[WIP] Wan2.2

src/diffusers/models/transformers/transformer_wan.py

@@ -170,8 +170,11 @@ def forward(
         timestep: torch.Tensor,
         encoder_hidden_states: torch.Tensor,
         encoder_hidden_states_image: Optional[torch.Tensor] = None,
+        timestep_seq_len: Optional[int] = None,
     ):
         timestep = self.timesteps_proj(timestep)
+        if timestep_seq_len is not None:
+            timestep = timestep.unflatten(0, (1, timestep_seq_len))
 
         time_embedder_dtype = next(iter(self.time_embedder.parameters())).dtype
         if timestep.dtype != time_embedder_dtype and time_embedder_dtype != torch.int8:
@@ -309,9 +312,23 @@ def forward(
         temb: torch.Tensor,
         rotary_emb: torch.Tensor,
     ) -> torch.Tensor:
-        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
-            self.scale_shift_table + temb.float()
-        ).chunk(6, dim=1)
+        if temb.ndim == 4:
+            # temb: batch_size, seq_len, 6, inner_dim (wan2.2 ti2v)
+            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
+                self.scale_shift_table.unsqueeze(0) + temb.float()
+            ).chunk(6, dim=2)
+            # batch_size, seq_len, 1, inner_dim
+            shift_msa = shift_msa.squeeze(2)
+            scale_msa = scale_msa.squeeze(2)
+            gate_msa = gate_msa.squeeze(2)
+            c_shift_msa = c_shift_msa.squeeze(2)
+            c_scale_msa = c_scale_msa.squeeze(2)
+            c_gate_msa = c_gate_msa.squeeze(2)
+        else:
+            # temb: batch_size, 6, inner_dim (wan2.1/wan2.2 14B)
+            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
+                self.scale_shift_table + temb.float()
+            ).chunk(6, dim=1)
 
         # 1. Self-attention
         norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states)
@@ -469,10 +486,22 @@ def forward(
         hidden_states = self.patch_embedding(hidden_states)
         hidden_states = hidden_states.flatten(2).transpose(1, 2)
 
+        # timestep shape: batch_size, or batch_size, seq_len (wan 2.2 ti2v)
+        if timestep.ndim == 2:
+            ts_seq_len = timestep.shape[1]
+            timestep = timestep.flatten()  # batch_size * seq_len
+        else:
+            ts_seq_len = None
+
         temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
-            timestep, encoder_hidden_states, encoder_hidden_states_image
+            timestep, encoder_hidden_states, encoder_hidden_states_image, timestep_seq_len=ts_seq_len
         )
-        timestep_proj = timestep_proj.unflatten(1, (6, -1))
+        if ts_seq_len is not None:
+            # batch_size, seq_len, 6, inner_dim
+            timestep_proj = timestep_proj.unflatten(2, (6, -1))
+        else:
+            # batch_size, 6, inner_dim
+            timestep_proj = timestep_proj.unflatten(1, (6, -1))
 
         if encoder_hidden_states_image is not None:
             encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)
@@ -488,7 +517,14 @@ def forward(
                 hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb)
 
         # 5. Output norm, projection & unpatchify
-        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
+        if temb.ndim == 3:
+            # batch_size, seq_len, inner_dim (wan 2.2 ti2v)
+            shift, scale = (self.scale_shift_table.unsqueeze(0) + temb.unsqueeze(2)).chunk(2, dim=2)
+            shift = shift.squeeze(2)
+            scale = scale.squeeze(2)
+        else:
+            # batch_size, inner_dim
+            shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
 
         # Move the shift and scale tensors to the same device as hidden_states.
         # When using multi-GPU inference via accelerate these will be on the

src/diffusers/pipelines/skyreels_v2/pipeline_skyreels_v2.py

@@ -275,7 +275,6 @@ def encode_prompt(
 
         return prompt_embeds, negative_prompt_embeds
 
-    # Copied from diffusers.pipelines.wan.pipeline_wan.WanPipeline.check_inputs
     def check_inputs(
         self,
         prompt,

src/diffusers/pipelines/skyreels_v2/pipeline_skyreels_v2_i2v.py

@@ -316,7 +316,6 @@ def encode_prompt(
 
         return prompt_embeds, negative_prompt_embeds
 
-    # Copied from diffusers.pipelines.wan.pipeline_wan_i2v.WanImageToVideoPipeline.check_inputs
     def check_inputs(
         self,
         prompt,

src/diffusers/pipelines/wan/pipeline_wan.py

@@ -112,10 +112,20 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
         vae ([`AutoencoderKLWan`]):
             Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        transformer_2 ([`WanTransformer3DModel`], *optional*):
+            Conditional Transformer to denoise the input latents during the low-noise stage. If provided, enables
+            two-stage denoising where `transformer` handles high-noise stages and `transformer_2` handles low-noise
+            stages. If not provided, only `transformer` is used.
+        boundary_ratio (`float`, *optional*, defaults to `None`):
+            Ratio of total timesteps to use as the boundary for switching between transformers in two-stage denoising.
+            The actual boundary timestep is calculated as `boundary_ratio * num_train_timesteps`. When provided,
+            `transformer` handles timesteps >= boundary_timestep and `transformer_2` handles timesteps <
+            boundary_timestep. If `None`, only `transformer` is used for the entire denoising process.
     """
 
-    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    model_cpu_offload_seq = "text_encoder->transformer->transformer_2->vae"
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _optional_components = ["transformer_2"]
 
     def __init__(
         self,
@@ -124,6 +134,9 @@ def __init__(
         transformer: WanTransformer3DModel,
         vae: AutoencoderKLWan,
         scheduler: FlowMatchEulerDiscreteScheduler,
+        transformer_2: Optional[WanTransformer3DModel] = None,
+        boundary_ratio: Optional[float] = None,
+        expand_timesteps: bool = False,  # Wan2.2 ti2v
     ):
         super().__init__()
 
@@ -133,10 +146,12 @@ def __init__(
             tokenizer=tokenizer,
             transformer=transformer,
             scheduler=scheduler,
+            transformer_2=transformer_2,
         )
-
-        self.vae_scale_factor_temporal = 2 ** sum(self.vae.temperal_downsample) if getattr(self, "vae", None) else 4
-        self.vae_scale_factor_spatial = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
+        self.register_to_config(boundary_ratio=boundary_ratio)
+        self.register_to_config(expand_timesteps=expand_timesteps)
+        self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal if getattr(self, "vae", None) else 4
+        self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 8
         self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
 
     def _get_t5_prompt_embeds(
@@ -270,6 +285,7 @@ def check_inputs(
         prompt_embeds=None,
         negative_prompt_embeds=None,
         callback_on_step_end_tensor_inputs=None,
+        guidance_scale_2=None,
     ):
         if height % 16 != 0 or width % 16 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
@@ -302,6 +318,9 @@ def check_inputs(
         ):
             raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
 
+        if self.config.boundary_ratio is None and guidance_scale_2 is not None:
+            raise ValueError("`guidance_scale_2` is only supported when the pipeline's `boundary_ratio` is not None.")
+
     def prepare_latents(
         self,
         batch_size: int,
@@ -369,6 +388,7 @@ def __call__(
         num_frames: int = 81,
         num_inference_steps: int = 50,
         guidance_scale: float = 5.0,
+        guidance_scale_2: Optional[float] = None,
         num_videos_per_prompt: Optional[int] = 1,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
@@ -407,6 +427,10 @@ def __call__(
                 of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
                 `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
                 the text `prompt`, usually at the expense of lower image quality.
+            guidance_scale_2 (`float`, *optional*, defaults to `None`):
+                Guidance scale for the low-noise stage transformer (`transformer_2`). If `None` and the pipeline's
+                `boundary_ratio` is not None, uses the same value as `guidance_scale`. Only used when `transformer_2`
+                and the pipeline's `boundary_ratio` are not None.
             num_videos_per_prompt (`int`, *optional*, defaults to 1):
                 The number of images to generate per prompt.
             generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
@@ -461,6 +485,7 @@ def __call__(
             prompt_embeds,
             negative_prompt_embeds,
             callback_on_step_end_tensor_inputs,
+            guidance_scale_2,
         )
 
         if num_frames % self.vae_scale_factor_temporal != 1:
@@ -470,7 +495,11 @@ def __call__(
             num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
         num_frames = max(num_frames, 1)
 
+        if self.config.boundary_ratio is not None and guidance_scale_2 is None:
+            guidance_scale_2 = guidance_scale
+
         self._guidance_scale = guidance_scale
+        self._guidance_scale_2 = guidance_scale_2
         self._attention_kwargs = attention_kwargs
         self._current_timestep = None
         self._interrupt = False
@@ -520,21 +549,44 @@ def __call__(
             latents,
         )
 
+        mask = torch.ones(latents.shape, dtype=torch.float32, device=device)
+
         # 6. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
         self._num_timesteps = len(timesteps)
 
+        if self.config.boundary_ratio is not None:
+            boundary_timestep = self.config.boundary_ratio * self.scheduler.config.num_train_timesteps
+        else:
+            boundary_timestep = None
+
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
                 if self.interrupt:
                     continue
 
                 self._current_timestep = t
-                latent_model_input = latents.to(transformer_dtype)
-                timestep = t.expand(latents.shape[0])
 
-                with self.transformer.cache_context("cond"):
-                    noise_pred = self.transformer(
+                if boundary_timestep is None or t >= boundary_timestep:
+                    # wan2.1 or high-noise stage in wan2.2
+                    current_model = self.transformer
+                    current_guidance_scale = guidance_scale
+                else:
+                    # low-noise stage in wan2.2
+                    current_model = self.transformer_2
+                    current_guidance_scale = guidance_scale_2
+
+                latent_model_input = latents.to(transformer_dtype)
+                if self.config.expand_timesteps:
+                    # seq_len: num_latent_frames * latent_height//2 * latent_width//2
+                    temp_ts = (mask[0][0][:, ::2, ::2] * t).flatten()
+                    # batch_size, seq_len
+                    timestep = temp_ts.unsqueeze(0).expand(latents.shape[0], -1)
+                else:
+                    timestep = t.expand(latents.shape[0])
+
+                with current_model.cache_context("cond"):
+                    noise_pred = current_model(
                         hidden_states=latent_model_input,
                         timestep=timestep,
                         encoder_hidden_states=prompt_embeds,
@@ -543,15 +595,15 @@ def __call__(
                     )[0]
 
                 if self.do_classifier_free_guidance:
-                    with self.transformer.cache_context("uncond"):
-                        noise_uncond = self.transformer(
+                    with current_model.cache_context("uncond"):
+                        noise_uncond = current_model(
                             hidden_states=latent_model_input,
                             timestep=timestep,
                             encoder_hidden_states=negative_prompt_embeds,
                             attention_kwargs=attention_kwargs,
                             return_dict=False,
                         )[0]
-                    noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
+                    noise_pred = noise_uncond + current_guidance_scale * (noise_pred - noise_uncond)
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]