Update Training Readme

app.py

@@ -24,11 +24,6 @@ if __name__ == "__main__":
     # EasyAnimateV4, V5 support "model_cpu_offload" "model_cpu_offload_and_qfloat8" "sequential_cpu_offload"
     # EasyAnimateV5.1 support "model_cpu_offload" "model_cpu_offload_and_qfloat8" 
     GPU_memory_mode = "model_cpu_offload_and_qfloat8"
-    # EasyAnimateV5.1 support TeaCache.
-    enable_teacache     = True
-    # Recommended to be set between 0.05 and 0.1. A larger threshold can cache more steps, speeding up the inference process, 
-    # but it may cause slight differences between the generated content and the original content.
-    teacache_threshold  = 0.1
     # Use torch.float16 if GPU does not support torch.bfloat16
     # ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
     weight_dtype = torch.bfloat16
@@ -49,11 +44,11 @@ if __name__ == "__main__":
     savedir_sample = "samples"
 
     if ui_mode == "modelscope":
-        demo, controller = ui_modelscope(model_type, edition, config_path, model_name, savedir_sample, GPU_memory_mode, enable_teacache, teacache_threshold, weight_dtype)
+        demo, controller = ui_modelscope(model_type, edition, config_path, model_name, savedir_sample, GPU_memory_mode, weight_dtype)
     elif ui_mode == "eas":
         demo, controller = ui_eas(edition, config_path, model_name, savedir_sample)
     else:
-        demo, controller = ui(GPU_memory_mode, enable_teacache, teacache_threshold, weight_dtype)
+        demo, controller = ui(GPU_memory_mode, weight_dtype)
 
     # launch gradio
     app, _, _ = demo.queue(status_update_rate=1).launch(

comfyui/comfyui_nodes.py

@@ -442,7 +442,7 @@ class EasyAnimateT2VSampler:
     FUNCTION = "process"
     CATEGORY = "EasyAnimateWrapper"
 
-    def process(self, easyanimate_model, prompt, negative_prompt, video_length, width, height, is_image, seed, steps, cfg, scheduler, teacache_threshold=0.10, enable_teacache=False):
+    def process(self, easyanimate_model, prompt, negative_prompt, video_length, width, height, is_image, seed, steps, cfg, scheduler):
         global transformer_cpu_cache
         global lora_path_before
         device = mm.get_torch_device()
@@ -459,9 +459,6 @@ class EasyAnimateT2VSampler:
         # Load Sampler
         pipeline.scheduler = all_cheduler_dict[scheduler].from_pretrained(model_name, subfolder='scheduler')
 
-        if enable_teacache:
-            pipeline.transformer.enable_teacache(steps, teacache_threshold)
-
         generator= torch.Generator(device).manual_seed(seed)
         
         video_length = 1 if is_image else video_length
@@ -591,8 +588,6 @@ class EasyAnimateV5_T2VSampler(EasyAnimateT2VSampler):
                         "default": 'Flow'
                     }
                 ),
-                "teacache_threshold": ("FLOAT", {"default": 0.10, "min": 0.00, "max": 1.00, "step": 0.005}),
-                "enable_teacache":([False, True],  {"default": True,}),
             },
         }
 
@@ -654,7 +649,7 @@ class EasyAnimateI2VSampler:
     FUNCTION = "process"
     CATEGORY = "EasyAnimateWrapper"
 
-    def process(self, easyanimate_model, prompt, negative_prompt, video_length, base_resolution, seed, steps, cfg, scheduler, start_img=None, end_img=None, teacache_threshold=0.10, enable_teacache=False):
+    def process(self, easyanimate_model, prompt, negative_prompt, video_length, base_resolution, seed, steps, cfg, scheduler, start_img=None, end_img=None):
         global transformer_cpu_cache
         global lora_path_before
         device = mm.get_torch_device()
@@ -679,9 +674,6 @@ class EasyAnimateI2VSampler:
         # Load Sampler
         pipeline.scheduler = all_cheduler_dict[scheduler].from_pretrained(model_name, subfolder='scheduler')
 
-        if enable_teacache:
-            pipeline.transformer.enable_teacache(steps, teacache_threshold)
-
         generator= torch.Generator(device).manual_seed(seed)
 
         with torch.no_grad():
@@ -788,9 +780,7 @@ class EasyAnimateV5_I2VSampler(EasyAnimateI2VSampler):
                     {
                         "default": 'Flow'
                     }
-                ),
-                "teacache_threshold": ("FLOAT", {"default": 0.10, "min": 0.00, "max": 1.00, "step": 0.005}),
-                "enable_teacache":([False, True],  {"default": True,}),
+                )
             },
             "optional":{
                 "start_img": ("IMAGE",),
@@ -859,7 +849,7 @@ class EasyAnimateV2VSampler:
     FUNCTION = "process"
     CATEGORY = "EasyAnimateWrapper"
 
-    def process(self, easyanimate_model, prompt, negative_prompt, video_length, base_resolution, seed, steps, cfg, denoise_strength, scheduler, validation_video=None, control_video=None, ref_image=None, camera_conditions=None, teacache_threshold=0.10, enable_teacache=False):
+    def process(self, easyanimate_model, prompt, negative_prompt, video_length, base_resolution, seed, steps, cfg, denoise_strength, scheduler, validation_video=None, control_video=None, ref_image=None, camera_conditions=None):
         global transformer_cpu_cache
         global lora_path_before
 
@@ -902,9 +892,6 @@ class EasyAnimateV2VSampler:
         # Load Sampler
         pipeline.scheduler = all_cheduler_dict[scheduler].from_pretrained(model_name, subfolder='scheduler')
 
-        if enable_teacache:
-            pipeline.transformer.enable_teacache(steps, teacache_threshold)
-
         generator= torch.Generator(device).manual_seed(seed)
 
         with torch.no_grad():
@@ -1046,8 +1033,6 @@ class EasyAnimateV5_V2VSampler(EasyAnimateV2VSampler):
                         "default": 'Flow'
                     }
                 ),
-                "teacache_threshold": ("FLOAT", {"default": 0.10, "min": 0.00, "max": 1.00, "step": 0.005}),
-                "enable_teacache":([False, True],  {"default": True,}),
             },
             "optional":{
                 "validation_video": ("IMAGE",),

comfyui/v5.1/easyanimatev5.1_workflow_control_camera.json

@@ -214,6 +214,89 @@
         "bf16"
       ]
     },
+    {
+      "id": 131,
+      "type": "EasyAnimateV5_V2VSampler",
+      "pos": {
+        "0": 821,
+        "1": 242
+      },
+      "size": {
+        "0": 504,
+        "1": 350
+      },
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "easyanimate_model",
+          "type": "EASYANIMATESMODEL",
+          "link": 271
+        },
+        {
+          "name": "prompt",
+          "type": "STRING_PROMPT",
+          "link": 272
+        },
+        {
+          "name": "negative_prompt",
+          "type": "STRING_PROMPT",
+          "link": 273
+        },
+        {
+          "name": "validation_video",
+          "type": "IMAGE",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "control_video",
+          "type": "IMAGE",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "ref_image",
+          "type": "IMAGE",
+          "link": 274,
+          "shape": 7
+        },
+        {
+          "name": "camera_conditions",
+          "type": "STRING",
+          "link": 275,
+          "widget": {
+            "name": "camera_conditions"
+          },
+          "shape": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "links": [
+            276
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EasyAnimateV5_V2VSampler"
+      },
+      "widgets_values": [
+        49,
+        512,
+        43,
+        "fixed",
+        43,
+        6,
+        1,
+        "Flow",
+        ""
+      ]
+    },
     {
       "id": 129,
       "type": "CameraTrajectoryFromChaoJie",
@@ -371,7 +454,7 @@
       },
       "size": [
         390,
-        546
+        535.4285714285714
       ],
       "flags": {},
       "order": 13,
@@ -449,10 +532,10 @@
         "0": 819,
         "1": 658
       },
-      "size": {
-        "0": 517.6458129882812,
-        "1": 93.61251831054688
-      },
+      "size": [
+        517.6458089787227,
+        93.61251593411134
+      ],
       "flags": {},
       "order": 10,
       "mode": 0,
@@ -466,91 +549,6 @@
       ],
       "color": "#432",
       "bgcolor": "#653"
-    },
-    {
-      "id": 131,
-      "type": "EasyAnimateV5_V2VSampler",
-      "pos": {
-        "0": 822,
-        "1": 211
-      },
-      "size": {
-        "0": 504,
-        "1": 394
-      },
-      "flags": {},
-      "order": 12,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "easyanimate_model",
-          "type": "EASYANIMATESMODEL",
-          "link": 271
-        },
-        {
-          "name": "prompt",
-          "type": "STRING_PROMPT",
-          "link": 272
-        },
-        {
-          "name": "negative_prompt",
-          "type": "STRING_PROMPT",
-          "link": 273
-        },
-        {
-          "name": "validation_video",
-          "type": "IMAGE",
-          "link": null,
-          "shape": 7
-        },
-        {
-          "name": "control_video",
-          "type": "IMAGE",
-          "link": null,
-          "shape": 7
-        },
-        {
-          "name": "ref_image",
-          "type": "IMAGE",
-          "link": 274,
-          "shape": 7
-        },
-        {
-          "name": "camera_conditions",
-          "type": "STRING",
-          "link": 275,
-          "widget": {
-            "name": "camera_conditions"
-          },
-          "shape": 7
-        }
-      ],
-      "outputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "links": [
-            276
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "EasyAnimateV5_V2VSampler"
-      },
-      "widgets_values": [
-        49,
-        512,
-        43,
-        "fixed",
-        43,
-        6,
-        1,
-        "Flow",
-        0.10,
-        true,
-        ""
-      ]
     }
   ],
   "links": [
@@ -664,16 +662,11 @@
   "config": {},
   "extra": {
     "ds": {
-      "scale": 1.1,
+      "scale": 0.6209213230591558,
       "offset": [
-        -465.8996857769304,
-        51.92597569190605
+        417.7460035994012,
+        -70.36580413723722
       ]
-    },
-    "node_versions": {
-      "EasyAnimate": "de24d49f07f6d9b12b4e98de98ec959a8b44b989",
-      "comfy-core": "v0.2.7-3-g8afb97c",
-      "ComfyUI-VideoHelperSuite": "70faa9bcef65932ab72e7404d6373fb300013a2e"
     }
   },
   "version": 0.4

comfyui/v5.1/easyanimatev5.1_workflow_control_trajectory.json

@@ -211,6 +211,86 @@
       "color": "#432",
       "bgcolor": "#653"
     },
+    {
+      "id": 106,
+      "type": "VHS_VideoCombine",
+      "pos": {
+        "0": 1497,
+        "1": 204
+      },
+      "size": [
+        390,
+        546
+      ],
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 261,
+          "slot_index": 0,
+          "label": "图像",
+          "shape": 7
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "link": null,
+          "label": "音频",
+          "shape": 7
+        },
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": null,
+          "label": "批次管理",
+          "shape": 7
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null,
+          "shape": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null,
+          "slot_index": 0,
+          "shape": 3,
+          "label": "文件名"
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 8,
+        "loop_count": 0,
+        "filename_prefix": "EasyAnimate",
+        "format": "video/h264-mp4",
+        "pix_fmt": "yuv420p",
+        "crf": 22,
+        "save_metadata": true,
+        "pingpong": false,
+        "save_output": true,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "filename": "EasyAnimate_00105.mp4",
+            "subfolder": "",
+            "type": "output",
+            "format": "video/h264-mp4",
+            "frame_rate": 8
+          }
+        }
+      }
+    },
     {
       "id": 99,
       "type": "LoadEasyAnimateModel",
@@ -241,12 +321,96 @@
       },
       "widgets_values": [
         "EasyAnimateV5.1-12b-zh-Control",
-        "model_cpu_offload_and_qfloat8",
+        "model_cpu_offload",
         "Control",
         "easyanimate_video_v5.1_magvit_qwen.yaml",
         "bf16"
       ]
     },
+    {
+      "id": 111,
+      "type": "EasyAnimateV5_V2VSampler",
+      "pos": {
+        "0": 905,
+        "1": 201
+      },
+      "size": {
+        "0": 504,
+        "1": 350
+      },
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "easyanimate_model",
+          "type": "EASYANIMATESMODEL",
+          "link": 256
+        },
+        {
+          "name": "prompt",
+          "type": "STRING_PROMPT",
+          "link": 257
+        },
+        {
+          "name": "negative_prompt",
+          "type": "STRING_PROMPT",
+          "link": 258
+        },
+        {
+          "name": "validation_video",
+          "type": "IMAGE",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "name": "control_video",
+          "type": "IMAGE",
+          "link": 259,
+          "shape": 7
+        },
+        {
+          "name": "ref_image",
+          "type": "IMAGE",
+          "link": 260,
+          "shape": 7
+        },
+        {
+          "name": "camera_conditions",
+          "type": "STRING",
+          "link": null,
+          "widget": {
+            "name": "camera_conditions"
+          },
+          "shape": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "links": [
+            261,
+            263
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EasyAnimateV5_V2VSampler"
+      },
+      "widgets_values": [
+        49,
+        512,
+        43,
+        "fixed",
+        50,
+        6,
+        1,
+        "Flow",
+        ""
+      ]
+    },
     {
       "id": 97,
       "type": "SplineEditor",
@@ -334,7 +498,7 @@
       },
       "size": [
         530,
-        310
+        650.4
       ],
       "flags": {},
       "order": 12,
@@ -457,10 +621,10 @@
         "0": 1140.1396484375,
         "1": 909.9193115234375
       },
-      "size": {
-        "0": 315,
-        "1": 82
-      },
+      "size": [
+        315,
+        82
+      ],
       "flags": {
         "collapsed": false
       },
@@ -551,7 +715,7 @@
       },
       "size": [
         530,
-        310
+        630
       ],
       "flags": {},
       "order": 16,
@@ -626,10 +790,10 @@
         "0": 1544.139404296875,
         "1": 1047.919189453125
       },
-      "size": {
-        "0": 645,
-        "1": 812
-      },
+      "size": [
+        645,
+        812
+      ],
       "flags": {},
       "order": 9,
       "mode": 0,
@@ -694,172 +858,6 @@
         null,
         null
       ]
-    },
-    {
-      "id": 111,
-      "type": "EasyAnimateV5_V2VSampler",
-      "pos": {
-        "0": 876,
-        "1": 147
-      },
-      "size": {
-        "0": 504,
-        "1": 394
-      },
-      "flags": {},
-      "order": 13,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "easyanimate_model",
-          "type": "EASYANIMATESMODEL",
-          "link": 256
-        },
-        {
-          "name": "prompt",
-          "type": "STRING_PROMPT",
-          "link": 257
-        },
-        {
-          "name": "negative_prompt",
-          "type": "STRING_PROMPT",
-          "link": 258
-        },
-        {
-          "name": "validation_video",
-          "type": "IMAGE",
-          "link": null,
-          "shape": 7
-        },
-        {
-          "name": "control_video",
-          "type": "IMAGE",
-          "link": 259,
-          "shape": 7
-        },
-        {
-          "name": "ref_image",
-          "type": "IMAGE",
-          "link": 260,
-          "shape": 7
-        },
-        {
-          "name": "camera_conditions",
-          "type": "STRING",
-          "link": null,
-          "widget": {
-            "name": "camera_conditions"
-          },
-          "shape": 7
-        }
-      ],
-      "outputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "links": [
-            261,
-            263
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "EasyAnimateV5_V2VSampler"
-      },
-      "widgets_values": [
-        49,
-        512,
-        43,
-        "fixed",
-        50,
-        6,
-        1,
-        "Flow",
-        0.10,
-        true,
-        ""
-      ]
-    },
-    {
-      "id": 106,
-      "type": "VHS_VideoCombine",
-      "pos": {
-        "0": 1496,
-        "1": 153
-      },
-      "size": [
-        390,
-        310
-      ],
-      "flags": {},
-      "order": 14,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 261,
-          "slot_index": 0,
-          "label": "图像",
-          "shape": 7
-        },
-        {
-          "name": "audio",
-          "type": "AUDIO",
-          "link": null,
-          "label": "音频",
-          "shape": 7
-        },
-        {
-          "name": "meta_batch",
-          "type": "VHS_BatchManager",
-          "link": null,
-          "label": "批次管理",
-          "shape": 7
-        },
-        {
-          "name": "vae",
-          "type": "VAE",
-          "link": null,
-          "shape": 7
-        }
-      ],
-      "outputs": [
-        {
-          "name": "Filenames",
-          "type": "VHS_FILENAMES",
-          "links": null,
-          "slot_index": 0,
-          "shape": 3,
-          "label": "文件名"
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VHS_VideoCombine"
-      },
-      "widgets_values": {
-        "frame_rate": 8,
-        "loop_count": 0,
-        "filename_prefix": "EasyAnimate",
-        "format": "video/h264-mp4",
-        "pix_fmt": "yuv420p",
-        "crf": 22,
-        "save_metadata": true,
-        "pingpong": false,
-        "save_output": true,
-        "videopreview": {
-          "hidden": false,
-          "paused": false,
-          "params": {
-            "filename": "EasyAnimate_00105.mp4",
-            "subfolder": "",
-            "type": "output",
-            "format": "video/h264-mp4",
-            "frame_rate": 8
-          }
-        }
-      }
     }
   ],
   "links": [
@@ -1029,17 +1027,11 @@
   "config": {},
   "extra": {
     "ds": {
-      "scale": 1.1,
+      "scale": 0.5054470284993016,
       "offset": [
-        -465.8996857769304,
-        51.92597569190605
+        -239.62344289721364,
+        114.98345539297088
       ]
-    },
-    "node_versions": {
-      "EasyAnimate": "de24d49f07f6d9b12b4e98de98ec959a8b44b989",
-      "comfy-core": "v0.2.7-3-g8afb97c",
-      "ComfyUI-KJNodes": "4c5c26a2c91de356212419ac8bc7fcf9869527e9",
-      "ComfyUI-VideoHelperSuite": "70faa9bcef65932ab72e7404d6373fb300013a2e"
     }
   },
   "version": 0.4

comfyui/v5.1/easyanimatev5.1_workflow_i2v.json

@@ -289,9 +289,7 @@
         "fixed",
         50,
         6,
-        "Flow",
-        0.10,
-        true
+        "Flow"
       ]
     },
     {

comfyui/v5.1/easyanimatev5.1_workflow_t2v.json

@@ -84,9 +84,7 @@
         "fixed",
         50,
         6,
-        "Flow",
-        0.10,
-        true
+        "Flow"
       ]
     },
     {

comfyui/v5.1/easyanimatev5.1_workflow_v2v.json

@@ -305,8 +305,6 @@
         6,
         0.7000000000000001,
         "Flow",
-        0.10,
-        true,
         ""
       ]
     },

comfyui/v5.1/easyanimatev5.1_workflow_v2v_control.json

@@ -154,7 +154,7 @@
       },
       "size": [
         390.9534912109375,
-        546.5720947265625
+        973.1686096191406
       ],
       "flags": {},
       "order": 9,
@@ -234,7 +234,7 @@
       },
       "size": {
         "0": 336,
-        "1": 394
+        "1": 350
       },
       "flags": {},
       "order": 8,
@@ -275,15 +275,6 @@
           "type": "IMAGE",
           "link": null,
           "shape": 7
-        },
-        {
-          "name": "camera_conditions",
-          "type": "STRING",
-          "link": null,
-          "widget": {
-            "name": "camera_conditions"
-          },
-          "shape": 7
         }
       ],
       "outputs": [
@@ -309,8 +300,6 @@
         6,
         1,
         "Flow",
-        0.10,
-        true,
         ""
       ]
     },
@@ -360,7 +349,7 @@
       },
       "size": [
         252.056640625,
-        262
+        685.7
       ],
       "flags": {},
       "order": 6,
@@ -443,10 +432,10 @@
         "0": -192,
         "1": -293
       },
-      "size": {
-        "0": 427.074951171875,
-        "1": 143.9142608642578
-      },
+      "size": [
+        427.074951171875,
+        143.9142608642578
+      ],
       "flags": {},
       "order": 7,
       "mode": 0,
@@ -506,14 +495,14 @@
   ],
   "groups": [
     {
-      "title": "Upload Your Video",
+      "title": "Prompts",
       "bounding": [
         218,
-        385,
-        487,
-        789
+        -127,
+        450,
+        483
       ],
-      "color": "#a1309b",
+      "color": "#3f789e",
       "font_size": 24,
       "flags": {}
     },
@@ -530,14 +519,14 @@
       "flags": {}
     },
     {
-      "title": "Prompts",
+      "title": "Upload Your Video",
       "bounding": [
         218,
-        -127,
-        450,
-        483
+        385,
+        487,
+        789
       ],
-      "color": "#3f789e",
+      "color": "#a1309b",
       "font_size": 24,
       "flags": {}
     }
@@ -545,18 +534,14 @@
   "config": {},
   "extra": {
     "ds": {
-      "scale": 0.8264462809917354,
+      "scale": 0.5644739300537782,
       "offset": [
-        -156.13347668602108,
-        275.2525393282698
+        634.4708817322136,
+        478.05663679245043
       ]
     },
     "workspace_info": {
       "id": "776b62b4-bd17-4ed3-9923-b7aad000b1ea"
-    },
-    "node_versions": {
-      "EasyAnimate": "de24d49f07f6d9b12b4e98de98ec959a8b44b989",
-      "ComfyUI-VideoHelperSuite": "70faa9bcef65932ab72e7404d6373fb300013a2e"
     }
   },
   "version": 0.4

comfyui/v5/easyanimatev5_workflow_v2v.json

@@ -226,21 +226,6 @@
           "type": "IMAGE",
           "link": null,
           "shape": 7
-        },
-        {
-          "name": "ref_image",
-          "type": "IMAGE",
-          "link": null,
-          "shape": 7
-        },
-        {
-          "name": "camera_conditions",
-          "type": "STRING",
-          "link": null,
-          "widget": {
-            "name": "camera_conditions"
-          },
-          "shape": 7
         }
       ],
       "outputs": [
@@ -265,10 +250,7 @@
         35,
         7,
         0.7,
-        "DDIM",
-        0.10,
-        true,
-        ""
+        "DDIM"
       ]
     },
     {

comfyui/v5/easyanimatev5_workflow_v2v_control.json

@@ -391,21 +391,6 @@
           "type": "IMAGE",
           "link": 53,
           "shape": 7
-        },
-        {
-          "name": "ref_image",
-          "type": "IMAGE",
-          "link": null,
-          "shape": 7
-        },
-        {
-          "name": "camera_conditions",
-          "type": "STRING",
-          "link": null,
-          "widget": {
-            "name": "camera_conditions"
-          },
-          "shape": 7
         }
       ],
       "outputs": [
@@ -430,10 +415,7 @@
         35,
         6,
         1,
-        "DDIM",
-        0.10,
-        true,
-        ""
+        "DDIM"
       ]
     },
     {

easyanimate/models/transformer3d.py

@@ -87,40 +87,6 @@ class Transformer3DModelOutput(BaseOutput):
     sample: torch.FloatTensor
 
 
-class TeaCache():
-    """
-    Timestep Embedding Aware Cache, a training-free caching approach that estimates and leverages
-    the fluctuating differences among model outputs across timesteps, thereby accelerating the inference.
-    Please refer to:
-    1. https://github.com/ali-vilab/TeaCache.
-    2. Liu, Feng, et al. "Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model." arXiv preprint arXiv:2411.19108 (2024).
-    """
-    def __init__(self, coefficients: list[float], num_steps: int, rel_l1_thresh: float = 0.0):
-        if num_steps < 1:
-            raise ValueError("`num_steps` must be greater than 0 but is {num_steps}.")
-        if rel_l1_thresh < 0:
-            raise ValueError("`rel_l1_thresh` must be greater than or equal to 0 but is {rel_l1_thresh}.")
-        self.coefficients = coefficients
-        self.cnt = 0
-        self.num_steps = num_steps
-        self.rel_l1_thresh = rel_l1_thresh
-        self.accumulated_rel_l1_distance = 0
-        self.previous_modulated_input = None
-        self.previous_residual = None
-        self.rescale_func = np.poly1d(self.coefficients)
-    
-    @staticmethod
-    def compute_rel_l1_distance(prev, cur):
-        rel_l1_distance = (torch.abs(cur - prev).mean()) / torch.abs(prev).mean()
-
-        return rel_l1_distance.cpu().item()
-    
-    def reset(self):
-        self.cnt = 0
-        self.previous_modulated_input = None
-        self.previous_residual = None
-
-
 class Transformer3DModel(ModelMixin, ConfigMixin):
     """
     A 3D Transformer model for image-like data.
@@ -1462,21 +1428,7 @@ class EasyAnimateTransformer3DModel(ModelMixin, ConfigMixin):
         )
         self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * out_channels)
 
-        self.teacache = None
-
         self.gradient_checkpointing = False
-    
-    def enable_teacache(
-        self,
-        num_steps: int,
-        rel_l1_thresh: float,
-        coefficients: list[float] = [-10.47857366, 8.33844143, -0.78477557, 0.68798618, 0.0136149]
-    ):
-        # The coefficient was obtained by sampling videos from T2V CompBench using EasyAnimateV5.1-12b-zh-InP.
-        # This coefficient can be applied to both the EasyAnimateV5.1-12b-zh and EasyAnimateV5.1-12b-Control.
-        # The coefficients for EasyAnimateV5.1-7b-zh-InP should be:
-        # [-3.64204720e+03, 1.43764725e+03, -1.93045263e+02, 1.09596499e+01, -1.70663507e-01]
-        self.teacache = TeaCache(coefficients, num_steps, rel_l1_thresh=rel_l1_thresh)
 
     def _set_gradient_checkpointing(self, module, value=False):
         self.gradient_checkpointing = value
@@ -1547,122 +1499,48 @@ class EasyAnimateTransformer3DModel(ModelMixin, ConfigMixin):
             clip_encoder_hidden_states = self.clip_proj(clip_encoder_hidden_states)
             
             encoder_hidden_states = torch.concat([clip_encoder_hidden_states, ref_latents], dim=1)
-        
-        # TeaCache
-        if self.teacache is not None:
-            inp = hidden_states.clone()
-            temb_ = temb.clone()
-            encoder_hidden_states_ = encoder_hidden_states.clone()
-            modulated_inp, _, _, _ = self.transformer_blocks[0].norm1(inp, encoder_hidden_states_, temb_)
-            if self.teacache.cnt == 0 or self.teacache.cnt == self.teacache.num_steps - 1:
-                should_calc = True
-                self.teacache.accumulated_rel_l1_distance = 0
+
+        # 4. Transformer blocks
+        for i, block in enumerate(self.transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    video_length,
+                    height // self.patch_size,
+                    width // self.patch_size,
+                    **ckpt_kwargs,
+                )
             else:
-                rel_l1_distance = self.teacache.compute_rel_l1_distance(self.teacache.previous_modulated_input, modulated_inp)
-                self.teacache.accumulated_rel_l1_distance += self.teacache.rescale_func(rel_l1_distance)
-                if self.teacache.accumulated_rel_l1_distance < self.teacache.rel_l1_thresh:
-                    should_calc = False
-                else:
-                    should_calc = True
-                    self.teacache.accumulated_rel_l1_distance = 0
-            self.teacache.previous_modulated_input = modulated_inp
-            self.teacache.cnt += 1
-            if self.teacache.cnt == self.teacache.num_steps:
-                # self.cnt = 0
-                self.teacache.reset()
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    num_frames=video_length,
+                    height=height // self.patch_size,
+                    width=width // self.patch_size
+                )
 
-        # TeaCache
-        if self.teacache is not None:
-            if not should_calc:
-                hidden_states += self.teacache.previous_residual
-            else:
-                ori_hidden_states = hidden_states.clone()
-
-                # 4. Transformer blocks
-                for i, block in enumerate(self.transformer_blocks):
-                    if torch.is_grad_enabled() and self.gradient_checkpointing:
-                        def create_custom_forward(module, return_dict=None):
-                            def custom_forward(*inputs):
-                                if return_dict is not None:
-                                    return module(*inputs, return_dict=return_dict)
-                                else:
-                                    return module(*inputs)
-
-                            return custom_forward
-
-                        ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                        hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
-                            create_custom_forward(block),
-                            hidden_states,
-                            encoder_hidden_states,
-                            temb,
-                            image_rotary_emb,
-                            video_length,
-                            height // self.patch_size,
-                            width // self.patch_size,
-                            **ckpt_kwargs,
-                        )
-                    else:
-                        hidden_states, encoder_hidden_states = block(
-                            hidden_states=hidden_states,
-                            encoder_hidden_states=encoder_hidden_states,
-                            temb=temb,
-                            image_rotary_emb=image_rotary_emb,
-                            num_frames=video_length,
-                            height=height // self.patch_size,
-                            width=width // self.patch_size
-                        )
-
-                hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-                hidden_states = self.norm_final(hidden_states)
-                hidden_states = hidden_states[:, encoder_hidden_states.size()[1]:]
-
-                # 5. Final block
-                hidden_states = self.norm_out(hidden_states, temb=temb)
-                self.teacache.previous_residual = hidden_states - ori_hidden_states
-        else:
-            # 4. Transformer blocks
-            for i, block in enumerate(self.transformer_blocks):
-                if torch.is_grad_enabled() and self.gradient_checkpointing:
-                    def create_custom_forward(module, return_dict=None):
-                        def custom_forward(*inputs):
-                            if return_dict is not None:
-                                return module(*inputs, return_dict=return_dict)
-                            else:
-                                return module(*inputs)
-
-                        return custom_forward
-
-                    ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                    hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(block),
-                        hidden_states,
-                        encoder_hidden_states,
-                        temb,
-                        image_rotary_emb,
-                        video_length,
-                        height // self.patch_size,
-                        width // self.patch_size,
-                        **ckpt_kwargs,
-                    )
-                else:
-                    hidden_states, encoder_hidden_states = block(
-                        hidden_states=hidden_states,
-                        encoder_hidden_states=encoder_hidden_states,
-                        temb=temb,
-                        image_rotary_emb=image_rotary_emb,
-                        num_frames=video_length,
-                        height=height // self.patch_size,
-                        width=width // self.patch_size
-                    )
-
-            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-            hidden_states = self.norm_final(hidden_states)
-            hidden_states = hidden_states[:, encoder_hidden_states.size()[1]:]
-
-            # 5. Final block
-            hidden_states = self.norm_out(hidden_states, temb=temb)
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+        hidden_states = self.norm_final(hidden_states)
+        hidden_states = hidden_states[:, encoder_hidden_states.size()[1]:]
 
+        # 5. Final block
+        hidden_states = self.norm_out(hidden_states, temb=temb)
         hidden_states = self.proj_out(hidden_states)
 
         # 6. Unpatchify

easyanimate/ui/ui.py

@@ -67,7 +67,7 @@ css = """
 """
 
 class EasyAnimateController:
-    def __init__(self, GPU_memory_mode, enable_teacache, teacache_threshold, weight_dtype):
+    def __init__(self, GPU_memory_mode, weight_dtype):
         # config dirs
         self.basedir                    = os.getcwd()
         self.config_dir                 = os.path.join(self.basedir, "config")
@@ -97,8 +97,6 @@ class EasyAnimateController:
         self.base_model_path       = "none"
         self.lora_model_path       = "none"
         self.GPU_memory_mode       = GPU_memory_mode
-        self.enable_teacache       = enable_teacache
-        self.teacache_threshold    = teacache_threshold
         
         self.weight_dtype          = weight_dtype
         self.edition               = "v5.1"
@@ -464,9 +462,6 @@ class EasyAnimateController:
             # lora part
             self.pipeline = merge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
         
-        if self.edition == "v5.1" and self.enable_teacache:
-            self.pipeline.transformer.enable_teacache(sample_step_slider, self.teacache_threshold)
-        
         try:
             if self.model_type == "Inpaint":
                 if self.transformer.config.in_channels != self.vae.config.latent_channels:
@@ -666,8 +661,8 @@ class EasyAnimateController:
                     return gr.Image.update(visible=False, value=None), gr.Video.update(value=save_sample_path, visible=True), "Success"
 
 
-def ui(GPU_memory_mode, enable_teacache, teacache_threshold, weight_dtype):
-    controller = EasyAnimateController(GPU_memory_mode, enable_teacache, teacache_threshold, weight_dtype)
+def ui(GPU_memory_mode, weight_dtype):
+    controller = EasyAnimateController(GPU_memory_mode, weight_dtype)
 
     with gr.Blocks(css=css) as demo:
         gr.Markdown(
@@ -1005,7 +1000,7 @@ def ui(GPU_memory_mode, enable_teacache, teacache_threshold, weight_dtype):
 
 
 class EasyAnimateController_Modelscope:
-    def __init__(self, model_type, edition, config_path, model_name, savedir_sample, GPU_memory_mode, enable_teacache, teacache_threshold, weight_dtype):
+    def __init__(self, model_type, edition, config_path, model_name, savedir_sample, GPU_memory_mode, weight_dtype):
         # Basic dir
         self.basedir                    = os.getcwd()
         self.personalized_model_dir     = os.path.join(self.basedir, "models", "Personalized_Model")
@@ -1017,8 +1012,6 @@ class EasyAnimateController_Modelscope:
         # Config and model path
         self.model_type = model_type
         self.edition = edition
-        self.enable_teacache = enable_teacache
-        self.teacache_threshold = teacache_threshold
         self.weight_dtype = weight_dtype
         self.inference_config = OmegaConf.load(config_path)
         Choosen_AutoencoderKL = name_to_autoencoder_magvit[
@@ -1266,10 +1259,6 @@ class EasyAnimateController_Modelscope:
             # lora part
             self.pipeline = merge_lora(self.pipeline, self.lora_model_path, multiplier=lora_alpha_slider)
         
-        if self.edition == "v5.1" and self.enable_teacache:
-            print(f"Enable TeaCache with threshold: {self.teacache_threshold}.")
-            self.pipeline.transformer.enable_teacache(sample_step_slider, self.teacache_threshold)
-        
         try:
             if self.model_type == "Inpaint":
                 if self.vae.cache_mag_vae:
@@ -1384,8 +1373,8 @@ class EasyAnimateController_Modelscope:
                     return gr.Image.update(visible=False, value=None), gr.Video.update(value=save_sample_path, visible=True), "Success"
 
 
-def ui_modelscope(model_type, edition, config_path, model_name, savedir_sample, GPU_memory_mode, enable_teacache, teacache_threshold, weight_dtype):
-    controller = EasyAnimateController_Modelscope(model_type, edition, config_path, model_name, savedir_sample, GPU_memory_mode, enable_teacache, teacache_threshold, weight_dtype)
+def ui_modelscope(model_type, edition, config_path, model_name, savedir_sample, GPU_memory_mode, weight_dtype):
+    controller = EasyAnimateController_Modelscope(model_type, edition, config_path, model_name, savedir_sample, GPU_memory_mode, weight_dtype)
 
     with gr.Blocks(css=css) as demo:
         gr.Markdown(

predict_i2v.py

@@ -33,11 +33,6 @@ from easyanimate.utils.utils import get_image_to_video_latent, save_videos_grid
 # EasyAnimateV4, V5 support "model_cpu_offload" "model_cpu_offload_and_qfloat8" "sequential_cpu_offload"
 # EasyAnimateV5.1 support "model_cpu_offload" "model_cpu_offload_and_qfloat8" 
 GPU_memory_mode     = "model_cpu_offload_and_qfloat8"
-# EasyAnimateV5.1 support TeaCache.
-enable_teacache     = True
-# Recommended to be set between 0.05 and 0.1. A larger threshold can cache more steps, speeding up the inference process, 
-# but it may cause slight differences between the generated content and the original content.
-teacache_threshold  = 0.1
 
 # Config and model path
 config_path         = "config/easyanimate_video_v5.1_magvit_qwen.yaml"
@@ -253,10 +248,6 @@ elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
 else:
     pipeline.enable_model_cpu_offload()
 
-if "v5.1" in config_path and enable_teacache:
-    print(f"Enable TeaCache with threshold: {teacache_threshold}.")
-    pipeline.transformer.enable_teacache(num_inference_steps, teacache_threshold)
-
 generator = torch.Generator(device="cuda").manual_seed(seed)
 
 if lora_path is not None:

predict_t2v.py

@@ -35,11 +35,6 @@ from easyanimate.utils.utils import get_image_to_video_latent, save_videos_grid
 # EasyAnimateV4, V5 support "model_cpu_offload" "model_cpu_offload_and_qfloat8" "sequential_cpu_offload"
 # EasyAnimateV5.1 support "model_cpu_offload" "model_cpu_offload_and_qfloat8" 
 GPU_memory_mode     = "model_cpu_offload_and_qfloat8"
-# EasyAnimateV5.1 support TeaCache.
-enable_teacache     = True
-# Recommended to be set between 0.05 and 0.1. A larger threshold can cache more steps, speeding up the inference process, 
-# but it may cause slight differences between the generated content and the original content.
-teacache_threshold  = 0.1
 
 # Config and model path
 config_path         = "config/easyanimate_video_v5.1_magvit_qwen.yaml"
@@ -261,10 +256,6 @@ elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
 else:
     pipeline.enable_model_cpu_offload()
 
-if "v5.1" in config_path and enable_teacache:
-    print(f"Enable TeaCache with threshold: {teacache_threshold}.")
-    pipeline.transformer.enable_teacache(num_inference_steps, teacache_threshold)
-
 generator = torch.Generator(device="cuda").manual_seed(seed)
 
 if lora_path is not None:

predict_v2v.py

@@ -34,11 +34,6 @@ from easyanimate.utils.utils import (get_video_to_video_latent,
 # EasyAnimateV4, V5 support "model_cpu_offload" "model_cpu_offload_and_qfloat8" "sequential_cpu_offload"
 # EasyAnimateV5.1 support "model_cpu_offload" "model_cpu_offload_and_qfloat8" 
 GPU_memory_mode     = "model_cpu_offload_and_qfloat8"
-# EasyAnimateV5.1 support TeaCache.
-enable_teacache     = True
-# Recommended to be set between 0.05 and 0.1. A larger threshold can cache more steps, speeding up the inference process, 
-# but it may cause slight differences between the generated content and the original content.
-teacache_threshold  = 0.1
 
 # Config and model path
 config_path         = "config/easyanimate_video_v5.1_magvit_qwen.yaml"
@@ -248,10 +243,6 @@ elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
 else:
     pipeline.enable_model_cpu_offload()
 
-if "v5.1" in config_path and enable_teacache:
-    print(f"Enable TeaCache with threshold: {teacache_threshold}.")
-    pipeline.transformer.enable_teacache(num_inference_steps, teacache_threshold)
-
 generator = torch.Generator(device="cuda").manual_seed(seed)
 
 if lora_path is not None:

predict_v2v_control.py

@@ -34,11 +34,6 @@ from diffusers import FlowMatchEulerDiscreteScheduler
 # EasyAnimateV5 support "model_cpu_offload" "model_cpu_offload_and_qfloat8" "sequential_cpu_offload"
 # EasyAnimateV5.1 support "model_cpu_offload" "model_cpu_offload_and_qfloat8" 
 GPU_memory_mode     = "model_cpu_offload_and_qfloat8"
-# EasyAnimateV5.1 support TeaCache.
-enable_teacache     = True
-# Recommended to be set between 0.05 and 0.1. A larger threshold can cache more steps, speeding up the inference process, 
-# but it may cause slight differences between the generated content and the original content.
-teacache_threshold  = 0.1
 
 # Config and model path
 config_path         = "config/easyanimate_video_v5.1_magvit_qwen.yaml"
@@ -233,10 +228,6 @@ elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
 else:
     pipeline.enable_model_cpu_offload()
 
-if "v5.1" in config_path and enable_teacache:
-    print(f"Enable TeaCache with threshold: {teacache_threshold}.")
-    pipeline.transformer.enable_teacache(num_inference_steps, teacache_threshold)
-
 generator = torch.Generator(device="cuda").manual_seed(seed)
 
 if lora_path is not None: