, and text. Here is an example.
 
 ```python
-
 image1 = Image.open("/path/to/image1.jpg")
 image2 = Image.open("/path/to/image2.jpg")
 image3 = Image.open("/path/to/image3.jpg")
@@ -217,28 +217,30 @@ print(output_text)
 
 ### Usage Tips
 
-#### Image Resolution for performance boost
+#### Image Resolution trade-off
 
-The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs.
+The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but one can reduce the resolution in case of limited GPU RAM, as follows:
 
 ```python
+min_pixels = 256*28*28
+max_pixels = 1024*28*28 
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
+```
+This ensures each image gets encoded using a number between 256-1024 tokens.
 
+Alternatively, higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs.
+
+```python
 min_pixels = 224*224
 max_pixels = 2048*2048
 processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
-
 ```
 
-
-
 #### Multiple Image Inputs
 
 By default, images and video content are directly included in the conversation. When handling multiple images, it's helpful to add labels to the images and videos for better reference. Users can control this behavior with the following settings:
 
-
-
 ```python
-
 conversation = [
     {
         "role": "user",
@@ -304,7 +306,6 @@ model = Qwen2VLForConditionalGeneration.from_pretrained(
 )
 ```
 
-
 ## Qwen2VLConfig
 
 [[autodoc]] Qwen2VLConfig