diff --git a/README.md b/README.md
index 3ad28e4..e7afd7b 100644
--- a/README.md
+++ b/README.md
@@ -6,19 +6,32 @@ Timestep Embedding Aware Cache (TeaCache) is a training-free caching approach th
 TeaCache has now been integrated into ComfyUI and is compatible with the ComfyUI native nodes. ComfyUI-TeaCache is easy to use, simply connect the TeaCache node with the ComfyUI native nodes for seamless usage.
 
 ## Updates
+- Jan 10 2025: ComfyUI-TeaCache supports LTX-Video:
+    - It can achieve a 1.4x lossless speedup and a 1.7x speedup without much visual quality degradation.
+    - Support Text to Video and Image to Video!
 - Jan 9 2025: ComfyUI-TeaCache supports HunyuanVideo:
-    - It can achieve a 1.6x lossless speedup and a 2x speedup without much visual quality degradation, which are consistent with the original [TeaCache4HunyuanVideo](https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4HunyuanVideo).
+    - It can achieve a 1.6x lossless speedup and a 2x speedup without much visual quality degradation.
 - Jan 8 2025: ComfyUI-TeaCache supports FLUX:
-    - It can achieve a 1.4x lossless speedup and a 2x speedup without much visual quality degradation, which are consistent with the original [TeaCache4FLUX](https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4FLUX).
+    - It can achieve a 1.4x lossless speedup and a 2x speedup without much visual quality degradation.
     - Support FLUX LoRA!
     - Support FLUX ControlNet!
 
 ## Installation
+Installation via ComfyUI-Manager is preferred. Simply search for ComfyUI-TeaCache in the list of nodes and click install.
+### Manual installation
 1. Go to comfyUI custom_nodes folder, `ComfyUI/custom_nodes/`
 2. git clone https://github.com/welltop-cn/ComfyUI-TeaCache.git
 
+## Recommended settings
+The following table gives the recommended rel_l1_thresh ​for different models:
+
+|                       |            FLUX              |      HunyuanVideo     |       LTX-Video       |
+|:---------------------:|:----------------------------:|:---------------------:|:---------------------:|
+|    rel_l1_thresh      |             0.4              |         0.15          |        0.06           |
+|       speedup         |             ~2x              |         ~2x           |        ~1.7x          |
+
 ## Usage
-The demo workflow is placed in examples folder.
+The demo workflows are placed in examples folder.
 
 ## Demo
 - <p><strong>FLUX</strong></p>
@@ -27,6 +40,9 @@ https://github.com/user-attachments/assets/e977cf34-f7d0-4b25-a2e3-10fd62ebfe30
 - <p><strong>HunyuanVideo</strong></p>
 https://github.com/user-attachments/assets/4d8e9f12-2c54-40c5-a992-c2cecbde019a
 
+- <p><strong>LTX-Video</strong></p>
+https://github.com/user-attachments/assets/19e63dd8-ecdf-418c-8ec2-b9b9dcf9a655
+
 ## Result comparison
 - <p><strong>FLUX</strong></p>
 ![](./assets/compare_flux.png)
@@ -34,9 +50,8 @@ https://github.com/user-attachments/assets/4d8e9f12-2c54-40c5-a992-c2cecbde019a
 - <p><strong>HunyuanVideo</strong></p>
 https://github.com/user-attachments/assets/b3aca64d-c2ae-440c-a362-f3a7b6c633e0
 
-## Roadmap
-
-- [ ] Support LTX-Video
+- <p><strong>LTX-Video</strong></p>
+https://github.com/user-attachments/assets/8fce9b48-2243-46f1-b411-80e4a53f6f7d
 
 ## Acknowledgments
 Thanks to TeaCache repo owner [ali-vilab/TeaCache: Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model](https://github.com/ali-vilab/TeaCache)
diff --git a/assets/teacache_ltx_video.png b/assets/teacache_ltx_video.png
new file mode 100644
index 0000000..553d3e6
Binary files /dev/null and b/assets/teacache_ltx_video.png differ
diff --git a/examples/teacache_ltx_video.json b/examples/teacache_ltx_video.json
new file mode 100644
index 0000000..5ee7d68
--- /dev/null
+++ b/examples/teacache_ltx_video.json
@@ -0,0 +1,713 @@
+{
+  "last_node_id": 88,
+  "last_link_id": 185,
+  "nodes": [
+    {
+      "id": 71,
+      "type": "LTXVScheduler",
+      "pos": [
+        856,
+        531
+      ],
+      "size": [
+        315,
+        154
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "latent",
+          "type": "LATENT",
+          "link": 168,
+          "shape": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "SIGMAS",
+          "type": "SIGMAS",
+          "links": [
+            182
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LTXVScheduler"
+      },
+      "widgets_values": [
+        30,
+        2.05,
+        0.95,
+        true,
+        0.1
+      ]
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        420,
+        190
+      ],
+      "size": [
+        422.84503173828125,
+        164.31304931640625
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 74
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            169
+          ],
+          "slot_index": 0
+        }
+      ],
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "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.",
+        true
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        420,
+        390
+      ],
+      "size": [
+        425.27801513671875,
+        180.6060791015625
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 75
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            170
+          ],
+          "slot_index": 0
+        }
+      ],
+      "title": "CLIP Text Encode (Negative Prompt)",
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "low quality, worst quality, deformed, distorted, disfigured, motion smear, motion artifacts, fused fingers, bad anatomy, weird hand, ugly",
+        true
+      ],
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 73,
+      "type": "KSamplerSelect",
+      "pos": [
+        860,
+        420
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "SAMPLER",
+          "type": "SAMPLER",
+          "links": [
+            172
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSamplerSelect"
+      },
+      "widgets_values": [
+        "euler"
+      ]
+    },
+    {
+      "id": 76,
+      "type": "Note",
+      "pos": [
+        40,
+        350
+      ],
+      "size": [
+        360,
+        200
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "This model needs long descriptive prompts, if the prompt is too short the quality will suffer greatly."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 70,
+      "type": "EmptyLTXVLatentVideo",
+      "pos": [
+        860,
+        240
+      ],
+      "size": [
+        315,
+        130
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            168,
+            175
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLTXVLatentVideo"
+      },
+      "widgets_values": [
+        768,
+        768,
+        97,
+        1
+      ]
+    },
+    {
+      "id": 69,
+      "type": "LTXVConditioning",
+      "pos": [
+        920,
+        60
+      ],
+      "size": [
+        223.8660125732422,
+        78
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 169
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 170
+        }
+      ],
+      "outputs": [
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "links": [
+            166
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [
+            167
+          ],
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LTXVConditioning"
+      },
+      "widgets_values": [
+        25
+      ]
+    },
+    {
+      "id": 38,
+      "type": "CLIPLoader",
+      "pos": [
+        60,
+        190
+      ],
+      "size": [
+        315,
+        82
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            74,
+            75
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPLoader"
+      },
+      "widgets_values": [
+        "t5xxl_fp16.safetensors",
+        "ltxv"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
+      "pos": [
+        1600,
+        30
+      ],
+      "size": [
+        210,
+        46
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 171
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 87
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            185
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 86,
+      "type": "VHS_VideoCombine",
+      "pos": [
+        1890.7164306640625,
+        30.7105770111084
+      ],
+      "size": [
+        312.7515869140625,
+        616.7515869140625
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 185
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null,
+          "shape": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 24,
+        "loop_count": 0,
+        "filename_prefix": "ltxv",
+        "format": "video/h264-mp4",
+        "pix_fmt": "yuv420p",
+        "crf": 19,
+        "save_metadata": true,
+        "pingpong": false,
+        "save_output": true,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "filename": "ltxv_00032.mp4",
+            "subfolder": "",
+            "type": "output",
+            "format": "video/h264-mp4",
+            "frame_rate": 24
+          },
+          "muted": false
+        }
+      }
+    },
+    {
+      "id": 72,
+      "type": "SamplerCustom",
+      "pos": [
+        1206.866943359375,
+        26.604873657226562
+      ],
+      "size": [
+        355.20001220703125,
+        230
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 184
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 166
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 167
+        },
+        {
+          "name": "sampler",
+          "type": "SAMPLER",
+          "link": 172
+        },
+        {
+          "name": "sigmas",
+          "type": "SIGMAS",
+          "link": 182
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 175
+        }
+      ],
+      "outputs": [
+        {
+          "name": "output",
+          "type": "LATENT",
+          "links": [
+            171
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "denoised_output",
+          "type": "LATENT",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SamplerCustom"
+      },
+      "widgets_values": [
+        true,
+        11905454606274,
+        "fixed",
+        3
+      ]
+    },
+    {
+      "id": 85,
+      "type": "TeaCacheForVidGen",
+      "pos": [
+        864.0098266601562,
+        -156.047119140625
+      ],
+      "size": [
+        315,
+        130
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 183
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            184
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "TeaCacheForVidGen"
+      },
+      "widgets_values": [
+        true,
+        "ltxv",
+        0.06,
+        30
+      ]
+    },
+    {
+      "id": 44,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        520.5762329101562,
+        17.9000244140625
+      ],
+      "size": [
+        315,
+        98
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            183
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": null
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            87
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "ltx-video-2b-v0.9.1.safetensors"
+      ]
+    }
+  ],
+  "links": [
+    [
+      74,
+      38,
+      0,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
+      75,
+      38,
+      0,
+      7,
+      0,
+      "CLIP"
+    ],
+    [
+      87,
+      44,
+      2,
+      8,
+      1,
+      "VAE"
+    ],
+    [
+      166,
+      69,
+      0,
+      72,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      167,
+      69,
+      1,
+      72,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      168,
+      70,
+      0,
+      71,
+      0,
+      "LATENT"
+    ],
+    [
+      169,
+      6,
+      0,
+      69,
+      0,
+      "CONDITIONING"
+    ],
+    [
+      170,
+      7,
+      0,
+      69,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      171,
+      72,
+      0,
+      8,
+      0,
+      "LATENT"
+    ],
+    [
+      172,
+      73,
+      0,
+      72,
+      3,
+      "SAMPLER"
+    ],
+    [
+      175,
+      70,
+      0,
+      72,
+      5,
+      "LATENT"
+    ],
+    [
+      182,
+      71,
+      0,
+      72,
+      4,
+      "SIGMAS"
+    ],
+    [
+      183,
+      44,
+      0,
+      85,
+      0,
+      "MODEL"
+    ],
+    [
+      184,
+      85,
+      0,
+      72,
+      0,
+      "MODEL"
+    ],
+    [
+      185,
+      8,
+      0,
+      86,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.7513148009015777,
+      "offset": [
+        -12.24569670436394,
+        293.53734923538843
+      ]
+    }
+  },
+  "version": 0.4
+}
\ No newline at end of file
diff --git a/nodes.py b/nodes.py
index 5fb4787..e2f7288 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1,12 +1,16 @@
+import math
 import torch
 import numpy as np
 
-from comfy.ldm.flux.model import Flux
-from comfy.ldm.hunyuan_video.model import HunyuanVideo
-from comfy.ldm.flux.layers import timestep_embedding
-
 from torch import Tensor
 
+from comfy.ldm.flux.model import Flux
+from comfy.ldm.flux.layers import timestep_embedding
+from comfy.ldm.hunyuan_video.model import HunyuanVideo
+from comfy.ldm.lightricks.model import LTXVModel, precompute_freqs_cis
+from comfy.ldm.common_dit import rms_norm
+
+
 def teacache_flux_forward(
         self,
         img: Tensor,
@@ -266,6 +270,162 @@ def teacache_hunyuanvideo_forward(
         img = img.reshape(initial_shape)
         return img
 
+def teacache_ltxvmodel_forward(
+        self,
+        x,
+        timestep,
+        context,
+        attention_mask,
+        frame_rate=25,
+        guiding_latent=None,
+        guiding_latent_noise_scale=0,
+        transformer_options={},
+        **kwargs
+    ):
+        patches_replace = transformer_options.get("patches_replace", {})
+
+        indices_grid = self.patchifier.get_grid(
+            orig_num_frames=x.shape[2],
+            orig_height=x.shape[3],
+            orig_width=x.shape[4],
+            batch_size=x.shape[0],
+            scale_grid=((1 / frame_rate) * 8, 32, 32),
+            device=x.device,
+        )
+
+        if guiding_latent is not None:
+            ts = torch.ones([x.shape[0], 1, x.shape[2], x.shape[3], x.shape[4]], device=x.device, dtype=x.dtype)
+            input_ts = timestep.view([timestep.shape[0]] + [1] * (x.ndim - 1))
+            ts *= input_ts
+            ts[:, :, 0] = guiding_latent_noise_scale * (input_ts[:, :, 0] ** 2)
+            timestep = self.patchifier.patchify(ts)
+            input_x = x.clone()
+            x[:, :, 0] = guiding_latent[:, :, 0]
+            if guiding_latent_noise_scale > 0:
+                if self.generator is None:
+                    self.generator = torch.Generator(device=x.device).manual_seed(42)
+                elif self.generator.device != x.device:
+                    self.generator = torch.Generator(device=x.device).set_state(self.generator.get_state())
+
+                noise_shape = [guiding_latent.shape[0], guiding_latent.shape[1], 1, guiding_latent.shape[3], guiding_latent.shape[4]]
+                scale = guiding_latent_noise_scale * (input_ts ** 2)
+                guiding_noise = scale * torch.randn(size=noise_shape, device=x.device, generator=self.generator)
+
+                x[:, :, 0] = guiding_noise[:, :, 0] + x[:, :, 0] *  (1.0 - scale[:, :, 0])
+
+
+        orig_shape = list(x.shape)
+
+        x = self.patchifier.patchify(x)
+
+        x = self.patchify_proj(x)
+        timestep = timestep * 1000.0
+
+        attention_mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1]))
+        attention_mask = attention_mask.masked_fill(attention_mask.to(torch.bool), float("-inf"))  # not sure about this
+        # attention_mask = (context != 0).any(dim=2).to(dtype=x.dtype)
+
+        pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype)
+
+        batch_size = x.shape[0]
+        timestep, embedded_timestep = self.adaln_single(
+            timestep.flatten(),
+            {"resolution": None, "aspect_ratio": None},
+            batch_size=batch_size,
+            hidden_dtype=x.dtype,
+        )
+        # Second dimension is 1 or number of tokens (if timestep_per_token)
+        timestep = timestep.view(batch_size, -1, timestep.shape[-1])
+        embedded_timestep = embedded_timestep.view(
+            batch_size, -1, embedded_timestep.shape[-1]
+        )
+
+        # 2. Blocks
+        if self.caption_projection is not None:
+            batch_size = x.shape[0]
+            context = self.caption_projection(context)
+            context = context.view(
+                batch_size, -1, x.shape[-1]
+            )
+
+        blocks_replace = patches_replace.get("dit", {})
+
+        # enable teacache
+        inp = x.clone()
+        timestep_ = timestep.clone()
+        num_ada_params = self.transformer_blocks[0].scale_shift_table.shape[0]
+        ada_values = self.transformer_blocks[0].scale_shift_table[None, None] + timestep_.reshape(batch_size, timestep_.size(1), num_ada_params, -1)
+        shift_msa, scale_msa, _, _, _, _ = ada_values.unbind(dim=2)
+        modulated_inp = rms_norm(inp)
+        modulated_inp = modulated_inp * (1 + scale_msa) + shift_msa
+
+        if self.cnt == 0 or self.cnt == self.steps - 1:
+            should_calc = True
+            self.accumulated_rel_l1_distance = 0
+        else: 
+            coefficients = [2.14700694e+01, -1.28016453e+01, 2.31279151e+00, 7.92487521e-01, 9.69274326e-03]
+            rescale_func = np.poly1d(coefficients)
+            self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item())
+            if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
+                should_calc = False
+            else:
+                should_calc = True
+                self.accumulated_rel_l1_distance = 0
+                
+        self.previous_modulated_input = modulated_inp 
+        self.cnt += 1
+
+        if self.cnt == self.steps:
+            self.cnt = 0
+        
+        if not should_calc:
+            x += self.previous_residual
+        else:
+            ori_x = x.clone()
+            for i, block in enumerate(self.transformer_blocks):
+                if ("double_block", i) in blocks_replace:
+                    def block_wrap(args):
+                        out = {}
+                        out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"])
+                        return out
+
+                    out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap})
+                    x = out["img"]
+                else:
+                    x = block(
+                        x,
+                        context=context,
+                        attention_mask=attention_mask,
+                        timestep=timestep,
+                        pe=pe
+                    )
+
+            # 3. Output
+            scale_shift_values = (
+                self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None]
+            )
+            shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
+            x = self.norm_out(x)
+            # Modulation
+            x = x * (1 + scale) + shift
+            self.previous_residual = x - ori_x
+
+        x = self.proj_out(x)
+
+        x = self.patchifier.unpatchify(
+            latents=x,
+            output_height=orig_shape[3],
+            output_width=orig_shape[4],
+            output_num_frames=orig_shape[2],
+            out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size),
+        )
+
+        if guiding_latent is not None:
+            x[:, :, 0] = (input_x[:, :, 0] - guiding_latent[:, :, 0]) / input_ts[:, :, 0]
+
+        # print("res", x)
+        return x
+
 class TeaCacheForImgGen:
     @classmethod
     def INPUT_TYPES(s):
@@ -286,10 +446,10 @@ class TeaCacheForImgGen:
     
     def apply_teacache(self, model, enable_teacache: bool, model_type: str, rel_l1_thresh: float, steps: int):
         if enable_teacache:
+            model.model.diffusion_model.__class__.cnt = 0
+            model.model.diffusion_model.__class__.rel_l1_thresh = rel_l1_thresh
+            model.model.diffusion_model.__class__.steps = steps
             if model_type == "flux":
-                model.model.diffusion_model.__class__.cnt = 0
-                model.model.diffusion_model.__class__.rel_l1_thresh = rel_l1_thresh
-                model.model.diffusion_model.__class__.steps = steps
                 model.model.diffusion_model.forward_orig = teacache_flux_forward.__get__(
                                                         model.model.diffusion_model,
                                                         model.model.diffusion_model.__class__
@@ -314,7 +474,7 @@ class TeaCacheForVidGen:
             "required": {
                 "model": ("MODEL", {"tooltip": "The video diffusion model the TeaCache will be applied to."}),
                 "enable_teacache": ("BOOLEAN", {"default": True, "tooltip": "Enable teacache will speed up inference but may lose visual quality."}),
-                "model_type": (["hunyuan_video"],),
+                "model_type": (["hunyuan_video", "ltxv"],),
                 "rel_l1_thresh": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "How strongly to cache the output of diffusion model. This value must be non-negative."}),
                 "steps": ("INT", {"default": 25, "min": 1, "max": 10000, "step": 1}),
             }
@@ -327,14 +487,19 @@ class TeaCacheForVidGen:
     
     def apply_teacache(self, model, enable_teacache: bool, model_type: str, rel_l1_thresh: float, steps: int):
         if enable_teacache:
+            model.model.diffusion_model.__class__.cnt = 0
+            model.model.diffusion_model.__class__.rel_l1_thresh = rel_l1_thresh
+            model.model.diffusion_model.__class__.steps = steps
             if model_type == "hunyuan_video":
-                model.model.diffusion_model.__class__.cnt = 0
-                model.model.diffusion_model.__class__.rel_l1_thresh = rel_l1_thresh
-                model.model.diffusion_model.__class__.steps = steps
                 model.model.diffusion_model.forward_orig = teacache_hunyuanvideo_forward.__get__(
                                                         model.model.diffusion_model,
                                                         model.model.diffusion_model.__class__
                                                         )
+            elif model_type == "ltxv":
+                model.model.diffusion_model.forward = teacache_ltxvmodel_forward.__get__(
+                                                        model.model.diffusion_model,
+                                                        model.model.diffusion_model.__class__
+                                                        )
             else:
                 raise ValueError(f"Unknown type {model_type}")
         else:
@@ -343,6 +508,11 @@ class TeaCacheForVidGen:
                                                         model.model.diffusion_model,
                                                         model.model.diffusion_model.__class__
                                                         )
+            elif model_type == "ltxv":
+                model.model.diffusion_model.forward = LTXVModel.forward.__get__(
+                                                        model.model.diffusion_model,
+                                                        model.model.diffusion_model.__class__
+                                                        )
             else:
                 raise ValueError(f"Unknown type {model_type}")