[HunyuanVideo1.5] support step-distilled (#12802)

* support step-distilled

* style

scripts/convert_hunyuan_video1_5_to_diffusers.py

@@ -69,6 +69,11 @@ TRANSFORMER_CONFIGS = {
         "target_size": 960,
         "task_type": "i2v",
     },
+    "480p_i2v_step_distilled": {
+        "target_size": 640,
+        "task_type": "i2v",
+        "use_meanflow": True,
+    },
 }
 
 SCHEDULER_CONFIGS = {
@@ -93,6 +98,9 @@ SCHEDULER_CONFIGS = {
     "720p_i2v_distilled": {
         "shift": 7.0,
     },
+    "480p_i2v_step_distilled": {
+        "shift": 7.0,
+    },
 }
 
 GUIDANCE_CONFIGS = {
@@ -117,6 +125,9 @@ GUIDANCE_CONFIGS = {
     "720p_i2v_distilled": {
         "guidance_scale": 1.0,
     },
+    "480p_i2v_step_distilled": {
+        "guidance_scale": 1.0,
+    },
 }
 
 
@@ -126,7 +137,7 @@ def swap_scale_shift(weight):
     return new_weight
 
 
-def convert_hyvideo15_transformer_to_diffusers(original_state_dict):
+def convert_hyvideo15_transformer_to_diffusers(original_state_dict, config=None):
     """
     Convert HunyuanVideo 1.5 original checkpoint to Diffusers format.
     """
@@ -142,6 +153,20 @@ def convert_hyvideo15_transformer_to_diffusers(original_state_dict):
     )
     converted_state_dict["time_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop("time_in.mlp.2.bias")
 
+    if config.use_meanflow:
+        converted_state_dict["time_embed.timestep_embedder_r.linear_1.weight"] = original_state_dict.pop(
+            "time_r_in.mlp.0.weight"
+        )
+        converted_state_dict["time_embed.timestep_embedder_r.linear_1.bias"] = original_state_dict.pop(
+            "time_r_in.mlp.0.bias"
+        )
+        converted_state_dict["time_embed.timestep_embedder_r.linear_2.weight"] = original_state_dict.pop(
+            "time_r_in.mlp.2.weight"
+        )
+        converted_state_dict["time_embed.timestep_embedder_r.linear_2.bias"] = original_state_dict.pop(
+            "time_r_in.mlp.2.bias"
+        )
+
     # 2. context_embedder.time_text_embed.timestep_embedder <- txt_in.t_embedder
     converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_1.weight"] = (
         original_state_dict.pop("txt_in.t_embedder.mlp.0.weight")
@@ -627,7 +652,7 @@ def convert_transformer(args):
     config = TRANSFORMER_CONFIGS[args.transformer_type]
     with init_empty_weights():
         transformer = HunyuanVideo15Transformer3DModel(**config)
-    state_dict = convert_hyvideo15_transformer_to_diffusers(original_state_dict)
+    state_dict = convert_hyvideo15_transformer_to_diffusers(original_state_dict, config=transformer.config)
     transformer.load_state_dict(state_dict, strict=True, assign=True)
 
     return transformer

src/diffusers/models/transformers/transformer_hunyuan_video15.py

@@ -184,19 +184,32 @@ class HunyuanVideo15TimeEmbedding(nn.Module):
             The dimension of the output embedding.
     """
 
-    def __init__(self, embedding_dim: int):
+    def __init__(self, embedding_dim: int, use_meanflow: bool = False):
         super().__init__()
 
         self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
         self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
 
+        self.use_meanflow = use_meanflow
+        self.time_proj_r = None
+        self.timestep_embedder_r = None
+        if use_meanflow:
+            self.time_proj_r = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+            self.timestep_embedder_r = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
     def forward(
         self,
         timestep: torch.Tensor,
+        timestep_r: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         timesteps_proj = self.time_proj(timestep)
         timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=timestep.dtype))
 
+        if timestep_r is not None:
+            timesteps_proj_r = self.time_proj_r(timestep_r)
+            timesteps_emb_r = self.timestep_embedder_r(timesteps_proj_r.to(dtype=timestep.dtype))
+            timesteps_emb = timesteps_emb + timesteps_emb_r
+
         return timesteps_emb
 
 
@@ -567,6 +580,7 @@ class HunyuanVideo15Transformer3DModel(
         # YiYi Notes: config based on target_size_config https://github.com/yiyixuxu/hy15/blob/main/hyvideo/pipelines/hunyuan_video_pipeline.py#L205
         target_size: int = 640,  # did not name sample_size since it is in pixel spaces
         task_type: str = "i2v",
+        use_meanflow: bool = False,
     ) -> None:
         super().__init__()
 
@@ -582,7 +596,7 @@ class HunyuanVideo15Transformer3DModel(
         )
         self.context_embedder_2 = HunyuanVideo15ByT5TextProjection(text_embed_2_dim, 2048, inner_dim)
 
-        self.time_embed = HunyuanVideo15TimeEmbedding(inner_dim)
+        self.time_embed = HunyuanVideo15TimeEmbedding(inner_dim, use_meanflow=use_meanflow)
 
         self.cond_type_embed = nn.Embedding(3, inner_dim)
 
@@ -612,6 +626,7 @@ class HunyuanVideo15Transformer3DModel(
         timestep: torch.LongTensor,
         encoder_hidden_states: torch.Tensor,
         encoder_attention_mask: torch.Tensor,
+        timestep_r: Optional[torch.LongTensor] = None,
         encoder_hidden_states_2: Optional[torch.Tensor] = None,
         encoder_attention_mask_2: Optional[torch.Tensor] = None,
         image_embeds: Optional[torch.Tensor] = None,
@@ -643,7 +658,7 @@ class HunyuanVideo15Transformer3DModel(
         image_rotary_emb = self.rope(hidden_states)
 
         # 2. Conditional embeddings
-        temb = self.time_embed(timestep)
+        temb = self.time_embed(timestep, timestep_r=timestep_r)
 
         hidden_states = self.x_embedder(hidden_states)
 

src/diffusers/pipelines/hunyuan_video1_5/pipeline_hunyuan_video1_5_image2video.py

@@ -852,6 +852,15 @@ class HunyuanVideo15ImageToVideoPipeline(DiffusionPipeline):
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 timestep = t.expand(latent_model_input.shape[0]).to(latent_model_input.dtype)
 
+                if self.transformer.config.use_meanflow:
+                    if i == len(timesteps) - 1:
+                        timestep_r = torch.tensor([0.0], device=device)
+                    else:
+                        timestep_r = timesteps[i + 1]
+                    timestep_r = timestep_r.expand(latents.shape[0]).to(latents.dtype)
+                else:
+                    timestep_r = None
+
                 # Step 1: Collect model inputs needed for the guidance method
                 # conditional inputs should always be first element in the tuple
                 guider_inputs = {
@@ -893,6 +902,7 @@ class HunyuanVideo15ImageToVideoPipeline(DiffusionPipeline):
                             hidden_states=latent_model_input,
                             image_embeds=image_embeds,
                             timestep=timestep,
+                            timestep_r=timestep_r,
                             attention_kwargs=self.attention_kwargs,
                             return_dict=False,
                             **cond_kwargs,