diff --git a/docs/source/en/api/pipelines/ltx_video.md b/docs/source/en/api/pipelines/ltx_video.md
index ac2b1c95b5b1..211cd3007d1e 100644
--- a/docs/source/en/api/pipelines/ltx_video.md
+++ b/docs/source/en/api/pipelines/ltx_video.md
@@ -61,6 +61,45 @@ pipe = LTXImageToVideoPipeline.from_single_file(
 )
 ```
 
+Loading [LTX GGUF checkpoints](https://huggingface.co/city96/LTX-Video-gguf) are also supported:
+
+```py
+import torch
+from diffusers.utils import export_to_video
+from diffusers import LTXPipeline, LTXVideoTransformer3DModel, GGUFQuantizationConfig
+
+ckpt_path = (
+    "https://huggingface.co/city96/LTX-Video-gguf/blob/main/ltx-video-2b-v0.9-Q3_K_S.gguf"
+)
+transformer = LTXVideoTransformer3DModel.from_single_file(
+    ckpt_path,
+    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
+    torch_dtype=torch.bfloat16,
+)
+pipe = LTXPipeline.from_pretrained(
+    "Lightricks/LTX-Video",
+    transformer=transformer,
+    generator=torch.manual_seed(0),
+    torch_dtype=torch.bfloat16,
+)
+pipe.enable_model_cpu_offload()
+
+prompt = "A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage"
+negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
+
+video = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    width=704,
+    height=480,
+    num_frames=161,
+    num_inference_steps=50,
+).frames[0]
+export_to_video(video, "output_gguf_ltx.mp4", fps=24)
+```
+
+Make sure to read the [documentation on GGUF](../../quantization/gguf) to learn more about our GGUF support.
+
 Refer to [this section](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox#memory-optimization) to learn more about optimizing memory consumption.
 
 ## LTXPipeline
diff --git a/src/diffusers/loaders/single_file_utils.py b/src/diffusers/loaders/single_file_utils.py
index 8b2bf12214cd..f1408c2c409b 100644
--- a/src/diffusers/loaders/single_file_utils.py
+++ b/src/diffusers/loaders/single_file_utils.py
@@ -99,10 +99,11 @@
         "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale",
     ],
     "ltx-video": [
-        (
-            "model.diffusion_model.patchify_proj.weight",
-            "model.diffusion_model.transformer_blocks.27.scale_shift_table",
-        ),
+        "model.diffusion_model.patchify_proj.weight",
+        "model.diffusion_model.transformer_blocks.27.scale_shift_table",
+        "patchify_proj.weight",
+        "transformer_blocks.27.scale_shift_table",
+        "vae.per_channel_statistics.mean-of-means",
     ],
     "autoencoder-dc": "decoder.stages.1.op_list.0.main.conv.conv.bias",
     "autoencoder-dc-sana": "encoder.project_in.conv.bias",
@@ -601,7 +602,7 @@ def infer_diffusers_model_type(checkpoint):
         else:
             model_type = "flux-schnell"
 
-    elif any(all(key in checkpoint for key in key_list) for key_list in CHECKPOINT_KEY_NAMES["ltx-video"]):
+    elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["ltx-video"]):
         model_type = "ltx-video"
 
     elif CHECKPOINT_KEY_NAMES["autoencoder-dc"] in checkpoint:
@@ -2266,9 +2267,7 @@ def swap_scale_shift(weight):
 
 
 def convert_ltx_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):
-    converted_state_dict = {
-        key: checkpoint.pop(key) for key in list(checkpoint.keys()) if "model.diffusion_model." in key
-    }
+    converted_state_dict = {key: checkpoint.pop(key) for key in list(checkpoint.keys()) if "vae" not in key}
 
     TRANSFORMER_KEYS_RENAME_DICT = {
         "model.diffusion_model.": "",