[HunyuanVideo1.5] support step-distilled

scripts/convert_hunyuan_video1_5_to_diffusers.py

@@ -69,6 +69,11 @@
  "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 @@
  "720p_i2v_distilled": {
  "shift": 7.0,
  },
+ "480p_i2v_step_distilled": {
+ "shift": 7.0,
+ },
 }
 
 GUIDANCE_CONFIGS = {
@@ -117,6 +125,9 @@
  "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 @@ def __init__(
  # 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 @@ def __init__(
  )
  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 @@ def forward(
  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 @@ def forward(
  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 @@ def __call__(
  # 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 @@ def __call__(
  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,