Add the support for fp8 t5

README.md

@@ -137,6 +137,12 @@ python generate.py  --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B
 
 You can also use the `--fp8` option to enable FP8 precision for reduced memory usage. Make sure to download the [FP8 model weight](https://huggingface.co/Kijai/WanVideo_comfy/resolve/main/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors) and place it in the `Wan2.1-T2V-1.3B` folder.
 
+Additionally, an [FP8 version of the T5 model](https://huggingface.co/Kijai/WanVideo_comfy/resolve/main/umt5-xxl-enc-fp8_e4m3fn.safetensors) is available. To use the FP8 T5 model, update the configuration file:
+
+```
+t2v_1_3B.t5_checkpoint = 'umt5-xxl-enc-fp8_e4m3fn.safetensors'
+```
+
 > 💡Note: If you are using the `T2V-1.3B` model, we recommend setting the parameter `--sample_guide_scale 6`. The `--sample_shift parameter` can be adjusted within the range of 8 to 12 based on the performance.
 
 

wan/configs/wan_i2v_14B.py

@@ -10,6 +10,7 @@ i2v_14B = EasyDict(__name__='Config: Wan I2V 14B')
 i2v_14B.update(wan_shared_cfg)
 
 i2v_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth'
+# i2v_14B.t5_checkpoint = 'umt5-xxl-enc-fp8_e4m3fn.safetensors' # fp8 model
 i2v_14B.t5_tokenizer = 'google/umt5-xxl'
 
 # clip

wan/configs/wan_t2v_14B.py

@@ -10,6 +10,7 @@ t2v_14B.update(wan_shared_cfg)
 
 # t5
 t2v_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth'
+# t2v_14B.t5_checkpoint = 'umt5-xxl-enc-fp8_e4m3fn.safetensors' # fp8 model
 t2v_14B.t5_tokenizer = 'google/umt5-xxl'
 
 # vae

wan/configs/wan_t2v_1_3B.py

@@ -10,6 +10,7 @@ t2v_1_3B.update(wan_shared_cfg)
 
 # t5
 t2v_1_3B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth'
+# t2v_1_3B.t5_checkpoint = 'umt5-xxl-enc-fp8_e4m3fn.safetensors' # fp8 model
 t2v_1_3B.t5_tokenizer = 'google/umt5-xxl'
 
 # vae

wan/image2video.py

@@ -80,6 +80,10 @@ class WanI2V:
         self.param_dtype = config.param_dtype
 
         shard_fn = partial(shard_model, device_id=device_id)
+        if config.t5_checkpoint == 'umt5-xxl-enc-fp8_e4m3fn.safetensors':
+            quantization = "fp8_e4m3fn" 
+        else:
+            quantization = "disabled"
         self.text_encoder = T5EncoderModel(
             text_len=config.text_len,
             dtype=config.t5_dtype,
@@ -87,6 +91,7 @@ class WanI2V:
             checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint),
             tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer),
             shard_fn=shard_fn if t5_fsdp else None,
+            quantization=quantization,
         )
 
         self.vae_stride = config.vae_stride
@@ -266,11 +271,13 @@ class WanI2V:
         # preprocess
         if not self.t5_cpu:
             self.text_encoder.model.to(self.device)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
                 context = self.text_encoder([input_prompt], self.device)
                 context_null = self.text_encoder([n_prompt], self.device)
             if offload_model:
                 self.text_encoder.model.cpu()
         else:
+            with torch.autocast(device_type="cpu", dtype=torch.bfloat16, enabled=True):
                 context = self.text_encoder([input_prompt], torch.device('cpu'))
                 context_null = self.text_encoder([n_prompt], torch.device('cpu'))
             context = [t.to(self.device) for t in context]

wan/modules/t5.py

@@ -9,6 +9,10 @@ import torch.nn.functional as F
 
 from .tokenizers import HuggingfaceTokenizer
 
+from accelerate import init_empty_weights
+from accelerate.utils import set_module_tensor_to_device
+from safetensors.torch import load_file
+
 __all__ = [
     'T5Model',
     'T5Encoder',
@@ -442,7 +446,7 @@ def _t5(name,
         model = model_cls(**kwargs)
 
     # set device
-    model = model.to(dtype=dtype, device=device)
+    # model = model.to(dtype=dtype, device=device)
 
     # init tokenizer
     if return_tokenizer:
@@ -479,6 +483,7 @@ class T5EncoderModel:
         checkpoint_path=None,
         tokenizer_path=None,
         shard_fn=None,
+        quantization="disabled",
     ):
         self.text_len = text_len
         self.dtype = dtype
@@ -486,14 +491,31 @@ class T5EncoderModel:
         self.checkpoint_path = checkpoint_path
         self.tokenizer_path = tokenizer_path
 
+        
+        logging.info(f'loading {checkpoint_path}')
+        if quantization == "disabled":
             # init model
             model = umt5_xxl(
                 encoder_only=True,
                 return_tokenizer=False,
                 dtype=dtype,
                 device=device).eval().requires_grad_(False)
-        logging.info(f'loading {checkpoint_path}')
             model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
+        elif quantization == "fp8_e4m3fn":
+            with init_empty_weights():
+                model = umt5_xxl(
+                    encoder_only=True,
+                    return_tokenizer=False,
+                    dtype=dtype,
+                    device=device).eval().requires_grad_(False)
+            cast_dtype = torch.float8_e4m3fn
+            state_dict = load_file(checkpoint_path, device="cpu")
+            params_to_keep = {'norm', 'pos_embedding', 'token_embedding'}
+            for name, param in model.named_parameters():
+                dtype_to_use = dtype if any(keyword in name for keyword in params_to_keep) else cast_dtype
+                set_module_tensor_to_device(model, name, device=device, dtype=dtype_to_use, value=state_dict[name])
+            del state_dict
+
         self.model = model
         if shard_fn is not None:
             self.model = shard_fn(self.model, sync_module_states=False)

wan/text2video.py

@@ -74,13 +74,19 @@ class WanT2V:
         self.param_dtype = config.param_dtype
 
         shard_fn = partial(shard_model, device_id=device_id)
+        if config.t5_checkpoint == 'umt5-xxl-enc-fp8_e4m3fn.safetensors':
+            quantization = "fp8_e4m3fn" 
+        else:
+            quantization = "disabled"
+        
         self.text_encoder = T5EncoderModel(
             text_len=config.text_len,
             dtype=config.t5_dtype,
             device=torch.device('cpu'),
             checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint),
             tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer),
-            shard_fn=shard_fn if t5_fsdp else None)
+            shard_fn=shard_fn if t5_fsdp else None,
+            quantization=quantization)
 
         self.vae_stride = config.vae_stride
         self.patch_size = config.patch_size
@@ -221,11 +227,13 @@ class WanT2V:
 
         if not self.t5_cpu:
             self.text_encoder.model.to(self.device)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
                 context = self.text_encoder([input_prompt], self.device)
                 context_null = self.text_encoder([n_prompt], self.device)
             if offload_model:
                 self.text_encoder.model.cpu()
         else:
+            with torch.autocast(device_type="cpu", dtype=torch.bfloat16, enabled=True):
                 context = self.text_encoder([input_prompt], torch.device('cpu'))
                 context_null = self.text_encoder([n_prompt], torch.device('cpu'))
             context = [t.to(self.device) for t in context]