diff --git a/backend/python/diffusers/backend.py b/backend/python/diffusers/backend.py
index cb2e073ac..f26a94b57 100755
--- a/backend/python/diffusers/backend.py
+++ b/backend/python/diffusers/backend.py
@@ -41,6 +41,14 @@ from optimum.quanto import freeze, qfloat8, quantize
 from transformers import T5EncoderModel
 from safetensors.torch import load_file
 
+# Import LTX-2 specific utilities
+try:
+    from diffusers.pipelines.ltx2.export_utils import encode_video as ltx2_encode_video
+    LTX2_AVAILABLE = True
+except ImportError:
+    LTX2_AVAILABLE = False
+    ltx2_encode_video = None
+
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
 COMPEL = os.environ.get("COMPEL", "0") == "1"
 XPU = os.environ.get("XPU", "0") == "1"
@@ -290,6 +298,20 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                 pipe.enable_model_cpu_offload()
             return pipe
 
+        # LTX2ImageToVideoPipeline - needs img2vid flag, CPU offload, and special handling
+        if pipeline_type == "LTX2ImageToVideoPipeline":
+            self.img2vid = True
+            self.ltx2_pipeline = True
+            pipe = load_diffusers_pipeline(
+                class_name="LTX2ImageToVideoPipeline",
+                model_id=request.Model,
+                torch_dtype=torchType,
+                variant=variant
+            )
+            if not DISABLE_CPU_OFFLOAD:
+                pipe.enable_model_cpu_offload()
+            return pipe
+
         # ================================================================
         # Dynamic pipeline loading - the default path for most pipelines
         # Uses the dynamic loader to instantiate any pipeline by class name
@@ -404,6 +426,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             fromSingleFile = request.Model.startswith("http") or request.Model.startswith("/") or local
             self.img2vid = False
             self.txt2vid = False
+            self.ltx2_pipeline = False
 
             # Load pipeline using dynamic loader
             # Special cases that require custom initialization are handled first
@@ -686,7 +709,44 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             print(f"Generating video with {kwargs=}", file=sys.stderr)
 
             # Generate video frames based on pipeline type
-            if self.PipelineType == "WanPipeline":
+            if self.ltx2_pipeline or self.PipelineType == "LTX2ImageToVideoPipeline":
+                # LTX-2 image-to-video generation with audio
+                if not LTX2_AVAILABLE:
+                    return backend_pb2.Result(success=False, message="LTX-2 pipeline requires diffusers.pipelines.ltx2.export_utils")
+                
+                # LTX-2 uses 'image' parameter instead of 'start_image'
+                if request.start_image:
+                    image = load_image(request.start_image)
+                    kwargs["image"] = image
+                    # Remove start_image if it was added
+                    kwargs.pop("start_image", None)
+                
+                # LTX-2 uses 'frame_rate' instead of 'fps'
+                frame_rate = float(fps)
+                kwargs["frame_rate"] = frame_rate
+                
+                # LTX-2 requires output_type="np" and return_dict=False
+                kwargs["output_type"] = "np"
+                kwargs["return_dict"] = False
+                
+                # Generate video and audio
+                video, audio = self.pipe(**kwargs)
+                
+                # Convert video to uint8 format
+                video = (video * 255).round().astype("uint8")
+                video = torch.from_numpy(video)
+                
+                # Use LTX-2's encode_video function which handles audio
+                ltx2_encode_video(
+                    video[0],
+                    fps=frame_rate,
+                    audio=audio[0].float().cpu(),
+                    audio_sample_rate=self.pipe.vocoder.config.output_sampling_rate,
+                    output_path=request.dst,
+                )
+                
+                return backend_pb2.Result(message="Video generated successfully", success=True)
+            elif self.PipelineType == "WanPipeline":
                 # WAN2.2 text-to-video generation
                 output = self.pipe(**kwargs)
                 frames = output.frames[0]  # WAN2.2 returns frames in this format
@@ -727,7 +787,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             else:
                 return backend_pb2.Result(success=False, message=f"Pipeline {self.PipelineType} does not support video generation")
 
-            # Export video
+            # Export video (for non-LTX-2 pipelines)
             export_to_video(frames, request.dst, fps=fps)
             
             return backend_pb2.Result(message="Video generated successfully", success=True)
diff --git a/gallery/index.yaml b/gallery/index.yaml
index da8c0cd0f..842633e7f 100644
--- a/gallery/index.yaml
+++ b/gallery/index.yaml
@@ -1247,6 +1247,63 @@
       cuda: true
       pipeline_type: QwenImageEditPipeline
       enable_parameters: num_inference_steps,image
+- &ltx2
+  name: "ltx-2"
+  url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
+  urls:
+    - https://huggingface.co/Lightricks/LTX-2
+  license: ltx-2-community-license-agreement
+  tags:
+    - diffusers
+    - gpu
+    - image-to-video
+    - video-generation
+    - audio-video
+  description: |
+    **LTX-2** is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model. It brings together the core building blocks of modern video generation, with open weights and a focus on practical, local execution.
+
+    **Key Features:**
+    - **Joint Audio-Video Generation**: Generates synchronized video and audio in a single model
+    - **Image-to-Video**: Converts static images into dynamic videos with matching audio
+    - **High Quality**: Produces realistic video with natural motion and synchronized audio
+    - **Open Weights**: Available under the LTX-2 Community License Agreement
+
+    **Model Details:**
+    - **Model Type**: Diffusion-based audio-video foundation model
+    - **Architecture**: DiT (Diffusion Transformer) based
+    - **Developed by**: Lightricks
+    - **Paper**: [LTX-2: Efficient Joint Audio-Visual Foundation Model](https://arxiv.org/abs/2601.03233)
+
+    **Usage Tips:**
+    - Width & height settings must be divisible by 32
+    - Frame count must be divisible by 8 + 1 (e.g., 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, 97, 105, 113, 121)
+    - Recommended settings: width=768, height=512, num_frames=121, frame_rate=24.0
+    - For best results, use detailed prompts describing motion and scene dynamics
+
+    **Limitations:**
+    - This model is not intended or able to provide factual information
+    - Prompt following is heavily influenced by the prompting-style
+    - When generating audio without speech, the audio may be of lower quality
+
+    **Citation:**
+    ```bibtex
+    @article{hacohen2025ltx2,
+      title={LTX-2: Efficient Joint Audio-Visual Foundation Model},
+      author={HaCohen, Yoav and Brazowski, Benny and Chiprut, Nisan and others},
+      journal={arXiv preprint arXiv:2601.03233},
+      year={2025}
+    }
+    ```
+  overrides:
+    backend: diffusers
+    low_vram: true
+    parameters:
+      model: Lightricks/LTX-2
+    diffusers:
+      cuda: true
+      pipeline_type: LTX2ImageToVideoPipeline
+    options:
+      - torch_dtype:bf16
 - &gptoss
   name: "gpt-oss-20b"
   url: "github:mudler/LocalAI/gallery/harmony.yaml@master"