From a40c99dc5681eeeffa688c21dfb0541cb6f418ee Mon Sep 17 00:00:00 2001
From: YunjieYu <yjyu007@163.com>
Date: Fri, 17 Jan 2025 12:09:50 +0800
Subject: [PATCH] Support CogVideoX

---
 README.md                                     |  35 +-
 __init__.py                                   |  15 +-
 assets/compare_pulid_flux.png                 | Bin 0 -> 745944 bytes
 assets/einstein.jpg                           | Bin 0 -> 9037 bytes
 examples/teacache_cogvideox.json              | 585 +++++++++++
 examples/teacache_compile_flux.json           | 983 -----------------
 examples/teacache_compile_hunyuanvideo.json   | 936 -----------------
 examples/teacache_compile_ltx_video.json      | 761 --------------
 examples/teacache_compile_pulid_flux.json     | 987 ------------------
 .../custom_cogvideox_transformer_3d.py        | 734 +++++++++++++
 models/cogvideox/embeddings.py                | 226 ++++
 models/cogvideox/enhance_a_video/__init__.py  |   0
 models/cogvideox/enhance_a_video/enhance.py   |  82 ++
 models/cogvideox/enhance_a_video/globals.py   |  31 +
 nodes.py                                      |  10 +-
 nodes_diffusers.py                            | 297 ++++++
 pyproject.toml                                |   2 +-
 17 files changed, 1984 insertions(+), 3700 deletions(-)
 create mode 100644 assets/compare_pulid_flux.png
 create mode 100644 assets/einstein.jpg
 create mode 100644 examples/teacache_cogvideox.json
 delete mode 100644 examples/teacache_compile_flux.json
 delete mode 100644 examples/teacache_compile_hunyuanvideo.json
 delete mode 100644 examples/teacache_compile_ltx_video.json
 delete mode 100644 examples/teacache_compile_pulid_flux.json
 create mode 100644 models/cogvideox/custom_cogvideox_transformer_3d.py
 create mode 100644 models/cogvideox/embeddings.py
 create mode 100644 models/cogvideox/enhance_a_video/__init__.py
 create mode 100644 models/cogvideox/enhance_a_video/enhance.py
 create mode 100644 models/cogvideox/enhance_a_video/globals.py
 create mode 100644 nodes_diffusers.py

diff --git a/README.md b/README.md
index 5a5813d..4ca4ec8 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,10 @@ Timestep Embedding Aware Cache ([TeaCache](https://github.com/ali-vilab/TeaCache
 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 17 2025: ComfyUI-TeaCache supports CogVideoX:
+    - It can achieve a 1.5x lossless speedup and a 2x speedup without much visual quality degradation.
+    - Support Text to Video and Image to Video!
+    - Note that TeaCache for CogVideoX node needs to be used with kijai's ComfyUI-CogVideoXWrapper nodes.
 - Jan 15 2025: Thanks [@fantacytyx](https://github.com/fantacytyx), ComfyUI-TeaCache supports PuLID-FLUX and fixes bug about rel_l1_thresh:
     - It can achieve a 1.2x lossless speedup and a 1.7x speedup without much visual quality degradation.
     - Fixes a bug about rel_l1_thresh, when there are multiple TeaCache nodes in a workflow, the rel_l1_thresh value is always the value of the last TeaCache node.
@@ -33,41 +37,34 @@ Installation via ComfyUI-Manager is preferred. Simply search for ComfyUI-TeaCach
 
 ## Usage
 ### TeaCache
-To use TeaCache node, simply add `TeaCache For Img Gen` or `TeaCache For Vid Gen` node to your workflow after `Load Diffusion Model` node or `Load LoRA` node (if you need LoRA). The following table gives the recommended rel_l1_thresh ​for different models:
+To use TeaCache node, simply add `TeaCache For Img Gen` or `TeaCache For Vid Gen` node to your workflow after `Load Diffusion Model` node or `Load LoRA` node (if you need LoRA). Generally, TeaCache can achieve a speedup of 1.5x to 3x with acceptable visual quality loss. The following table gives the recommended rel_l1_thresh ​for different models:
 
-|                 |       FLUX        |     PuLID-FLUX     |     HunyuanVideo   |    LTX-Video    |
-|:---------------:|:-----------------:|:------------------:|:------------------:|:---------------:|
-| rel_l1_thresh   |        0.4        |        0.4         |        0.15        |     0.06        |
-|    speedup      |        ~2x        |        ~1.7x       |        ~2x         |     ~1.7x       |
+|                 |       FLUX        |     PuLID-FLUX     |     HunyuanVideo   |    LTX-Video    |    CogVideoX    |
+|:---------------:|:-----------------:|:------------------:|:------------------:|:---------------:|:---------------:|
+| rel_l1_thresh   |        0.4        |        0.4         |        0.15        |     0.06        |     0.3        |
+|    speedup      |        ~2x        |        ~1.7x       |        ~2x         |     ~1.7x       |     ~2x       |
 
-The demo workflows ([teacache_flux](./examples/teacache_flux.json), [teacache_pulid_flux](./examples/teacache_pulid_flux.json), [teacache_hunyuanvideo](./examples/teacache_hunyuanvideo.json), [teacache_ltx_video](./examples/teacache_ltx_video.json)) are placed in examples folder.
+The demo workflows ([teacache_flux](./examples/teacache_flux.json), [teacache_pulid_flux](./examples/teacache_pulid_flux.json), [teacache_hunyuanvideo](./examples/teacache_hunyuanvideo.json), [teacache_ltx_video](./examples/teacache_ltx_video.json), [teacache_cogvideox](./examples/teacache_cogvideox.json)) are placed in examples folder.
 
 ### Compile Model
 To use Compile Model node, simply add `Compile Model` node to your workflow after TeaCache node. Compile Model uses `torch.compile` to enhance the model performance by compiling model into more efficient intermediate representations (IRs). This compilation process leverages backend compilers to generate optimized code, which can significantly speed up inference. The compilation may take long time when you run the workflow at first, but once it is compiled, inference is extremely fast. The usage is shown below:
 ![](./assets/compile.png)
 
-The demo workflows ([teacache_compile_flux](./examples/teacache_compile_flux.json), [teacache_compile_pulid_flux](./examples/teacache_compile_pulid_flux.json), [teacache_compile_hunyuanvideo](./examples/teacache_compile_hunyuanvideo.json), [teacache_compile_ltx_video](./examples/teacache_compile_ltx_video.json)) are also placed in examples folder.
-
-
-## Demo
-- <p><strong>FLUX</strong></p>
-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)
 
+- <p><strong>PuLID-FLUX</strong></p>
+![](./assets/compare_pulid_flux.png)
+
 - <p><strong>HunyuanVideo</strong></p>
 https://github.com/user-attachments/assets/b3aca64d-c2ae-440c-a362-f3a7b6c633e0
 
 - <p><strong>LTX-Video</strong></p>
 https://github.com/user-attachments/assets/8fce9b48-2243-46f1-b411-80e4a53f6f7d
 
+- <p><strong>CogVideoX</strong></p>
+https://github.com/user-attachments/assets/de8f5400-e885-446d-936f-c026a78ba5c2
+
 ## 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/__init__.py b/__init__.py
index aefb566..886de42 100644
--- a/__init__.py
+++ b/__init__.py
@@ -1,10 +1,7 @@
-# only import if running as a custom node
-try:
-    import comfy.utils
-except ImportError:
-    pass
-else:
-    from .nodes import NODE_CLASS_MAPPINGS
+from .nodes import NODE_CLASS_MAPPINGS as NODES_CLASS, NODE_DISPLAY_NAME_MAPPINGS as NODES_DISPLAY
+from .nodes_diffusers import NODE_CLASS_MAPPINGS as DIFFUSERS_NODE_CLASS, NODE_DISPLAY_NAME_MAPPINGS as DIFFUSERS_NODES_DISPLAY
 
-    NODE_DISPLAY_NAME_MAPPINGS = {k: v.TITLE for k, v in NODE_CLASS_MAPPINGS.items()}
-    __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
+NODE_CLASS_MAPPINGS = {**NODES_CLASS, **DIFFUSERS_NODE_CLASS}
+NODE_DISPLAY_NAME_MAPPINGS = {**NODES_DISPLAY, **DIFFUSERS_NODES_DISPLAY}
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
\ No newline at end of file
diff --git a/assets/compare_pulid_flux.png b/assets/compare_pulid_flux.png
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diff --git a/examples/teacache_cogvideox.json b/examples/teacache_cogvideox.json
new file mode 100644
index 0000000..31be6f1
--- /dev/null
+++ b/examples/teacache_cogvideox.json
@@ -0,0 +1,585 @@
+{
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+  "last_link_id": 78,
+  "nodes": [
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+          "type": "CONDITIONING",
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+          "links": [
+            54
+          ],
+          "slot_index": 0,
+          "shape": 3
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+      ],
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+      },
+      "widgets_values": [
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+        {
+          "name": "samples",
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+          "type": "IMAGE",
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+            59
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+          "link": null,
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+        {
+          "name": "vae",
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+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
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+        "loop_count": 0,
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+        "format": "video/h264-mp4",
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+        "crf": 19,
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+    },
+    {
+      "id": 35,
+      "type": "CogVideoSampler",
+      "pos": [
+        1231.78271484375,
+        307.19598388671875
+      ],
+      "size": [
+        330,
+        574
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "COGVIDEOMODEL",
+          "link": 77
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 67
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 68
+        },
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 72,
+          "shape": 7
+        },
+        {
+          "name": "image_cond_latents",
+          "type": "LATENT",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "context_options",
+          "type": "COGCONTEXT",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "controlnet",
+          "type": "COGVIDECONTROLNET",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "tora_trajectory",
+          "type": "TORAFEATURES",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "fastercache",
+          "type": "FASTERCACHEARGS",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "feta_args",
+          "type": "FETAARGS",
+          "link": null,
+          "shape": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "links": [
+            69
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CogVideoSampler"
+      },
+      "widgets_values": [
+        49,
+        25,
+        6,
+        932680538321021,
+        "randomize",
+        "CogVideoXDDIM",
+        1
+      ]
+    }
+  ],
+  "links": [
+    [
+      54,
+      20,
+      0,
+      30,
+      0,
+      "CLIP"
+    ],
+    [
+      59,
+      11,
+      0,
+      33,
+      0,
+      "IMAGE"
+    ],
+    [
+      65,
+      30,
+      1,
+      31,
+      0,
+      "CLIP"
+    ],
+    [
+      67,
+      30,
+      0,
+      35,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      68,
+      31,
+      0,
+      35,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      69,
+      35,
+      0,
+      11,
+      1,
+      "LATENT"
+    ],
+    [
+      71,
+      36,
+      1,
+      11,
+      0,
+      "VAE"
+    ],
+    [
+      72,
+      37,
+      0,
+      35,
+      3,
+      "LATENT"
+    ],
+    [
+      76,
+      36,
+      0,
+      49,
+      0,
+      "COGVIDEOMODEL"
+    ],
+    [
+      77,
+      49,
+      0,
+      35,
+      0,
+      "COGVIDEOMODEL"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.5209868481924403,
+      "offset": [
+        293.8549765424847,
+        279.76517786713686
+      ]
+    }
+  },
+  "version": 0.4
+}
\ No newline at end of file
diff --git a/examples/teacache_compile_flux.json b/examples/teacache_compile_flux.json
deleted file mode 100644
index 8cbadfd..0000000
--- a/examples/teacache_compile_flux.json
+++ /dev/null
@@ -1,983 +0,0 @@
-{
-  "last_node_id": 41,
-  "last_link_id": 124,
-  "nodes": [
-    {
-      "id": 11,
-      "type": "DualCLIPLoader",
-      "pos": [
-        48,
-        288
-      ],
-      "size": [
-        315,
-        106
-      ],
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "CLIP",
-          "type": "CLIP",
-          "links": [
-            10
-          ],
-          "slot_index": 0,
-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "DualCLIPLoader"
-      },
-      "widgets_values": [
-        "t5xxl_fp16.safetensors",
-        "clip_l.safetensors",
-        "flux"
-      ]
-    },
-    {
-      "id": 26,
-      "type": "FluxGuidance",
-      "pos": [
-        480,
-        144
-      ],
-      "size": [
-        317.4000244140625,
-        58
-      ],
-      "flags": {},
-      "order": 12,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "conditioning",
-          "type": "CONDITIONING",
-          "link": 41
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [
-            42
-          ],
-          "slot_index": 0,
-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "FluxGuidance"
-      },
-      "widgets_values": [
-        3.5
-      ],
-      "color": "#233",
-      "bgcolor": "#355"
-    },
-    {
-      "id": 37,
-      "type": "Note",
-      "pos": [
-        480,
-        1344
-      ],
-      "size": [
-        314.99755859375,
-        117.98363494873047
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [],
-      "properties": {
-        "text": ""
-      },
-      "widgets_values": [
-        "The reference sampling implementation auto adjusts the shift value based on the resolution, if you don't want this you can just bypass (CTRL-B) this ModelSamplingFlux node.\n"
-      ],
-      "color": "#432",
-      "bgcolor": "#653"
-    },
-    {
-      "id": 10,
-      "type": "VAELoader",
-      "pos": [
-        48,
-        432
-      ],
-      "size": [
-        311.81634521484375,
-        60.429901123046875
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "VAE",
-          "type": "VAE",
-          "links": [
-            12
-          ],
-          "slot_index": 0,
-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VAELoader"
-      },
-      "widgets_values": [
-        "ae.safetensors"
-      ]
-    },
-    {
-      "id": 28,
-      "type": "Note",
-      "pos": [
-        48,
-        576
-      ],
-      "size": [
-        336,
-        288
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [],
-      "properties": {
-        "text": ""
-      },
-      "widgets_values": [
-        "If you get an error in any of the nodes above make sure the files are in the correct directories.\n\nSee the top of the examples page for the links : https://comfyanonymous.github.io/ComfyUI_examples/flux/\n\nflux1-dev.safetensors goes in: ComfyUI/models/unet/\n\nt5xxl_fp16.safetensors and clip_l.safetensors go in: ComfyUI/models/clip/\n\nae.safetensors goes in: ComfyUI/models/vae/\n\n\nTip: You can set the weight_dtype above to one of the fp8 types if you have memory issues."
-      ],
-      "color": "#432",
-      "bgcolor": "#653"
-    },
-    {
-      "id": 34,
-      "type": "PrimitiveNode",
-      "pos": [
-        432,
-        480
-      ],
-      "size": [
-        210,
-        82
-      ],
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "INT",
-          "type": "INT",
-          "links": [
-            112,
-            115
-          ],
-          "slot_index": 0,
-          "widget": {
-            "name": "width"
-          }
-        }
-      ],
-      "title": "width",
-      "properties": {
-        "Run widget replace on values": false
-      },
-      "widgets_values": [
-        1024,
-        "fixed"
-      ],
-      "color": "#323",
-      "bgcolor": "#535"
-    },
-    {
-      "id": 35,
-      "type": "PrimitiveNode",
-      "pos": [
-        672,
-        480
-      ],
-      "size": [
-        210,
-        82
-      ],
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "INT",
-          "type": "INT",
-          "links": [
-            113,
-            114
-          ],
-          "slot_index": 0,
-          "widget": {
-            "name": "height"
-          }
-        }
-      ],
-      "title": "height",
-      "properties": {
-        "Run widget replace on values": false
-      },
-      "widgets_values": [
-        1024,
-        "fixed"
-      ],
-      "color": "#323",
-      "bgcolor": "#535"
-    },
-    {
-      "id": 30,
-      "type": "ModelSamplingFlux",
-      "pos": [
-        480,
-        1152
-      ],
-      "size": [
-        315,
-        130
-      ],
-      "flags": {},
-      "order": 14,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "model",
-          "type": "MODEL",
-          "link": 124,
-          "slot_index": 0
-        },
-        {
-          "name": "width",
-          "type": "INT",
-          "link": 115,
-          "slot_index": 1,
-          "widget": {
-            "name": "width"
-          }
-        },
-        {
-          "name": "height",
-          "type": "INT",
-          "link": 114,
-          "slot_index": 2,
-          "widget": {
-            "name": "height"
-          }
-        }
-      ],
-      "outputs": [
-        {
-          "name": "MODEL",
-          "type": "MODEL",
-          "links": [
-            54,
-            55
-          ],
-          "slot_index": 0,
-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "ModelSamplingFlux"
-      },
-      "widgets_values": [
-        1.15,
-        0.5,
-        1024,
-        1024
-      ]
-    },
-    {
-      "id": 22,
-      "type": "BasicGuider",
-      "pos": [
-        576,
-        48
-      ],
-      "size": [
-        222.3482666015625,
-        46
-      ],
-      "flags": {},
-      "order": 15,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "model",
-          "type": "MODEL",
-          "link": 54,
-          "slot_index": 0
-        },
-        {
-          "name": "conditioning",
-          "type": "CONDITIONING",
-          "link": 42,
-          "slot_index": 1
-        }
-      ],
-      "outputs": [
-        {
-          "name": "GUIDER",
-          "type": "GUIDER",
-          "links": [
-            30
-          ],
-          "slot_index": 0,
-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "BasicGuider"
-      },
-      "widgets_values": []
-    },
-    {
-      "id": 13,
-      "type": "SamplerCustomAdvanced",
-      "pos": [
-        864,
-        192
-      ],
-      "size": [
-        272.3617858886719,
-        124.53733825683594
-      ],
-      "flags": {},
-      "order": 17,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "noise",
-          "type": "NOISE",
-          "link": 37,
-          "slot_index": 0
-        },
-        {
-          "name": "guider",
-          "type": "GUIDER",
-          "link": 30,
-          "slot_index": 1
-        },
-        {
-          "name": "sampler",
-          "type": "SAMPLER",
-          "link": 19,
-          "slot_index": 2
-        },
-        {
-          "name": "sigmas",
-          "type": "SIGMAS",
-          "link": 20,
-          "slot_index": 3
-        },
-        {
-          "name": "latent_image",
-          "type": "LATENT",
-          "link": 116,
-          "slot_index": 4
-        }
-      ],
-      "outputs": [
-        {
-          "name": "output",
-          "type": "LATENT",
-          "links": [
-            24
-          ],
-          "slot_index": 0,
-          "shape": 3
-        },
-        {
-          "name": "denoised_output",
-          "type": "LATENT",
-          "links": null,
-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "SamplerCustomAdvanced"
-      },
-      "widgets_values": []
-    },
-    {
-      "id": 16,
-      "type": "KSamplerSelect",
-      "pos": [
-        480,
-        912
-      ],
-      "size": [
-        315,
-        58
-      ],
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "SAMPLER",
-          "type": "SAMPLER",
-          "links": [
-            19
-          ],
-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "KSamplerSelect"
-      },
-      "widgets_values": [
-        "euler"
-      ]
-    },
-    {
-      "id": 27,
-      "type": "EmptySD3LatentImage",
-      "pos": [
-        480,
-        624
-      ],
-      "size": [
-        315,
-        106
-      ],
-      "flags": {},
-      "order": 10,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "width",
-          "type": "INT",
-          "link": 112,
-          "widget": {
-            "name": "width"
-          }
-        },
-        {
-          "name": "height",
-          "type": "INT",
-          "link": 113,
-          "widget": {
-            "name": "height"
-          }
-        }
-      ],
-      "outputs": [
-        {
-          "name": "LATENT",
-          "type": "LATENT",
-          "links": [
-            116
-          ],
-          "slot_index": 0,
-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "EmptySD3LatentImage"
-      },
-      "widgets_values": [
-        1024,
-        1024,
-        1
-      ]
-    },
-    {
-      "id": 6,
-      "type": "CLIPTextEncode",
-      "pos": [
-        384,
-        240
-      ],
-      "size": [
-        422.84503173828125,
-        164.31304931640625
-      ],
-      "flags": {},
-      "order": 9,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "clip",
-          "type": "CLIP",
-          "link": 10
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [
-            41
-          ],
-          "slot_index": 0
-        }
-      ],
-      "title": "CLIP Text Encode (Positive Prompt)",
-      "properties": {
-        "Node name for S&R": "CLIPTextEncode"
-      },
-      "widgets_values": [
-        "cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open holding a fancy black forest cake with candles on top in the kitchen of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere",
-        true
-      ],
-      "color": "#232",
-      "bgcolor": "#353"
-    },
-    {
-      "id": 8,
-      "type": "VAEDecode",
-      "pos": [
-        866,
-        367
-      ],
-      "size": [
-        210,
-        46
-      ],
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-      "order": 18,
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-      "inputs": [
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-          "name": "samples",
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-          "name": "vae",
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-          "name": "IMAGE",
-          "type": "IMAGE",
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-          ],
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-      ],
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-          "name": "model",
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-      "outputs": [
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-          "name": "SIGMAS",
-          "type": "SIGMAS",
-          "links": [
-            20
-          ],
-          "shape": 3
-        }
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-        "Node name for S&R": "BasicScheduler"
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-        "simple",
-        30,
-        1
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-      "type": "SaveImage",
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-          "type": "IMAGE",
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-        }
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-      "properties": {
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-            37
-          ],
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-        }
-      ],
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-        "Node name for S&R": "RandomNoise"
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-      "color": "#2a363b",
-      "bgcolor": "#3f5159"
-    },
-    {
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-      "type": "UNETLoader",
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-        144
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-            122
-          ],
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-          "shape": 3
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-      "properties": {
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-      },
-      "widgets_values": [
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-        "default"
-      ],
-      "color": "#223",
-      "bgcolor": "#335"
-    },
-    {
-      "id": 40,
-      "type": "TeaCacheForImgGen",
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-        990.72705078125
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-      "flags": {},
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-      "mode": 0,
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-          "name": "model",
-          "type": "MODEL",
-          "link": 122
-        }
-      ],
-      "outputs": [
-        {
-          "name": "MODEL",
-          "type": "MODEL",
-          "links": [
-            123
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "TeaCacheForImgGen"
-      },
-      "widgets_values": [
-        "flux",
-        0.4,
-        30
-      ]
-    },
-    {
-      "id": 41,
-      "type": "CompileModel",
-      "pos": [
-        103.08354187011719,
-        1156.1611328125
-      ],
-      "size": [
-        315,
-        130
-      ],
-      "flags": {},
-      "order": 13,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "model",
-          "type": "MODEL",
-          "link": 123
-        }
-      ],
-      "outputs": [
-        {
-          "name": "MODEL",
-          "type": "MODEL",
-          "links": [
-            124
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "CompileModel"
-      },
-      "widgets_values": [
-        "default",
-        "inductor",
-        false,
-        false
-      ]
-    }
-  ],
-  "links": [
-    [
-      9,
-      8,
-      0,
-      9,
-      0,
-      "IMAGE"
-    ],
-    [
-      10,
-      11,
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diff --git a/examples/teacache_compile_hunyuanvideo.json b/examples/teacache_compile_hunyuanvideo.json
deleted file mode 100644
index 2cac575..0000000
--- a/examples/teacache_compile_hunyuanvideo.json
+++ /dev/null
@@ -1,936 +0,0 @@
-{
-  "last_node_id": 82,
-  "last_link_id": 227,
-  "nodes": [
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-      "id": 16,
-      "type": "KSamplerSelect",
-      "pos": [
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-          "type": "SAMPLER",
-          "links": [
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-          "shape": 3
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-      "properties": {
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-      "widgets_values": [
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-      "mode": 0,
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diff --git a/examples/teacache_compile_ltx_video.json b/examples/teacache_compile_ltx_video.json
deleted file mode 100644
index 84eadfe..0000000
--- a/examples/teacache_compile_ltx_video.json
+++ /dev/null
@@ -1,761 +0,0 @@
-{
-  "last_node_id": 90,
-  "last_link_id": 190,
-  "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"
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-          "link": 74
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-          "type": "CONDITIONING",
-          "links": [
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-          ],
-          "slot_index": 0
-        }
-      ],
-      "title": "CLIP Text Encode (Positive Prompt)",
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-      ],
-      "color": "#232",
-      "bgcolor": "#353"
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-      "type": "CLIPTextEncode",
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-      ],
-      "color": "#322",
-      "bgcolor": "#533"
-    },
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-      "type": "KSamplerSelect",
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-          "type": "SAMPLER",
-          "links": [
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-      "flags": {},
-      "order": 1,
-      "mode": 0,
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-      "bgcolor": "#653"
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-      "type": "LTXVConditioning",
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-        60
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-          "link": 169
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-          "link": 170
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-          "name": "positive",
-          "type": "CONDITIONING",
-          "links": [
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-          "slot_index": 0
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-        190
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-        82
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-      "flags": {},
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-            75
-          ],
-          "slot_index": 0
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-        "ltxv"
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-          "name": "vae",
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-          "name": "IMAGE",
-          "type": "IMAGE",
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-          "slot_index": 0
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-      ],
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-          "link": 185
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-          "type": "AUDIO",
-          "link": null,
-          "shape": 7
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-        {
-          "name": "meta_batch",
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-          "name": "Filenames",
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-          "links": null
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-        "filename_prefix": "ltxv",
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-        "save_metadata": true,
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-          "paused": false,
-          "params": {
-            "filename": "ltxv_00032.mp4",
-            "subfolder": "",
-            "type": "output",
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-            "frame_rate": 24
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-          "muted": false
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-          "name": "MODEL",
-          "type": "MODEL",
-          "links": [
-            189
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
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-      },
-      "widgets_values": [
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diff --git a/examples/teacache_compile_pulid_flux.json b/examples/teacache_compile_pulid_flux.json
deleted file mode 100644
index 22cf947..0000000
--- a/examples/teacache_compile_pulid_flux.json
+++ /dev/null
@@ -1,987 +0,0 @@
-{
-  "last_node_id": 73,
-  "last_link_id": 154,
-  "nodes": [
-    {
-      "id": 26,
-      "type": "FluxGuidance",
-      "pos": [
-        372,
-        -171
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-      "size": [
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-      },
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-          "name": "conditioning",
-          "type": "CONDITIONING",
-          "link": 41
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [
-            107
-          ],
-          "slot_index": 0,
-          "shape": 3
-        }
-      ],
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-      "color": "#233",
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-    },
-    {
-      "id": 6,
-      "type": "CLIPTextEncode",
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-      "mode": 0,
-      "inputs": [
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-          "link": 132
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [
-            41
-          ],
-          "slot_index": 0
-        }
-      ],
-      "title": "CLIP Text Encode (Positive Prompt)",
-      "properties": {
-        "Node name for S&R": "CLIPTextEncode"
-      },
-      "widgets_values": [
-        "Half body portrait of 60 years old guy, with an surprised expression, he is lost in vectors of AI models, sourounded by PC monitors and many cables, on his tshirt is a text with words printed in Arial font:\"PuLID Flux\", detailed, glowy background, photorealistic style with skin inperfections, looks like shot with an smartphone, skin details without plastic look, ASUS Keyboard.",
-        true
-      ],
-      "color": "#232",
-      "bgcolor": "#353"
-    },
-    {
-      "id": 27,
-      "type": "EmptySD3LatentImage",
-      "pos": [
-        383,
-        155
-      ],
-      "size": [
-        315,
-        106
-      ],
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
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-          ],
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-          "shape": 3
-        }
-      ],
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-        1024,
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-    },
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-      "type": "KSamplerSelect",
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-          "type": "SAMPLER",
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-            85
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-          "shape": 3
-        }
-      ],
-      "properties": {
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-      "widgets_values": [
-        "euler"
-      ]
-    },
-    {
-      "id": 54,
-      "type": "LoadImage",
-      "pos": [
-        729,
-        -490
-      ],
-      "size": [
-        315,
-        314
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
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-          "type": "IMAGE",
-          "links": [
-            126
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-          "slot_index": 0,
-          "shape": 3
-        },
-        {
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-          "shape": 3
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoadImage"
-      },
-      "widgets_values": [
-        "einstein.jpg",
-        "image"
-      ]
-    },
-    {
-      "id": 53,
-      "type": "PulidFluxInsightFaceLoader",
-      "pos": [
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diff --git a/models/cogvideox/custom_cogvideox_transformer_3d.py b/models/cogvideox/custom_cogvideox_transformer_3d.py
new file mode 100644
index 0000000..fb859a5
--- /dev/null
+++ b/models/cogvideox/custom_cogvideox_transformer_3d.py
@@ -0,0 +1,734 @@
+# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI 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.
+
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+import numpy as np
+from einops import rearrange
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.utils import logging
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.attention import Attention, FeedForward
+from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import AdaLayerNorm, CogVideoXLayerNormZero
+from diffusers.loaders import PeftAdapterMixin
+from diffusers.models.embeddings import apply_rotary_emb
+from .embeddings import CogVideoXPatchEmbed
+
+from .enhance_a_video.enhance import get_feta_scores
+from .enhance_a_video.globals import is_enhance_enabled, set_num_frames
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+try:
+    from sageattention import sageattn
+    SAGEATTN_IS_AVAILABLE = True
+except:
+    SAGEATTN_IS_AVAILABLE = False
+
+from comfy.ldm.modules.attention import optimized_attention
+
+
+def set_attention_func(attention_mode, heads):
+    if attention_mode == "sdpa" or attention_mode == "fused_sdpa":
+        def func(q, k, v, is_causal=False, attn_mask=None):
+            return F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=0.0, is_causal=is_causal)
+        return func
+    elif attention_mode == "comfy":
+        def func(q, k, v, is_causal=False, attn_mask=None):
+            return optimized_attention(q, k, v, mask=attn_mask, heads=heads, skip_reshape=True)
+        return func
+
+    elif attention_mode == "sageattn" or attention_mode == "fused_sageattn":
+        @torch.compiler.disable()
+        def func(q, k, v, is_causal=False, attn_mask=None):
+            return sageattn(q, k, v, is_causal=is_causal, attn_mask=attn_mask)
+        return func
+    elif attention_mode == "sageattn_qk_int8_pv_fp16_cuda":
+        from sageattention import sageattn_qk_int8_pv_fp16_cuda
+        @torch.compiler.disable()
+        def func(q, k, v, is_causal=False, attn_mask=None):
+            return sageattn_qk_int8_pv_fp16_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32")
+        return func
+    elif attention_mode == "sageattn_qk_int8_pv_fp16_triton":
+        from sageattention import sageattn_qk_int8_pv_fp16_triton
+        @torch.compiler.disable()
+        def func(q, k, v, is_causal=False, attn_mask=None):
+            return sageattn_qk_int8_pv_fp16_triton(q, k, v, is_causal=is_causal, attn_mask=attn_mask)
+        return func
+    elif attention_mode == "sageattn_qk_int8_pv_fp8_cuda":
+        from sageattention import sageattn_qk_int8_pv_fp8_cuda
+        @torch.compiler.disable()
+        def func(q, k, v, is_causal=False, attn_mask=None):
+            return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp32")
+        return func
+
+def fft(tensor):
+    tensor_fft = torch.fft.fft2(tensor)
+    tensor_fft_shifted = torch.fft.fftshift(tensor_fft)
+    B, C, H, W = tensor.size()
+    radius = min(H, W) // 5
+            
+    Y, X = torch.meshgrid(torch.arange(H), torch.arange(W))
+    center_x, center_y = W // 2, H // 2
+    mask = (X - center_x) ** 2 + (Y - center_y) ** 2 <= radius ** 2
+    low_freq_mask = mask.unsqueeze(0).unsqueeze(0).to(tensor.device)
+    high_freq_mask = ~low_freq_mask
+            
+    low_freq_fft = tensor_fft_shifted * low_freq_mask
+    high_freq_fft = tensor_fft_shifted * high_freq_mask
+
+    return low_freq_fft, high_freq_fft
+
+#region Attention
+class CogVideoXAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
+    query and key vectors, but does not include spatial normalization.
+    """
+
+    def __init__(self, attn_func, attention_mode: Optional[str] = None):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+        self.attention_mode = attention_mode
+        self.attn_func = attn_func
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        text_seq_length = encoder_hidden_states.size(1)
+
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.to_q.weight.dtype == torch.float16 or attn.to_q.weight.dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(attn.to_q.weight.dtype)
+
+        if not "fused" in self.attention_mode:
+            query = attn.to_q(hidden_states)
+            key = attn.to_k(hidden_states)
+            value = attn.to_v(hidden_states)
+        else:
+            qkv = attn.to_qkv(hidden_states)
+            split_size = qkv.shape[-1] // 3
+            query, key, value = torch.split(qkv, split_size, dim=-1)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
+            if not attn.is_cross_attention:
+                key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
+
+        #feta
+        if is_enhance_enabled():
+            feta_scores = get_feta_scores(attn, query, key, head_dim, text_seq_length)
+                
+        hidden_states = self.attn_func(query, key, value, attn_mask=attention_mask, is_causal=False)
+       
+        if self.attention_mode != "comfy":
+            hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        encoder_hidden_states, hidden_states = hidden_states.split(
+            [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
+        )
+
+        if is_enhance_enabled():
+            hidden_states *= feta_scores
+
+        return hidden_states, encoder_hidden_states
+
+#region Blocks
+@maybe_allow_in_graph
+class CogVideoXBlock(nn.Module):
+    
+    r"""
+    Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model.
+
+    Parameters:
+        dim (`int`):
+            The number of channels in the input and output.
+        num_attention_heads (`int`):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`):
+            The number of channels in each head.
+        time_embed_dim (`int`):
+            The number of channels in timestep embedding.
+        dropout (`float`, defaults to `0.0`):
+            The dropout probability to use.
+        activation_fn (`str`, defaults to `"gelu-approximate"`):
+            Activation function to be used in feed-forward.
+        attention_bias (`bool`, defaults to `False`):
+            Whether or not to use bias in attention projection layers.
+        qk_norm (`bool`, defaults to `True`):
+            Whether or not to use normalization after query and key projections in Attention.
+        norm_elementwise_affine (`bool`, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_eps (`float`, defaults to `1e-5`):
+            Epsilon value for normalization layers.
+        final_dropout (`bool` defaults to `False`):
+            Whether to apply a final dropout after the last feed-forward layer.
+        ff_inner_dim (`int`, *optional*, defaults to `None`):
+            Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used.
+        ff_bias (`bool`, defaults to `True`):
+            Whether or not to use bias in Feed-forward layer.
+        attention_out_bias (`bool`, defaults to `True`):
+            Whether or not to use bias in Attention output projection layer.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        time_embed_dim: int,
+        dropout: float = 0.0,
+        activation_fn: str = "gelu-approximate",
+        attention_bias: bool = False,
+        qk_norm: bool = True,
+        norm_elementwise_affine: bool = True,
+        norm_eps: float = 1e-5,
+        final_dropout: bool = True,
+        ff_inner_dim: Optional[int] = None,
+        ff_bias: bool = True,
+        attention_out_bias: bool = True,
+        attention_mode: Optional[str] = "sdpa",
+    ):
+        super().__init__()
+
+        # 1. Self Attention
+        self.norm1 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
+
+        attn_func = set_attention_func(attention_mode, num_attention_heads)
+       
+        self.attn1 = Attention(
+            query_dim=dim,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            qk_norm="layer_norm" if qk_norm else None,
+            eps=1e-6,
+            bias=attention_bias,
+            out_bias=attention_out_bias,
+            processor=CogVideoXAttnProcessor2_0(attn_func, attention_mode=attention_mode),
+        )
+
+        # 2. Feed Forward
+        self.norm2 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True)
+
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,
+            activation_fn=activation_fn,
+            final_dropout=final_dropout,
+            inner_dim=ff_inner_dim,
+            bias=ff_bias,
+        )
+        self.cached_hidden_states = []
+        self.cached_encoder_hidden_states = []
+        
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        video_flow_feature: Optional[torch.Tensor] = None,
+        fuser=None,
+        block_use_fastercache=False,
+        fastercache_counter=0,
+        fastercache_start_step=15,
+        fastercache_device="cuda:0",
+    ) -> torch.Tensor:
+        #print("hidden_states in block: ", hidden_states.shape) #1.5: torch.Size([2, 3200, 3072]) 10.: torch.Size([2, 6400, 3072])
+        text_seq_length = encoder_hidden_states.size(1)
+
+        # norm & modulate
+        norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1(
+            hidden_states, encoder_hidden_states, temb
+        )
+        #print("norm_hidden_states in block: ", norm_hidden_states.shape) #torch.Size([2, 3200, 3072])
+      
+        # Tora Motion-guidance Fuser
+        if video_flow_feature is not None:
+            H, W = video_flow_feature.shape[-2:]
+            T = norm_hidden_states.shape[1] // H // W
+            h = rearrange(norm_hidden_states, "B (T H W) C -> (B T) C H W", H=H, W=W)
+            h = fuser(h, video_flow_feature.to(h), T=T)
+            norm_hidden_states = rearrange(h, "(B T) C H W ->  B (T H W) C", T=T)
+            del h, fuser        
+        
+        #region fastercache
+        if block_use_fastercache:
+            B = norm_hidden_states.shape[0]
+            if fastercache_counter >= fastercache_start_step + 3 and fastercache_counter%3!=0 and self.cached_hidden_states[-1].shape[0] >= B:
+                attn_hidden_states = (
+                    self.cached_hidden_states[1][:B] + 
+                    (self.cached_hidden_states[1][:B] - self.cached_hidden_states[0][:B]) 
+                    * 0.3
+                    ).to(norm_hidden_states.device, non_blocking=True)
+                attn_encoder_hidden_states = (
+                    self.cached_encoder_hidden_states[1][:B] + 
+                    (self.cached_encoder_hidden_states[1][:B] - self.cached_encoder_hidden_states[0][:B])
+                    * 0.3
+                    ).to(norm_hidden_states.device, non_blocking=True)
+            else:
+                attn_hidden_states, attn_encoder_hidden_states = self.attn1(
+                    hidden_states=norm_hidden_states,
+                    encoder_hidden_states=norm_encoder_hidden_states,
+                    image_rotary_emb=image_rotary_emb,
+                )
+                if fastercache_counter == fastercache_start_step:
+                    self.cached_hidden_states = [attn_hidden_states.to(fastercache_device), attn_hidden_states.to(fastercache_device)]
+                    self.cached_encoder_hidden_states = [attn_encoder_hidden_states.to(fastercache_device), attn_encoder_hidden_states.to(fastercache_device)]
+                elif fastercache_counter > fastercache_start_step:
+                    self.cached_hidden_states[-1].copy_(attn_hidden_states.to(fastercache_device))
+                    self.cached_encoder_hidden_states[-1].copy_(attn_encoder_hidden_states.to(fastercache_device))
+        else:
+            attn_hidden_states, attn_encoder_hidden_states = self.attn1(
+                hidden_states=norm_hidden_states,
+                encoder_hidden_states=norm_encoder_hidden_states,
+                image_rotary_emb=image_rotary_emb
+            )
+        
+        hidden_states = hidden_states + gate_msa * attn_hidden_states
+        encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states
+
+        # norm & modulate
+       
+        norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
+            hidden_states, encoder_hidden_states, temb
+        )
+
+        # feed-forward
+        norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
+        ff_output = self.ff(norm_hidden_states)
+
+        hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:]
+        encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length]
+
+        return hidden_states, encoder_hidden_states
+
+#region Transformer
+class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+    """
+    A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo).
+
+    Parameters:
+        num_attention_heads (`int`, defaults to `30`):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, defaults to `64`):
+            The number of channels in each head.
+        in_channels (`int`, defaults to `16`):
+            The number of channels in the input.
+        out_channels (`int`, *optional*, defaults to `16`):
+            The number of channels in the output.
+        flip_sin_to_cos (`bool`, defaults to `True`):
+            Whether to flip the sin to cos in the time embedding.
+        time_embed_dim (`int`, defaults to `512`):
+            Output dimension of timestep embeddings.
+        text_embed_dim (`int`, defaults to `4096`):
+            Input dimension of text embeddings from the text encoder.
+        num_layers (`int`, defaults to `30`):
+            The number of layers of Transformer blocks to use.
+        dropout (`float`, defaults to `0.0`):
+            The dropout probability to use.
+        attention_bias (`bool`, defaults to `True`):
+            Whether or not to use bias in the attention projection layers.
+        sample_width (`int`, defaults to `90`):
+            The width of the input latents.
+        sample_height (`int`, defaults to `60`):
+            The height of the input latents.
+        sample_frames (`int`, defaults to `49`):
+            The number of frames in the input latents. Note that this parameter was incorrectly initialized to 49
+            instead of 13 because CogVideoX processed 13 latent frames at once in its default and recommended settings,
+            but cannot be changed to the correct value to ensure backwards compatibility. To create a transformer with
+            K latent frames, the correct value to pass here would be: ((K - 1) * temporal_compression_ratio + 1).
+        patch_size (`int`, defaults to `2`):
+            The size of the patches to use in the patch embedding layer.
+        temporal_compression_ratio (`int`, defaults to `4`):
+            The compression ratio across the temporal dimension. See documentation for `sample_frames`.
+        max_text_seq_length (`int`, defaults to `226`):
+            The maximum sequence length of the input text embeddings.
+        activation_fn (`str`, defaults to `"gelu-approximate"`):
+            Activation function to use in feed-forward.
+        timestep_activation_fn (`str`, defaults to `"silu"`):
+            Activation function to use when generating the timestep embeddings.
+        norm_elementwise_affine (`bool`, defaults to `True`):
+            Whether or not to use elementwise affine in normalization layers.
+        norm_eps (`float`, defaults to `1e-5`):
+            The epsilon value to use in normalization layers.
+        spatial_interpolation_scale (`float`, defaults to `1.875`):
+            Scaling factor to apply in 3D positional embeddings across spatial dimensions.
+        temporal_interpolation_scale (`float`, defaults to `1.0`):
+            Scaling factor to apply in 3D positional embeddings across temporal dimensions.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 30,
+        attention_head_dim: int = 64,
+        in_channels: int = 16,
+        out_channels: Optional[int] = 16,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        time_embed_dim: int = 512,
+        ofs_embed_dim: Optional[int] = None,
+        text_embed_dim: int = 4096,
+        num_layers: int = 30,
+        dropout: float = 0.0,
+        attention_bias: bool = True,
+        sample_width: int = 90,
+        sample_height: int = 60,
+        sample_frames: int = 49,
+        patch_size: int = 2,
+        patch_size_t: int = None,
+        temporal_compression_ratio: int = 4,
+        max_text_seq_length: int = 226,
+        activation_fn: str = "gelu-approximate",
+        timestep_activation_fn: str = "silu",
+        norm_elementwise_affine: bool = True,
+        norm_eps: float = 1e-5,
+        spatial_interpolation_scale: float = 1.875,
+        temporal_interpolation_scale: float = 1.0,
+        use_rotary_positional_embeddings: bool = False,
+        use_learned_positional_embeddings: bool = False,
+        patch_bias: bool = True,
+        attention_mode: Optional[str] = "sdpa",
+    ):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+
+        if not use_rotary_positional_embeddings and use_learned_positional_embeddings:
+            raise ValueError(
+                "There are no CogVideoX checkpoints available with disable rotary embeddings and learned positional "
+                "embeddings. If you're using a custom model and/or believe this should be supported, please open an "
+                "issue at https://github.com/huggingface/diffusers/issues."
+            )
+
+        # 1. Patch embedding
+        self.patch_embed = CogVideoXPatchEmbed(
+            patch_size=patch_size,
+            patch_size_t=patch_size_t,
+            in_channels=in_channels,
+            embed_dim=inner_dim,
+            text_embed_dim=text_embed_dim,
+            bias=patch_bias,
+            sample_width=sample_width,
+            sample_height=sample_height,
+            sample_frames=sample_frames,
+            temporal_compression_ratio=temporal_compression_ratio,
+            max_text_seq_length=max_text_seq_length,
+            spatial_interpolation_scale=spatial_interpolation_scale,
+            temporal_interpolation_scale=temporal_interpolation_scale,
+            use_positional_embeddings=not use_rotary_positional_embeddings,
+            use_learned_positional_embeddings=use_learned_positional_embeddings,
+        )
+        self.embedding_dropout = nn.Dropout(dropout)
+
+        # 2. Time embeddings
+        self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
+        self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
+
+        self.ofs_proj = None
+        self.ofs_embedding = None
+
+        if ofs_embed_dim:
+            self.ofs_proj = Timesteps(ofs_embed_dim, flip_sin_to_cos, freq_shift)
+            self.ofs_embedding = TimestepEmbedding(ofs_embed_dim, ofs_embed_dim, timestep_activation_fn) # same as time embeddings, for ofs
+
+        # 3. Define spatio-temporal transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                CogVideoXBlock(
+                    dim=inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    time_embed_dim=time_embed_dim,
+                    dropout=dropout,
+                    activation_fn=activation_fn,
+                    attention_bias=attention_bias,
+                    attention_mode=attention_mode,
+                    norm_elementwise_affine=norm_elementwise_affine,
+                    norm_eps=norm_eps,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+        self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine)
+
+        # 4. Output blocks
+        self.norm_out = AdaLayerNorm(
+            embedding_dim=time_embed_dim,
+            output_dim=2 * inner_dim,
+            norm_elementwise_affine=norm_elementwise_affine,
+            norm_eps=norm_eps,
+            chunk_dim=1,
+        )
+        if patch_size_t is None:
+            # For CogVideox 1.0
+            output_dim = patch_size * patch_size * out_channels
+        else:
+            # For CogVideoX 1.5
+            output_dim = patch_size * patch_size * patch_size_t * out_channels
+
+        self.proj_out = nn.Linear(inner_dim, output_dim)
+
+        self.gradient_checkpointing = False
+
+        self.fuser_list = None
+        self.use_fastercache = False
+        self.fastercache_counter = 0
+        self.fastercache_start_step = 15
+        self.fastercache_lf_step = 40
+        self.fastercache_hf_step = 30
+        self.fastercache_device = "cuda"
+        self.fastercache_num_blocks_to_cache = len(self.transformer_blocks)
+        self.attention_mode = attention_mode
+        
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        self.gradient_checkpointing = value
+    #region forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: Union[int, float, torch.LongTensor],
+        timestep_cond: Optional[torch.Tensor] = None,
+        ofs: Optional[Union[int, float, torch.LongTensor]] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        controlnet_states: torch.Tensor = None,
+        controlnet_weights: Optional[Union[float, int, list, np.ndarray, torch.FloatTensor]] = 1.0,
+        video_flow_features: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ):
+        batch_size, num_frames, channels, height, width = hidden_states.shape
+
+        set_num_frames(num_frames) #enhance a video global
+   
+        # 1. Time embedding
+        timesteps = timestep
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=hidden_states.dtype)
+
+        emb = self.time_embedding(t_emb, timestep_cond)
+        if self.ofs_embedding is not None: #1.5 I2V
+            ofs_emb = self.ofs_proj(ofs)
+            ofs_emb = ofs_emb.to(dtype=hidden_states.dtype)
+            ofs_emb = self.ofs_embedding(ofs_emb)
+            emb = emb + ofs_emb
+
+        # 2. Patch embedding
+        p = self.config.patch_size
+        p_t = self.config.patch_size_t
+
+        #print("hidden_states before patch_embedding", hidden_states.shape) #torch.Size([2, 4, 16, 60, 90])
+        
+        hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
+        #print("hidden_states after patch_embedding", hidden_states.shape) #1.5: torch.Size([2, 2926, 3072]) #1.0: torch.Size([2, 5626, 3072])
+        hidden_states = self.embedding_dropout(hidden_states)
+
+        text_seq_length = encoder_hidden_states.shape[1]
+        encoder_hidden_states = hidden_states[:, :text_seq_length]
+        hidden_states = hidden_states[:, text_seq_length:]
+        #print("hidden_states after split", hidden_states.shape) #1.5: torch.Size([2, 2700, 3072]) #1.0: torch.Size([2, 5400, 3072])
+      
+        if self.use_fastercache:
+            self.fastercache_counter+=1
+        if self.fastercache_counter >= self.fastercache_start_step + 3 and self.fastercache_counter % 5 !=0:
+            # 3. Transformer blocks
+            for i, block in enumerate(self.transformer_blocks):
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states=hidden_states[:1],
+                    encoder_hidden_states=encoder_hidden_states[:1],
+                    temb=emb[:1],
+                    image_rotary_emb=image_rotary_emb,
+                    video_flow_feature=video_flow_features[i][:1] if video_flow_features is not None else None,
+                    fuser = self.fuser_list[i] if self.fuser_list is not None else None,
+                    block_use_fastercache = i <= self.fastercache_num_blocks_to_cache,
+                    fastercache_counter = self.fastercache_counter,
+                    fastercache_start_step = self.fastercache_start_step,
+                    fastercache_device = self.fastercache_device
+                )
+
+                if (controlnet_states is not None) and (i < len(controlnet_states)):
+                    controlnet_states_block = controlnet_states[i]
+                    controlnet_block_weight = 1.0
+                    if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
+                        controlnet_block_weight = controlnet_weights[i]
+                    elif isinstance(controlnet_weights, (float, int)):
+                        controlnet_block_weight = controlnet_weights
+                    
+                    hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
+                    
+            if not self.config.use_rotary_positional_embeddings:
+                # CogVideoX-2B
+                hidden_states = self.norm_final(hidden_states)
+            else:
+                # CogVideoX-5B
+                hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+                hidden_states = self.norm_final(hidden_states)
+                hidden_states = hidden_states[:, text_seq_length:]
+
+            # 4. Final block
+            hidden_states = self.norm_out(hidden_states, temb=emb[:1])
+            hidden_states = self.proj_out(hidden_states)
+
+            # 5. Unpatchify
+            # Note: we use `-1` instead of `channels`:
+            #   - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
+            #   - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
+            
+            if p_t is None:
+                output = hidden_states.reshape(1, num_frames, height // p, width // p, -1, p, p)
+                output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
+            else:
+                output = hidden_states.reshape(
+                    1, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
+                )
+                output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
+            
+            (bb, tt, cc, hh, ww) = output.shape
+            cond = rearrange(output, "B T C H W -> (B T) C H W", B=bb, C=cc, T=tt, H=hh, W=ww)
+            lf_c, hf_c = fft(cond.float())
+            #lf_step = 40
+            #hf_step = 30
+            if self.fastercache_counter <= self.fastercache_lf_step:
+                self.delta_lf = self.delta_lf * 1.1
+            if self.fastercache_counter >= self.fastercache_hf_step:
+                self.delta_hf = self.delta_hf * 1.1
+   
+            new_hf_uc = self.delta_hf + hf_c
+            new_lf_uc = self.delta_lf + lf_c
+
+            combine_uc = new_lf_uc + new_hf_uc
+            combined_fft = torch.fft.ifftshift(combine_uc)
+            recovered_uncond = torch.fft.ifft2(combined_fft).real
+            recovered_uncond = rearrange(recovered_uncond.to(output.dtype), "(B T) C H W -> B T C H W", B=bb, C=cc, T=tt, H=hh, W=ww)
+            output = torch.cat([output, recovered_uncond])
+        else:
+            for i, block in enumerate(self.transformer_blocks):
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=emb,
+                    image_rotary_emb=image_rotary_emb,
+                    video_flow_feature=video_flow_features[i] if video_flow_features is not None else None,
+                    fuser = self.fuser_list[i] if self.fuser_list is not None else None,
+                    block_use_fastercache = i <= self.fastercache_num_blocks_to_cache,
+                    fastercache_counter = self.fastercache_counter,
+                    fastercache_start_step = self.fastercache_start_step,
+                    fastercache_device = self.fastercache_device
+                )
+                #has_nan = torch.isnan(hidden_states).any()
+                #if has_nan:
+                #    raise ValueError(f"block output hidden_states has nan: {has_nan}")
+
+                #controlnet
+                if (controlnet_states is not None) and (i < len(controlnet_states)):
+                    controlnet_states_block = controlnet_states[i]
+                    controlnet_block_weight = 1.0
+                    if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
+                        controlnet_block_weight = controlnet_weights[i]
+                        print(controlnet_block_weight)
+                    elif isinstance(controlnet_weights, (float, int)):
+                        controlnet_block_weight = controlnet_weights                    
+                    hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
+                    
+            if not self.config.use_rotary_positional_embeddings:
+                # CogVideoX-2B
+                hidden_states = self.norm_final(hidden_states)
+            else:
+                # CogVideoX-5B
+                hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+                hidden_states = self.norm_final(hidden_states)
+                hidden_states = hidden_states[:, text_seq_length:]
+
+            # 4. Final block
+            hidden_states = self.norm_out(hidden_states, temb=emb)
+            hidden_states = self.proj_out(hidden_states)
+
+            # 5. Unpatchify
+            # Note: we use `-1` instead of `channels`:
+            #   - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
+            #   - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
+           
+            if p_t is None:
+                output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
+                output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
+            else:
+                output = hidden_states.reshape(
+                    batch_size, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
+                )
+                output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
+
+            if self.fastercache_counter >= self.fastercache_start_step + 1: 
+                (bb, tt, cc, hh, ww) = output.shape
+                cond = rearrange(output[0:1].float(), "B T C H W -> (B T) C H W", B=bb//2, C=cc, T=tt, H=hh, W=ww)
+                uncond = rearrange(output[1:2].float(), "B T C H W -> (B T) C H W", B=bb//2, C=cc, T=tt, H=hh, W=ww)
+
+                lf_c, hf_c = fft(cond)
+                lf_uc, hf_uc = fft(uncond)
+
+                self.delta_lf = lf_uc - lf_c
+                self.delta_hf = hf_uc - hf_c
+
+        if not return_dict:
+            return (output,)
+        return Transformer2DModelOutput(sample=output)
+    
\ No newline at end of file
diff --git a/models/cogvideox/embeddings.py b/models/cogvideox/embeddings.py
new file mode 100644
index 0000000..111ba04
--- /dev/null
+++ b/models/cogvideox/embeddings.py
@@ -0,0 +1,226 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from typing import Tuple, Union, Optional
+from diffusers.models.embeddings import get_3d_sincos_pos_embed, get_1d_rotary_pos_embed
+
+
+class CogVideoXPatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 2,
+        patch_size_t: Optional[int] = None,
+        in_channels: int = 16,
+        embed_dim: int = 1920,
+        text_embed_dim: int = 4096,
+        bias: bool = True,
+        sample_width: int = 90,
+        sample_height: int = 60,
+        sample_frames: int = 49,
+        temporal_compression_ratio: int = 4,
+        max_text_seq_length: int = 226,
+        spatial_interpolation_scale: float = 1.875,
+        temporal_interpolation_scale: float = 1.0,
+        use_positional_embeddings: bool = True,
+        use_learned_positional_embeddings: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.patch_size = patch_size
+        self.patch_size_t = patch_size_t
+        self.embed_dim = embed_dim
+        self.sample_height = sample_height
+        self.sample_width = sample_width
+        self.sample_frames = sample_frames
+        self.temporal_compression_ratio = temporal_compression_ratio
+        self.max_text_seq_length = max_text_seq_length
+        self.spatial_interpolation_scale = spatial_interpolation_scale
+        self.temporal_interpolation_scale = temporal_interpolation_scale
+        self.use_positional_embeddings = use_positional_embeddings
+        self.use_learned_positional_embeddings = use_learned_positional_embeddings
+
+        if patch_size_t is None:
+            # CogVideoX 1.0 checkpoints
+            self.proj = nn.Conv2d(
+                in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
+            )
+        else:
+            # CogVideoX 1.5 checkpoints
+            self.proj = nn.Linear(in_channels * patch_size * patch_size * patch_size_t, embed_dim)
+
+        self.text_proj = nn.Linear(text_embed_dim, embed_dim)
+
+        if use_positional_embeddings or use_learned_positional_embeddings:
+            persistent = use_learned_positional_embeddings
+            pos_embedding = self._get_positional_embeddings(sample_height, sample_width, sample_frames)
+            self.register_buffer("pos_embedding", pos_embedding, persistent=persistent)
+
+    def _get_positional_embeddings(self, sample_height: int, sample_width: int, sample_frames: int) -> torch.Tensor:
+        post_patch_height = sample_height // self.patch_size
+        post_patch_width = sample_width // self.patch_size
+        post_time_compression_frames = (sample_frames - 1) // self.temporal_compression_ratio + 1
+        num_patches = post_patch_height * post_patch_width * post_time_compression_frames
+
+        pos_embedding = get_3d_sincos_pos_embed(
+            self.embed_dim,
+            (post_patch_width, post_patch_height),
+            post_time_compression_frames,
+            self.spatial_interpolation_scale,
+            self.temporal_interpolation_scale,
+        )
+        pos_embedding = torch.from_numpy(pos_embedding).flatten(0, 1)
+        joint_pos_embedding = torch.zeros(
+            1, self.max_text_seq_length + num_patches, self.embed_dim, requires_grad=False
+        )
+        joint_pos_embedding.data[:, self.max_text_seq_length :].copy_(pos_embedding)
+
+        return joint_pos_embedding
+
+    def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):
+        r"""
+        Args:
+            text_embeds (`torch.Tensor`):
+                Input text embeddings. Expected shape: (batch_size, seq_length, embedding_dim).
+            image_embeds (`torch.Tensor`):
+                Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width).
+        """
+        text_embeds = self.text_proj(text_embeds)
+
+        batch_size, num_frames, channels, height, width = image_embeds.shape
+
+        if self.patch_size_t is None:
+            image_embeds = image_embeds.reshape(-1, channels, height, width)
+            image_embeds = self.proj(image_embeds)
+            image_embeds = image_embeds.view(batch_size, num_frames, *image_embeds.shape[1:])
+            image_embeds = image_embeds.flatten(3).transpose(2, 3)  # [batch, num_frames, height x width, channels]
+            image_embeds = image_embeds.flatten(1, 2)  # [batch, num_frames x height x width, channels]
+        else:
+            p = self.patch_size
+            p_t = self.patch_size_t
+
+            image_embeds = image_embeds.permute(0, 1, 3, 4, 2)
+            image_embeds = image_embeds.reshape(
+                batch_size, num_frames // p_t, p_t, height // p, p, width // p, p, channels
+            )
+            image_embeds = image_embeds.permute(0, 1, 3, 5, 7, 2, 4, 6).flatten(4, 7).flatten(1, 3)
+            image_embeds = self.proj(image_embeds)
+
+        embeds = torch.cat(
+            [text_embeds, image_embeds], dim=1
+        ).contiguous()  # [batch, seq_length + num_frames x height x width, channels]
+
+        if self.use_positional_embeddings or self.use_learned_positional_embeddings:
+            if self.use_learned_positional_embeddings and (self.sample_width != width or self.sample_height != height):
+                raise ValueError(
+                    "It is currently not possible to generate videos at a different resolution that the defaults. This should only be the case with 'THUDM/CogVideoX-5b-I2V'."
+                    "If you think this is incorrect, please open an issue at https://github.com/huggingface/diffusers/issues."
+                )
+
+            pre_time_compression_frames = (num_frames - 1) * self.temporal_compression_ratio + 1
+
+            if (
+                self.sample_height != height
+                or self.sample_width != width
+                or self.sample_frames != pre_time_compression_frames
+            ):
+                pos_embedding = self._get_positional_embeddings(height, width, pre_time_compression_frames)
+                pos_embedding = pos_embedding.to(embeds.device, dtype=embeds.dtype)
+            else:
+                pos_embedding = self.pos_embedding
+
+            embeds = embeds + pos_embedding
+
+        return embeds
+
+def get_3d_rotary_pos_embed(
+    embed_dim,
+    crops_coords,
+    grid_size,
+    temporal_size,
+    theta: int = 10000,
+    use_real: bool = True,
+    grid_type: str = "linspace",
+    max_size: Optional[Tuple[int, int]] = None,
+) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+    """
+    RoPE for video tokens with 3D structure.
+
+    Args:
+    embed_dim: (`int`):
+        The embedding dimension size, corresponding to hidden_size_head.
+    crops_coords (`Tuple[int]`):
+        The top-left and bottom-right coordinates of the crop.
+    grid_size (`Tuple[int]`):
+        The grid size of the spatial positional embedding (height, width).
+    temporal_size (`int`):
+        The size of the temporal dimension.
+    theta (`float`):
+        Scaling factor for frequency computation.
+    grid_type (`str`):
+        Whether to use "linspace" or "slice" to compute grids.
+
+    Returns:
+        `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
+    """
+    if use_real is not True:
+        raise ValueError(" `use_real = False` is not currently supported for get_3d_rotary_pos_embed")
+
+    if grid_type == "linspace":
+        start, stop = crops_coords
+        grid_size_h, grid_size_w = grid_size
+        grid_h = np.linspace(start[0], stop[0], grid_size_h, endpoint=False, dtype=np.float32)
+        grid_w = np.linspace(start[1], stop[1], grid_size_w, endpoint=False, dtype=np.float32)
+        grid_t = np.arange(temporal_size, dtype=np.float32)
+        grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
+    elif grid_type == "slice":
+        max_h, max_w = max_size
+        grid_size_h, grid_size_w = grid_size
+        grid_h = np.arange(max_h, dtype=np.float32)
+        grid_w = np.arange(max_w, dtype=np.float32)
+        grid_t = np.arange(temporal_size, dtype=np.float32)
+    else:
+        raise ValueError("Invalid value passed for `grid_type`.")
+
+    # Compute dimensions for each axis
+    dim_t = embed_dim // 4
+    dim_h = embed_dim // 8 * 3
+    dim_w = embed_dim // 8 * 3
+
+    # Temporal frequencies
+    freqs_t = get_1d_rotary_pos_embed(dim_t, grid_t, use_real=True)
+    # Spatial frequencies for height and width
+    freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, use_real=True)
+    freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, use_real=True)
+
+    # BroadCast and concatenate temporal and spaial frequencie (height and width) into a 3d tensor
+    def combine_time_height_width(freqs_t, freqs_h, freqs_w):
+        freqs_t = freqs_t[:, None, None, :].expand(
+            -1, grid_size_h, grid_size_w, -1
+        )  # temporal_size, grid_size_h, grid_size_w, dim_t
+        freqs_h = freqs_h[None, :, None, :].expand(
+            temporal_size, -1, grid_size_w, -1
+        )  # temporal_size, grid_size_h, grid_size_2, dim_h
+        freqs_w = freqs_w[None, None, :, :].expand(
+            temporal_size, grid_size_h, -1, -1
+        )  # temporal_size, grid_size_h, grid_size_2, dim_w
+
+        freqs = torch.cat(
+            [freqs_t, freqs_h, freqs_w], dim=-1
+        )  # temporal_size, grid_size_h, grid_size_w, (dim_t + dim_h + dim_w)
+        freqs = freqs.view(
+            temporal_size * grid_size_h * grid_size_w, -1
+        )  # (temporal_size * grid_size_h * grid_size_w), (dim_t + dim_h + dim_w)
+        return freqs
+
+    t_cos, t_sin = freqs_t  # both t_cos and t_sin has shape: temporal_size, dim_t
+    h_cos, h_sin = freqs_h  # both h_cos and h_sin has shape: grid_size_h, dim_h
+    w_cos, w_sin = freqs_w  # both w_cos and w_sin has shape: grid_size_w, dim_w
+
+    if grid_type == "slice":
+        t_cos, t_sin = t_cos[:temporal_size], t_sin[:temporal_size]
+        h_cos, h_sin = h_cos[:grid_size_h], h_sin[:grid_size_h]
+        w_cos, w_sin = w_cos[:grid_size_w], w_sin[:grid_size_w]
+
+    cos = combine_time_height_width(t_cos, h_cos, w_cos)
+    sin = combine_time_height_width(t_sin, h_sin, w_sin)
+    return cos, sin
\ No newline at end of file
diff --git a/models/cogvideox/enhance_a_video/__init__.py b/models/cogvideox/enhance_a_video/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/models/cogvideox/enhance_a_video/enhance.py b/models/cogvideox/enhance_a_video/enhance.py
new file mode 100644
index 0000000..12e2fa7
--- /dev/null
+++ b/models/cogvideox/enhance_a_video/enhance.py
@@ -0,0 +1,82 @@
+import torch
+from einops import rearrange
+from diffusers.models.attention import Attention
+from .globals import get_enhance_weight, get_num_frames
+
+# def get_feta_scores(query, key):
+#     img_q, img_k = query, key
+   
+#     num_frames = get_num_frames()
+    
+#     B, S, N, C = img_q.shape
+
+#     # Calculate spatial dimension
+#     spatial_dim = S // num_frames
+    
+#     # Add time dimension between spatial and head dims
+#     query_image = img_q.reshape(B, spatial_dim, num_frames, N, C)
+#     key_image = img_k.reshape(B, spatial_dim, num_frames, N, C)
+    
+#     # Expand time dimension
+#     query_image = query_image.expand(-1, -1, num_frames, -1, -1)  # [B, S, T, N, C]
+#     key_image = key_image.expand(-1, -1, num_frames, -1, -1)      # [B, S, T, N, C]
+    
+#     # Reshape to match feta_score input format: [(B S) N T C]
+#     query_image = rearrange(query_image, "b s t n c -> (b s) n t c")  #torch.Size([3200, 24, 5, 128])
+#     key_image = rearrange(key_image, "b s t n c -> (b s) n t c")
+    
+#     return feta_score(query_image, key_image, C, num_frames)
+ 
+def get_feta_scores(
+        attn: Attention,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        head_dim: int,
+        text_seq_length: int,
+    ) -> torch.Tensor:
+        num_frames = get_num_frames()
+        spatial_dim = int((query.shape[2] - text_seq_length) / num_frames)
+
+        query_image = rearrange(
+            query[:, :, text_seq_length:],
+            "B N (T S) C -> (B S) N T C",
+            N=attn.heads,
+            T=num_frames,
+            S=spatial_dim,
+            C=head_dim,
+        )
+        key_image = rearrange(
+            key[:, :, text_seq_length:],
+            "B N (T S) C -> (B S) N T C",
+            N=attn.heads,
+            T=num_frames,
+            S=spatial_dim,
+            C=head_dim,
+        )
+        return feta_score(query_image, key_image, head_dim, num_frames)
+
+def feta_score(query_image, key_image, head_dim, num_frames):
+    scale = head_dim**-0.5
+    query_image = query_image * scale
+    attn_temp = query_image @ key_image.transpose(-2, -1)  # translate attn to float32
+    attn_temp = attn_temp.to(torch.float32)
+    attn_temp = attn_temp.softmax(dim=-1)
+
+    # Reshape to [batch_size * num_tokens, num_frames, num_frames]
+    attn_temp = attn_temp.reshape(-1, num_frames, num_frames)
+
+    # Create a mask for diagonal elements
+    diag_mask = torch.eye(num_frames, device=attn_temp.device).bool()
+    diag_mask = diag_mask.unsqueeze(0).expand(attn_temp.shape[0], -1, -1)
+
+    # Zero out diagonal elements
+    attn_wo_diag = attn_temp.masked_fill(diag_mask, 0)
+
+    # Calculate mean for each token's attention matrix
+    # Number of off-diagonal elements per matrix is n*n - n
+    num_off_diag = num_frames * num_frames - num_frames
+    mean_scores = attn_wo_diag.sum(dim=(1, 2)) / num_off_diag
+
+    enhance_scores = mean_scores.mean() * (num_frames + get_enhance_weight())
+    enhance_scores = enhance_scores.clamp(min=1)
+    return enhance_scores
diff --git a/models/cogvideox/enhance_a_video/globals.py b/models/cogvideox/enhance_a_video/globals.py
new file mode 100644
index 0000000..50d0da2
--- /dev/null
+++ b/models/cogvideox/enhance_a_video/globals.py
@@ -0,0 +1,31 @@
+NUM_FRAMES = None
+FETA_WEIGHT = None
+ENABLE_FETA = False
+
+def set_num_frames(num_frames: int):
+    global NUM_FRAMES
+    NUM_FRAMES = num_frames
+
+
+def get_num_frames() -> int:
+    return NUM_FRAMES
+
+
+def enable_enhance():
+    global ENABLE_FETA
+    ENABLE_FETA = True
+
+def disable_enhance():
+    global ENABLE_FETA
+    ENABLE_FETA = False
+
+def is_enhance_enabled() -> bool:
+    return ENABLE_FETA
+
+def set_enhance_weight(feta_weight: float):
+    global FETA_WEIGHT
+    FETA_WEIGHT = feta_weight
+
+
+def get_enhance_weight() -> float:
+    return FETA_WEIGHT
diff --git a/nodes.py b/nodes.py
index 64798fa..5bded8c 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1,14 +1,11 @@
 import math
 import torch
-import numpy as np
 
 from torch import Tensor
 from unittest.mock import patch
 
-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.lightricks.model import precompute_freqs_cis
 from comfy.ldm.common_dit import rms_norm
 
 
@@ -471,6 +468,7 @@ class TeaCacheForImgGen:
         }
     
     RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("model",)
     FUNCTION = "apply_teacache"
     CATEGORY = "TeaCache"
     TITLE = "TeaCache For Img Gen"
@@ -517,6 +515,7 @@ class TeaCacheForVidGen:
         }
     
     RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("model",)
     FUNCTION = "apply_teacache"
     CATEGORY = "TeaCache"
     TITLE = "TeaCache For Vid Gen"
@@ -571,6 +570,7 @@ class CompileModel:
         }
     
     RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("model",)
     FUNCTION = "apply_compile"
     CATEGORY = "TeaCache"
     TITLE = "Compile Model"
@@ -596,3 +596,5 @@ NODE_CLASS_MAPPINGS = {
     "TeaCacheForVidGen": TeaCacheForVidGen,
     "CompileModel": CompileModel
 }
+
+NODE_DISPLAY_NAME_MAPPINGS = {k: v.TITLE for k, v in NODE_CLASS_MAPPINGS.items()}
diff --git a/nodes_diffusers.py b/nodes_diffusers.py
new file mode 100644
index 0000000..c2aa3ca
--- /dev/null
+++ b/nodes_diffusers.py
@@ -0,0 +1,297 @@
+import torch
+import numpy as np
+
+from einops import rearrange
+from typing import Optional, Tuple, Union
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+
+from .models.cogvideox.custom_cogvideox_transformer_3d import CogVideoXTransformer3DModel
+from .models.cogvideox.enhance_a_video.globals import set_num_frames
+
+def poly1d(coefficients, x):
+    result = torch.zeros_like(x)
+    for i, coeff in enumerate(coefficients):
+        result += coeff * (x ** (len(coefficients) - 1 - i))
+    return result.abs()
+
+def fft(tensor):
+    tensor_fft = torch.fft.fft2(tensor)
+    tensor_fft_shifted = torch.fft.fftshift(tensor_fft)
+    B, C, H, W = tensor.size()
+    radius = min(H, W) // 5
+            
+    Y, X = torch.meshgrid(torch.arange(H), torch.arange(W))
+    center_x, center_y = W // 2, H // 2
+    mask = (X - center_x) ** 2 + (Y - center_y) ** 2 <= radius ** 2
+    low_freq_mask = mask.unsqueeze(0).unsqueeze(0).to(tensor.device)
+    high_freq_mask = ~low_freq_mask
+            
+    low_freq_fft = tensor_fft_shifted * low_freq_mask
+    high_freq_fft = tensor_fft_shifted * high_freq_mask
+
+    return low_freq_fft, high_freq_fft
+
+def teacache_cogvideox_forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: Union[int, float, torch.LongTensor],
+        timestep_cond: Optional[torch.Tensor] = None,
+        ofs: Optional[Union[int, float, torch.LongTensor]] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        controlnet_states: torch.Tensor = None,
+        controlnet_weights: Optional[Union[float, int, list, np.ndarray, torch.FloatTensor]] = 1.0,
+        video_flow_features: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ):
+        batch_size, num_frames, channels, height, width = hidden_states.shape
+
+        set_num_frames(num_frames) # enhance a video global
+   
+        # 1. Time embedding
+        timesteps = timestep
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=hidden_states.dtype)
+        emb = self.time_embedding(t_emb, timestep_cond)
+        if self.ofs_embedding is not None: #1.5 I2V
+            ofs_emb = self.ofs_proj(ofs)
+            ofs_emb = ofs_emb.to(dtype=hidden_states.dtype)
+            ofs_emb = self.ofs_embedding(ofs_emb)
+            emb = emb + ofs_emb
+
+        # 2. Patch embedding
+        p = self.config.patch_size
+        p_t = self.config.patch_size_t
+
+        hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
+        hidden_states = self.embedding_dropout(hidden_states)
+
+        text_seq_length = encoder_hidden_states.shape[1]
+        encoder_hidden_states = hidden_states[:, :text_seq_length]
+        hidden_states = hidden_states[:, text_seq_length:]
+
+        # enable teacache
+        if not hasattr(self, 'accumulated_rel_l1_distance'):
+            should_calc = True
+            self.accumulated_rel_l1_distance = 0
+        else:
+            try:
+                if not self.config.use_rotary_positional_embeddings:
+                    # CogVideoX-2B
+                    coefficients = [-3.10658903e+01, 2.54732368e+01, -5.92380459e+00, 1.75769064e+00, -3.61568434e-03]
+                else:
+                    # CogVideoX-5B
+                    coefficients = [-1.53880483e+03, 8.43202495e+02, -1.34363087e+02, 7.97131516e+00, -5.23162339e-02]
+                
+                self.accumulated_rel_l1_distance += poly1d(coefficients, ((emb-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()))
+                if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
+                    should_calc = False
+                else:
+                    should_calc = True
+                    self.accumulated_rel_l1_distance = 0
+            except:
+                should_calc = True
+                self.accumulated_rel_l1_distance = 0
+
+        self.previous_modulated_input = emb
+
+        if self.use_fastercache:
+            self.fastercache_counter += 1
+        if self.fastercache_counter >= self.fastercache_start_step + 3 and self.fastercache_counter % 5 != 0:
+            if not should_calc:
+                hidden_states += self.previous_residual
+                encoder_hidden_states += self.previous_residual_encoder
+            else:
+                ori_hidden_states = hidden_states.clone()
+                ori_encoder_hidden_states = encoder_hidden_states.clone()
+                # 3. Transformer blocks
+                for i, block in enumerate(self.transformer_blocks):
+                    hidden_states, encoder_hidden_states = block(
+                        hidden_states=hidden_states[:1],
+                        encoder_hidden_states=encoder_hidden_states[:1],
+                        temb=emb[:1],
+                        image_rotary_emb=image_rotary_emb,
+                        video_flow_feature=video_flow_features[i][:1] if video_flow_features is not None else None,
+                        fuser = self.fuser_list[i] if self.fuser_list is not None else None,
+                        block_use_fastercache = i <= self.fastercache_num_blocks_to_cache,
+                        fastercache_counter = self.fastercache_counter,
+                        fastercache_start_step = self.fastercache_start_step,
+                        fastercache_device = self.fastercache_device
+                    )
+
+                    if (controlnet_states is not None) and (i < len(controlnet_states)):
+                        controlnet_states_block = controlnet_states[i]
+                        controlnet_block_weight = 1.0
+                        if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
+                            controlnet_block_weight = controlnet_weights[i]
+                        elif isinstance(controlnet_weights, (float, int)):
+                            controlnet_block_weight = controlnet_weights
+                        
+                        hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
+                self.previous_residual = hidden_states - ori_hidden_states
+                self.previous_residual_encoder = encoder_hidden_states - ori_encoder_hidden_states
+                    
+            if not self.config.use_rotary_positional_embeddings:
+                # CogVideoX-2B
+                hidden_states = self.norm_final(hidden_states)
+            else:
+                # CogVideoX-5B
+                hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+                hidden_states = self.norm_final(hidden_states)
+                hidden_states = hidden_states[:, text_seq_length:]
+
+            # 4. Final block
+            hidden_states = self.norm_out(hidden_states, temb=emb[:1])
+            hidden_states = self.proj_out(hidden_states)
+
+            # 5. Unpatchify
+            # Note: we use `-1` instead of `channels`:
+            #   - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
+            #   - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
+            
+            if p_t is None:
+                output = hidden_states.reshape(1, num_frames, height // p, width // p, -1, p, p)
+                output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
+            else:
+                output = hidden_states.reshape(
+                    1, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
+                )
+                output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
+            
+            (bb, tt, cc, hh, ww) = output.shape
+            cond = rearrange(output, "B T C H W -> (B T) C H W", B=bb, C=cc, T=tt, H=hh, W=ww)
+            lf_c, hf_c = fft(cond.float())
+
+            if self.fastercache_counter <= self.fastercache_lf_step:
+                self.delta_lf = self.delta_lf * 1.1
+            if self.fastercache_counter >= self.fastercache_hf_step:
+                self.delta_hf = self.delta_hf * 1.1
+
+            new_hf_uc = self.delta_hf + hf_c
+            new_lf_uc = self.delta_lf + lf_c
+
+            combine_uc = new_lf_uc + new_hf_uc
+            combined_fft = torch.fft.ifftshift(combine_uc)
+            recovered_uncond = torch.fft.ifft2(combined_fft).real
+            recovered_uncond = rearrange(recovered_uncond.to(output.dtype), "(B T) C H W -> B T C H W", B=bb, C=cc, T=tt, H=hh, W=ww)
+            output = torch.cat([output, recovered_uncond])
+        else:
+            if not should_calc:
+                hidden_states += self.previous_residual
+                encoder_hidden_states += self.previous_residual_encoder
+            else:
+                ori_hidden_states = hidden_states.clone()
+                ori_encoder_hidden_states = encoder_hidden_states.clone()
+                for i, block in enumerate(self.transformer_blocks):
+                    hidden_states, encoder_hidden_states = block(
+                        hidden_states=hidden_states,
+                        encoder_hidden_states=encoder_hidden_states,
+                        temb=emb,
+                        image_rotary_emb=image_rotary_emb,
+                        video_flow_feature=video_flow_features[i] if video_flow_features is not None else None,
+                        fuser = self.fuser_list[i] if self.fuser_list is not None else None,
+                        block_use_fastercache = i <= self.fastercache_num_blocks_to_cache,
+                        fastercache_counter = self.fastercache_counter,
+                        fastercache_start_step = self.fastercache_start_step,
+                        fastercache_device = self.fastercache_device
+                    )
+
+                    # controlnet
+                    if (controlnet_states is not None) and (i < len(controlnet_states)):
+                        controlnet_states_block = controlnet_states[i]
+                        controlnet_block_weight = 1.0
+                        if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
+                            controlnet_block_weight = controlnet_weights[i]
+                            print(controlnet_block_weight)
+                        elif isinstance(controlnet_weights, (float, int)):
+                            controlnet_block_weight = controlnet_weights                    
+                        hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
+                self.previous_residual = hidden_states - ori_hidden_states
+                self.previous_residual_encoder = encoder_hidden_states - ori_encoder_hidden_states
+                    
+            if not self.config.use_rotary_positional_embeddings:
+                # CogVideoX-2B
+                hidden_states = self.norm_final(hidden_states)
+            else:
+                # CogVideoX-5B
+                hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+                hidden_states = self.norm_final(hidden_states)
+                hidden_states = hidden_states[:, text_seq_length:]
+
+            # 4. Final block
+            hidden_states = self.norm_out(hidden_states, temb=emb)
+            hidden_states = self.proj_out(hidden_states)
+
+            # 5. Unpatchify
+            # Note: we use `-1` instead of `channels`:
+            #   - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
+            #   - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
+            
+            if p_t is None:
+                output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
+                output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
+            else:
+                output = hidden_states.reshape(
+                    batch_size, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
+                )
+                output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
+
+            if self.fastercache_counter >= self.fastercache_start_step + 1: 
+                (bb, tt, cc, hh, ww) = output.shape
+                cond = rearrange(output[0:1].float(), "B T C H W -> (B T) C H W", B=bb//2, C=cc, T=tt, H=hh, W=ww)
+                uncond = rearrange(output[1:2].float(), "B T C H W -> (B T) C H W", B=bb//2, C=cc, T=tt, H=hh, W=ww)
+
+                lf_c, hf_c = fft(cond)
+                lf_uc, hf_uc = fft(uncond)
+
+                self.delta_lf = lf_uc - lf_c
+                self.delta_hf = hf_uc - hf_c
+
+        if not return_dict:
+            return (output,)
+        return Transformer2DModelOutput(sample=output)
+
+class TeaCacheForCogVideoX:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("COGVIDEOMODEL", {"tooltip": "The CogVideoX model the TeaCache will be applied to."}),
+                "enable_teacache": ("BOOLEAN", {"default": True, "tooltip": "Enable teacache will speed up inference but may lose visual quality."}),
+                "rel_l1_thresh": ("FLOAT", {"default": 0.3, "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."})
+            }
+        }
+    
+    RETURN_TYPES = ("COGVIDEOMODEL",)
+    RETURN_NAMES = ("model",)
+    FUNCTION = "apply_teacache"
+    CATEGORY = "TeaCache"
+    TITLE = "TeaCache For CogVideoX"
+    
+    def apply_teacache(self, model, enable_teacache: bool, rel_l1_thresh: float):
+        if enable_teacache:
+            transformer = model["pipe"].transformer
+            transformer.__class__.rel_l1_thresh = rel_l1_thresh
+            transformer.forward = teacache_cogvideox_forward.__get__(
+                                transformer,
+                                transformer.__class__
+                            )
+        else:
+            transformer = model["pipe"].transformer
+            transformer.forward = CogVideoXTransformer3DModel.forward.__get__(
+                                transformer,
+                                transformer.__class__
+                            )
+        
+        return (model,)
+    
+
+NODE_CLASS_MAPPINGS = {
+    "TeaCacheForCogVideoX": TeaCacheForCogVideoX
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {k: v.TITLE for k, v in NODE_CLASS_MAPPINGS.items()}
diff --git a/pyproject.toml b/pyproject.toml
index 75a4572..0e31289 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "teacache"
 description = "Unofficial implementation of [ali-vilab/TeaCache](https://github.com/ali-vilab/TeaCache) for ComfyUI"
-version = "1.1.2"
+version = "1.2.0"
 license = {file = "LICENSE"}
 
 [project.urls]