update hunyuan cache (#79)

Co-authored-by: Linboyan-trc <1584340372@qq.com>

lightx2v/models/networks/hunyuan/model.py

@@ -7,8 +7,12 @@ from lightx2v.models.networks.hunyuan.weights.transformer_weights import Hunyuan
 from lightx2v.models.networks.hunyuan.infer.pre_infer import HunyuanPreInfer
 from lightx2v.models.networks.hunyuan.infer.post_infer import HunyuanPostInfer
 from lightx2v.models.networks.hunyuan.infer.transformer_infer import HunyuanTransformerInfer
-from lightx2v.models.networks.hunyuan.infer.feature_caching.transformer_infer import HunyuanTransformerInferTaylorCaching, HunyuanTransformerInferTeaCaching
-
+from lightx2v.models.networks.hunyuan.infer.feature_caching.transformer_infer import (
+    HunyuanTransformerInferTaylorCaching,
+    HunyuanTransformerInferTeaCaching,
+    HunyuanTransformerInferAdaCaching,
+    HunyuanTransformerInferCustomCaching,
+)
 import lightx2v.attentions.distributed.ulysses.wrap as ulysses_dist_wrap
 import lightx2v.attentions.distributed.ring.wrap as ring_dist_wrap
 from lightx2v.utils.envs import *
@@ -156,10 +160,6 @@ class HunyuanModel:
         if self.config["cpu_offload"]:
             self.pre_weight.to_cpu()
             self.post_weight.to_cpu()
-        if self.config["feature_caching"] == "Tea":
-            self.scheduler.cnt += 1
-            if self.scheduler.cnt == self.scheduler.num_steps:
-                self.scheduler.cnt = 0
 
     def _init_infer_class(self):
         self.pre_infer_class = HunyuanPreInfer
@@ -170,5 +170,9 @@ class HunyuanModel:
             self.transformer_infer_class = HunyuanTransformerInferTaylorCaching
         elif self.config["feature_caching"] == "Tea":
             self.transformer_infer_class = HunyuanTransformerInferTeaCaching
+        elif self.config["feature_caching"] == "Ada":
+            self.transformer_infer_class = HunyuanTransformerInferAdaCaching
+        elif self.config["feature_caching"] == "Custom":
+            self.transformer_infer_class = HunyuanTransformerInferCustomCaching
         else:
             raise NotImplementedError(f"Unsupported feature_caching type: {self.config['feature_caching']}")

lightx2v/models/networks/wan/infer/feature_caching/transformer_infer.py

@@ -305,13 +305,23 @@ class WanTransformerInferTaylorCaching(WanTransformerInfer, BaseTaylorCachingTra
         for cache in self.blocks_cache_even:
             for key in cache:
                 if cache[key] is not None:
+                    if isinstance(cache[key], torch.Tensor):
                         cache[key] = cache[key].cpu()
+                    elif isinstance(cache[key], dict):
+                        for k, v in cache[key].items():
+                            if isinstance(v, torch.Tensor):
+                                cache[key][k] = v.cpu()
             cache.clear()
 
         for cache in self.blocks_cache_odd:
             for key in cache:
                 if cache[key] is not None:
+                    if isinstance(cache[key], torch.Tensor):
                         cache[key] = cache[key].cpu()
+                    elif isinstance(cache[key], dict):
+                        for k, v in cache[key].items():
+                            if isinstance(v, torch.Tensor):
+                                cache[key][k] = v.cpu()
             cache.clear()
         torch.cuda.empty_cache()
 

lightx2v/models/networks/wan/model.py

@@ -62,7 +62,7 @@ class WanModel:
             self.transformer_infer_class = WanTransformerInfer
         elif self.config["feature_caching"] == "Tea":
             self.transformer_infer_class = WanTransformerInferTeaCaching
-        elif self.config["feature_caching"] == "Taylor":
+        elif self.config["feature_caching"] == "TaylorSeer":
             self.transformer_infer_class = WanTransformerInferTaylorCaching
         elif self.config["feature_caching"] == "Ada":
             self.transformer_infer_class = WanTransformerInferAdaCaching

lightx2v/models/runners/hunyuan/hunyuan_runner.py

@@ -6,7 +6,7 @@ from PIL import Image
 from lightx2v.utils.registry_factory import RUNNER_REGISTER
 from lightx2v.models.runners.default_runner import DefaultRunner
 from lightx2v.models.schedulers.hunyuan.scheduler import HunyuanScheduler
-from lightx2v.models.schedulers.hunyuan.feature_caching.scheduler import HunyuanSchedulerTaylorCaching, HunyuanSchedulerTeaCaching
+from lightx2v.models.schedulers.hunyuan.feature_caching.scheduler import HunyuanSchedulerTaylorCaching, HunyuanSchedulerTeaCaching, HunyuanSchedulerAdaCaching, HunyuanSchedulerCustomCaching
 from lightx2v.models.input_encoders.hf.llama.model import TextEncoderHFLlamaModel
 from lightx2v.models.input_encoders.hf.clip.model import TextEncoderHFClipModel
 from lightx2v.models.input_encoders.hf.llava.model import TextEncoderHFLlavaModel
@@ -47,6 +47,10 @@ class HunyuanRunner(DefaultRunner):
             scheduler = HunyuanSchedulerTeaCaching(self.config)
         elif self.config.feature_caching == "TaylorSeer":
             scheduler = HunyuanSchedulerTaylorCaching(self.config)
+        elif self.config.feature_caching == "Ada":
+            scheduler = HunyuanSchedulerAdaCaching(self.config)
+        elif self.config.feature_caching == "Custom":
+            scheduler = HunyuanSchedulerCustomCaching(self.config)
         else:
             raise NotImplementedError(f"Unsupported feature_caching type: {self.config.feature_caching}")
         self.model.set_scheduler(scheduler)

lightx2v/models/runners/wan/wan_runner.py

@@ -117,7 +117,7 @@ class WanRunner(DefaultRunner):
             scheduler = WanScheduler(self.config)
         elif self.config.feature_caching == "Tea":
             scheduler = WanSchedulerTeaCaching(self.config)
-        elif self.config.feature_caching == "Taylor":
+        elif self.config.feature_caching == "TaylorSeer":
             scheduler = WanSchedulerTaylorCaching(self.config)
         elif self.config.feature_caching == "Ada":
             scheduler = WanSchedulerAdaCaching(self.config)

lightx2v/models/schedulers/hunyuan/feature_caching/scheduler.py

-from .utils import cache_init, cal_type
 from ..scheduler import HunyuanScheduler
 import torch
 
@@ -6,31 +5,32 @@ import torch
 class HunyuanSchedulerTeaCaching(HunyuanScheduler):
     def __init__(self, config):
         super().__init__(config)
-        self.cnt = 0
-        self.num_steps = self.config.infer_steps
-        self.teacache_thresh = self.config.teacache_thresh
-        self.accumulated_rel_l1_distance = 0
-        self.previous_modulated_input = None
-        self.previous_residual = None
-        self.coefficients = [7.33226126e02, -4.01131952e02, 6.75869174e01, -3.14987800e00, 9.61237896e-02]
 
     def clear(self):
-        if self.previous_residual is not None:
-            self.previous_residual = self.previous_residual.cpu()
-        if self.previous_modulated_input is not None:
-            self.previous_modulated_input = self.previous_modulated_input.cpu()
-
-        self.previous_modulated_input = None
-        self.previous_residual = None
-        torch.cuda.empty_cache()
+        self.transformer_infer.clear()
 
 
 class HunyuanSchedulerTaylorCaching(HunyuanScheduler):
     def __init__(self, config):
         super().__init__(config)
-        self.cache_dic, self.current = cache_init(self.infer_steps)
+        pattern = [True, False, False, False]
+        self.caching_records = (pattern * ((config.infer_steps + 3) // 4))[: config.infer_steps]
+
+    def clear(self):
+        self.transformer_infer.clear()
+
+
+class HunyuanSchedulerAdaCaching(HunyuanScheduler):
+    def __init__(self, config):
+        super().__init__(config)
 
-    def step_pre(self, step_index):
-        super().step_pre(step_index)
-        self.current["step"] = step_index
-        cal_type(self.cache_dic, self.current)
+    def clear(self):
+        self.transformer_infer.clear()
+
+
+class HunyuanSchedulerCustomCaching(HunyuanScheduler):
+    def __init__(self, config):
+        super().__init__(config)
+
+    def clear(self):
+        self.transformer_infer.clear()

lightx2v/models/schedulers/hunyuan/scheduler.py

@@ -237,7 +237,6 @@ def get_1d_rotary_pos_embed_riflex(
 class HunyuanScheduler(BaseScheduler):
     def __init__(self, config):
         super().__init__(config)
-        self.infer_steps = self.config.infer_steps
         self.shift = 7.0
         self.timesteps, self.sigmas = set_timesteps_sigmas(self.infer_steps, self.shift, device=torch.device("cuda"))
         assert len(self.timesteps) == self.infer_steps