Fix offload bugs and support wan2.2_vae offload

Fix offload bugs and support wan2.2_vae offload

lightx2v/models/networks/wan/model.py

@@ -37,6 +37,9 @@ class WanModel:
     def __init__(self, model_path, config, device):
         self.model_path = model_path
         self.config = config
+        self.cpu_offload = self.config.get("cpu_offload", False)
+        self.offload_granularity = self.config.get("offload_granularity", "block")
+
         self.clean_cuda_cache = self.config.get("clean_cuda_cache", False)
         self.dit_quantized = self.config.mm_config.get("mm_type", "Default") != "Default"
 
@@ -202,15 +205,18 @@ class WanModel:
 
     @torch.no_grad()
     def infer(self, inputs):
+        if self.cpu_offload:
+            if self.offload_granularity == "model" and self.scheduler.step_index == 0:
+                self.to_cuda()
+            elif self.offload_granularity != "model":
+                self.pre_weight.to_cuda()
+                self.post_weight.to_cuda()
+
         if self.transformer_infer.mask_map is None:
             _, c, h, w = self.scheduler.latents.shape
             video_token_num = c * (h // 2) * (w // 2)
             self.transformer_infer.mask_map = MaskMap(video_token_num, c)
 
-        if self.config.get("cpu_offload", False):
-            self.pre_weight.to_cuda()
-            self.post_weight.to_cuda()
-
         embed, grid_sizes, pre_infer_out = self.pre_infer.infer(self.pre_weight, inputs, positive=True)
         x = self.transformer_infer.infer(self.transformer_weights, grid_sizes, embed, *pre_infer_out)
         noise_pred_cond = self.post_infer.infer(self.post_weight, x, embed, grid_sizes)[0]
@@ -228,14 +234,17 @@ class WanModel:
 
             self.scheduler.noise_pred = noise_pred_uncond + self.scheduler.sample_guide_scale * (self.scheduler.noise_pred - noise_pred_uncond)
 
-            if self.config.get("cpu_offload", False):
+            if self.clean_cuda_cache:
+                del x, embed, pre_infer_out, noise_pred_uncond, grid_sizes
+                torch.cuda.empty_cache()
+
+        if self.cpu_offload:
+            if self.offload_granularity == "model" and self.scheduler.step_index == self.scheduler.infer_steps - 1:
+                self.to_cpu()
+            elif self.offload_granularity != "model":
                 self.pre_weight.to_cpu()
                 self.post_weight.to_cpu()
 
-                if self.clean_cuda_cache:
-                    del x, embed, pre_infer_out, noise_pred_uncond, grid_sizes
-                    torch.cuda.empty_cache()
-
 
 class Wan22MoeModel(WanModel):
     def _load_ckpt(self, use_bf16, skip_bf16):
@@ -248,6 +257,10 @@ class Wan22MoeModel(WanModel):
 
     @torch.no_grad()
     def infer(self, inputs):
+        if self.cpu_offload and self.offload_granularity != "model":
+            self.pre_weight.to_cuda()
+            self.post_weight.to_cuda()
+
         embed, grid_sizes, pre_infer_out = self.pre_infer.infer(self.pre_weight, inputs, positive=True)
         x = self.transformer_infer.infer(self.transformer_weights, grid_sizes, embed, *pre_infer_out)
         noise_pred_cond = self.post_infer.infer(self.post_weight, x, embed, grid_sizes)[0]
@@ -260,3 +273,7 @@ class Wan22MoeModel(WanModel):
             noise_pred_uncond = self.post_infer.infer(self.post_weight, x, embed, grid_sizes)[0]
 
             self.scheduler.noise_pred = noise_pred_uncond + self.scheduler.sample_guide_scale * (self.scheduler.noise_pred - noise_pred_uncond)
+
+        if self.cpu_offload and self.offload_granularity != "model":
+            self.pre_weight.to_cpu()
+            self.post_weight.to_cpu()

lightx2v/models/runners/wan/wan_runner.py

@@ -418,5 +418,5 @@ class Wan22DenseRunner(WanRunner):
         return vae_encoder_out
 
     def get_vae_encoder_output(self, img):
-        z = self.vae_encoder.encode(img)
+        z = self.vae_encoder.encode(img, self.config)
         return z

lightx2v/models/video_encoders/hf/wan/vae_2_2.py

@@ -844,7 +844,7 @@ class Wan2_2_VAE:
         self.dtype = dtype
         self.device = device
 
-        mean = torch.tensor(
+        self.mean = torch.tensor(
             [
                 -0.2289,
                 -0.0052,
@@ -898,7 +898,7 @@ class Wan2_2_VAE:
             dtype=dtype,
             device=device,
         )
-        std = torch.tensor(
+        self.std = torch.tensor(
             [
                 0.4765,
                 1.0364,
@@ -952,8 +952,8 @@ class Wan2_2_VAE:
             dtype=dtype,
             device=device,
         )
-        self.scale = [mean, 1.0 / std]
-
+        self.inv_std = 1.0 / self.std
+        self.scale = [self.mean, self.inv_std]
         # init model
         self.model = (
             _video_vae(
@@ -968,25 +968,35 @@ class Wan2_2_VAE:
             .to(device)
         )
 
-    def encode(self, videos):
-        # try:
-        #     if not isinstance(videos, list):
-        #         raise TypeError("videos should be a list")
-        #     with amp.autocast(dtype=self.dtype):
-        #         return [
-        #             self.model.encode(u.unsqueeze(0),
-        #                               self.scale).float().squeeze(0)
-        #             for u in videos
-        #         ]
-        # except TypeError as e:
-        #     logging.info(e)
-        #     return None
-
-        # print(1111111)
-        # print(self.model.encode(videos.unsqueeze(0), self.scale).float().shape)
-        # exit()
-
-        return self.model.encode(videos.unsqueeze(0), self.scale).float().squeeze(0)
+    def to_cpu(self):
+        self.model.encoder = self.model.encoder.to("cpu")
+        self.model.decoder = self.model.decoder.to("cpu")
+        self.model = self.model.to("cpu")
+        self.mean = self.mean.cpu()
+        self.inv_std = self.inv_std.cpu()
+        self.scale = [self.mean, self.inv_std]
+
+    def to_cuda(self):
+        self.model.encoder = self.model.encoder.to("cuda")
+        self.model.decoder = self.model.decoder.to("cuda")
+        self.model = self.model.to("cuda")
+        self.mean = self.mean.cuda()
+        self.inv_std = self.inv_std.cuda()
+        self.scale = [self.mean, self.inv_std]
+
+    def encode(self, videos, args):
+        if hasattr(args, "cpu_offload") and args.cpu_offload:
+            self.to_cuda()
+        out = self.model.encode(videos.unsqueeze(0), self.scale).float().squeeze(0)
+        if hasattr(args, "cpu_offload") and args.cpu_offload:
+            self.to_cpu()
+        return out
 
     def decode(self, zs, generator, config):
-        return self.model.decode(zs.unsqueeze(0), self.scale).float().clamp_(-1, 1)
+        if config.cpu_offload:
+            self.to_cuda()
+        images = self.model.decode(zs.unsqueeze(0), self.scale).float().clamp_(-1, 1)
+        if config.cpu_offload:
+            images = images.cpu().float()
+            self.to_cpu()
+        return images