small fixes

hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py

@@ -727,16 +727,16 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                     )
                     latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=1)
 
-                if embedded_guidance_scale is not None and not cfg_enabled:
+                if self.do_classifier_free_guidance:
                     guidance_expand = (
-                        torch.tensor(
-                            [embedded_guidance_scale] * latent_model_input.shape[0],
-                            dtype=self.base_dtype,
-                            device=device,
-                        ) * 1000.0
+                        torch.tensor([embedded_guidance_scale] * latents.shape[0] * 2, dtype=self.base_dtype, device=device)
+                        * 1000.0
                     )
                 else:
-                    guidance_expand = None
+                    guidance_expand = (
+                        torch.tensor([embedded_guidance_scale] * latents.shape[0], dtype=self.base_dtype, device=device)
+                        * 1000.0
+                    )
 
                 if use_context_schedule:
                     counter = torch.zeros_like(latent_model_input)

hyvideo/modules/attention.py

@@ -9,7 +9,7 @@ except ImportError:
     flash_attn_varlen_func = None
 
 try:
-    from sageattention import sageattn_varlen
+    from sageattention import sageattn_varlen, sageattn
     @torch.compiler.disable()
     def sageattn_varlen_func(
             q,
@@ -21,6 +21,9 @@ try:
             max_seqlen_kv,
         ):
         return sageattn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv)
+    @torch.compiler.disable()
+    def sageattn_func(q, k, v, attn_mask=None, dropout_p=0, is_causal=False):
+        return sageattn(q, k, v, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal)
 except ImportError:
     sageattn_varlen_func = None
 
@@ -39,6 +42,10 @@ MEMORY_LAYOUT = {
         lambda x: x.transpose(1, 2),
         lambda x: x.transpose(1, 2),
     ),
+    "sageattn": (
+        lambda x: x.transpose(1, 2),
+        lambda x: x.transpose(1, 2),
+    ),
     "comfy": (
         lambda x: x.transpose(1, 2),
         lambda x: x.transpose(1, 2),
@@ -174,7 +181,10 @@ def attention(
         )  # reshape x to [b, s, a, d]
     elif mode == "comfy":
         x = optimized_attention(q, k, v, mask=attn_mask, heads=heads, skip_reshape=True)
-      
+    elif mode == "sageattn":
+        if attn_mask is not None and attn_mask.dtype != torch.bool:
+            attn_mask = attn_mask.to(q.dtype)
+        x = sageattn_func(q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal)
     elif mode == "flash_attn_varlen":
         x = flash_attn_varlen_func(
             q,

hyvideo/modules/models.py

@@ -967,7 +967,7 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
         img_seq_len = img.shape[1]
         max_seqlen_q = max_seqlen_kv = img_seq_len + txt_seq_len
 
-        if self.attention_mode == "sdpa" or self.attention_mode == "comfy":
+        if "varlen" not in self.attention_mode:
             cu_seqlens_q, cu_seqlens_kv = None, None
             # Create a square boolean mask filled with False
             attn_mask = torch.zeros((1, max_seqlen_q, max_seqlen_q), dtype=torch.bool, device=text_mask.device)

nodes.py

@@ -289,6 +289,7 @@ class HyVideoModelLoader:
                     "sdpa",
                     "flash_attn_varlen",
                     "sageattn_varlen",
+                    "sageattn",
                     "comfy",
                     ], {"default": "flash_attn"}),
                 "compile_args": ("COMPILEARGS", ),
@@ -1000,8 +1001,8 @@ class HyVideoCFG:
         return {"required": {
             "negative_prompt": ("STRING", {"default": "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion", "multiline": True} ),
             "cfg": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.01, "tooltip": "guidance scale"} ),
-            "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.01, "tooltip": "Start percentage of the steps to apply CFG, rest of the steps use guidance_embeds"} ),
-            "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "tooltip": "End percentage of the steps to apply CFG, rest of the steps use guidance_embeds"} ),
+            "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage of the steps to apply CFG, rest of the steps use guidance_embeds"} ),
+            "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage of the steps to apply CFG, rest of the steps use guidance_embeds"} ),
             },
         }