Add Granite 4.1 Vision (granite4_vision)

docs/source/en/_toctree.yml

@@ -1235,6 +1235,8 @@
         title: GlmOcr
       - local: model_doc/got_ocr2
         title: GOT-OCR2
+      - local: model_doc/granite4_vision
+        title: Granite4Vision
       - local: model_doc/granitevision
         title: GraniteVision
       - local: model_doc/grounding-dino

docs/source/en/model_doc/granite4_vision.md

@@ -0,0 +1,185 @@
+<!--Copyright 2026 IBM and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2026-03-27 and added to Hugging Face Transformers on 2026-05-03.*
+
+<div style="float: right;">
+  <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+  </div>
+</div>
+
+# Granite4Vision
+
+[Granite Vision 4.1](https://huggingface.co/ibm-granite/granite-vision-4.1-4b) is a vision-language model from IBM Research designed for enterprise-grade document data extraction. It specializes in chart extraction (Chart2CSV, Chart2Summary, Chart2Code), table extraction (JSON, HTML, OTSL), and semantic key-value pair extraction.
+
+The model builds on [LLaVA-NeXT](llava_next) with several architectural innovations:
+
+1. **SigLIP2 Vision Encoder** ([`google/siglip2-so400m-patch16-384`](https://huggingface.co/google/siglip2-so400m-patch16-384)): images are tiled into 384x384 patches.
+2. **Window Q-Former Projectors**: compress visual features 4x using windowed cross-attention over 4x4 patch windows into 2x2 tokens.
+3. **DeepStack Feature Injection** with 8 vision-to-LLM injection points:
+   - *LayerDeepstack*: features from 4 vision encoder depths are projected into different early LLM layers.
+   - *SpatialDeepstack*: deepest vision features are split into 4 spatial groups and injected at later LLM layers.
+4. **Language Model**: [Granite 4.1](https://huggingface.co/ibm-granite/granite-4.1-4b-base) (4B params) with LoRA adapters (rank 256) across all self-attention and MLP layers.
+
+The model is delivered as a LoRA adapter on top of the base LLM, enabling single deployments to support both multimodal and text-only workloads. Total parameter count is ~4B.
+
+> [!TIP]
+> This model was contributed by the [IBM Granite Vision Team](https://github.com/ibm-granite).
+
+## Usage Tips
+
+- Set `padding_side="left"` during batched generation for more accurate results.
+
+```py
+processor.tokenizer.padding_side = "left"
+```
+
+- The model supports specialized task tags for document extraction: `<chart2csv>`, `<chart2summary>`, `<chart2code>`, `<tables_html>`, `<tables_otsl>`, `<tables_json>`. Pass these as the text prompt along with a document image.
+
+- For key-value pair extraction, provide a JSON schema describing the fields to extract. The model returns structured JSON matching the schema.
+
+The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.
+
+<hfoptions id="usage">
+
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+pipe = pipeline(
+    task="image-text-to-text",
+    model="ibm-granite/granite-vision-4.1-4b",
+)
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+pipe(text=messages, max_new_tokens=100, return_full_text=False)
+```
+
+</hfoption>
+
+<hfoption id="AutoModel">
+
+```python
+import torch
+from transformers import AutoProcessor, AutoModelForImageTextToText
+
+model_id = "ibm-granite/granite-vision-4.1-4b"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(model_id)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    },
+]
+inputs = processor.apply_chat_template(
+    conversation,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+output = model.generate(**inputs, max_new_tokens=100)
+print(processor.decode(output[0], skip_special_tokens=True))
+```
+
+</hfoption>
+
+</hfoptions>
+
+Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
+
+The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.
+
+```python
+import torch
+from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig
+
+quant_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_quant_type="nf4",
+)
+
+model_id = "ibm-granite/granite-vision-4.1-4b"
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id, quantization_config=quant_config, device_map="auto"
+)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    },
+]
+inputs = processor.apply_chat_template(
+    conversation,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+output = model.generate(**inputs, max_new_tokens=100)
+print(processor.decode(output[0], skip_special_tokens=True))
+```
+
+## Granite4VisionConfig
+
+[[autodoc]] Granite4VisionConfig
+
+## Granite4VisionTextConfig
+
+[[autodoc]] Granite4VisionTextConfig
+
+## Granite4VisionProcessor
+
+[[autodoc]] Granite4VisionProcessor
+    - __call__
+
+## Granite4VisionModel
+
+[[autodoc]] Granite4VisionModel
+
+## Granite4VisionTextModel
+
+[[autodoc]] Granite4VisionTextModel
+
+## Granite4VisionForConditionalGeneration
+
+[[autodoc]] Granite4VisionForConditionalGeneration
+    - forward
+    - get_image_features

src/transformers/models/__init__.py

@@ -178,6 +178,7 @@
     from .gpt_sw3 import *
     from .gptj import *
     from .granite import *
+    from .granite4_vision import *
     from .granite_speech import *
     from .granite_speech_plus import *
     from .granitemoe import *

src/transformers/models/auto/auto_mappings.py

@@ -235,6 +235,7 @@
         ("gpt_oss", "GptOssConfig"),
         ("gptj", "GPTJConfig"),
         ("granite", "GraniteConfig"),
+        ("granite4_vision", "Granite4VisionConfig"),
         ("granite_speech", "GraniteSpeechConfig"),
         ("granite_speech_encoder", "GraniteSpeechEncoderConfig"),
         ("granite_speech_plus", "GraniteSpeechPlusConfig"),
@@ -892,6 +893,7 @@
         ("glm_image", {"pil": "GlmImageImageProcessorPil", "torchvision": "GlmImageImageProcessor"}),
         ("glpn", {"pil": "GLPNImageProcessorPil", "torchvision": "GLPNImageProcessor"}),
         ("got_ocr2", {"pil": "GotOcr2ImageProcessorPil", "torchvision": "GotOcr2ImageProcessor"}),
+        ("granite4_vision", {"pil": "LlavaNextImageProcessorPil", "torchvision": "LlavaNextImageProcessor"}),
         ("grounding-dino", {"pil": "GroundingDinoImageProcessorPil", "torchvision": "GroundingDinoImageProcessor"}),
         ("idefics", {"pil": "IdeficsImageProcessorPil", "torchvision": "IdeficsImageProcessor"}),
         ("idefics2", {"pil": "Idefics2ImageProcessorPil", "torchvision": "Idefics2ImageProcessor"}),

src/transformers/models/auto/image_processing_auto.py

@@ -88,6 +88,7 @@
             ("focalnet", {"torchvision": "BitImageProcessor", "pil": "BitImageProcessorPil"}),
             ("gemma3n", {"torchvision": "SiglipImageProcessor", "pil": "SiglipImageProcessorPil"}),
             ("git", {"torchvision": "CLIPImageProcessor", "pil": "CLIPImageProcessorPil"}),
+            ("granite4_vision", {"torchvision": "LlavaNextImageProcessor", "pil": "LlavaNextImageProcessorPil"}),
             ("groupvit", {"torchvision": "CLIPImageProcessor", "pil": "CLIPImageProcessorPil"}),
             ("hiera", {"torchvision": "BitImageProcessor", "pil": "BitImageProcessorPil"}),
             ("ijepa", {"torchvision": "ViTImageProcessor", "pil": "ViTImageProcessorPil"}),

src/transformers/models/auto/modeling_auto.py

@@ -211,6 +211,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("gpt_oss", "GptOssModel"),
         ("gptj", "GPTJModel"),
         ("granite", "GraniteModel"),
+        ("granite4_vision", "Granite4VisionModel"),
         ("granite_speech", "GraniteSpeechForConditionalGeneration"),
         ("granitemoe", "GraniteMoeModel"),
         ("granitemoehybrid", "GraniteMoeHybridModel"),
@@ -995,6 +996,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("glm4v_moe", "Glm4vMoeForConditionalGeneration"),
         ("glm_ocr", "GlmOcrForConditionalGeneration"),
         ("got_ocr2", "GotOcr2ForConditionalGeneration"),
+        ("granite4_vision", "Granite4VisionForConditionalGeneration"),
         ("idefics", "IdeficsForVisionText2Text"),
         ("idefics2", "Idefics2ForConditionalGeneration"),
         ("idefics3", "Idefics3ForConditionalGeneration"),

src/transformers/models/auto/processing_auto.py

@@ -89,6 +89,7 @@
             ("glm_image", "Glm4vProcessor"),
             ("glmasr", "GlmAsrProcessor"),
             ("got_ocr2", "GotOcr2Processor"),
+            ("granite4_vision", "Granite4VisionProcessor"),
             ("granite_speech", "GraniteSpeechProcessor"),
             ("granite_speech_plus", "GraniteSpeechProcessor"),
             ("grounding-dino", "GroundingDinoProcessor"),

src/transformers/models/granite4_vision/__init__.py

@@ -0,0 +1,28 @@
+# Copyright 2025 IBM. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_granite4_vision import *
+    from .modeling_granite4_vision import *
+    from .processing_granite4_vision import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)