add skip_layers argument to SD3 transformer model class

src/diffusers/models/transformers/transformer_sd3.py

@@ -268,6 +268,7 @@ def forward(
         block_controlnet_hidden_states: List = None,
         joint_attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
+        skip_layers: Optional[List[int]] = None,
     ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
         """
         The [`SD3Transformer2DModel`] forward method.
@@ -279,9 +280,9 @@ def forward(
                 Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
             pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
                 from the embeddings of input conditions.
-            timestep ( `torch.LongTensor`):
+            timestep (`torch.LongTensor`):
                 Used to indicate denoising step.
-            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+            block_controlnet_hidden_states (`list` of `torch.Tensor`):
                 A list of tensors that if specified are added to the residuals of transformer blocks.
             joint_attention_kwargs (`dict`, *optional*):
                 A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
@@ -290,6 +291,8 @@ def forward(
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
                 tuple.
+            skip_layers (`list` of `int`, *optional*):
+                A list of layer indices to skip during the forward pass.
 
         Returns:
             If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
@@ -317,7 +320,10 @@ def forward(
         encoder_hidden_states = self.context_embedder(encoder_hidden_states)
 
         for index_block, block in enumerate(self.transformer_blocks):
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
+            # Skip specified layers
+            is_skip = True if skip_layers is not None and index_block in skip_layers else False
+
+            if torch.is_grad_enabled() and self.gradient_checkpointing and not is_skip:
 
                 def create_custom_forward(module, return_dict=None):
                     def custom_forward(*inputs):
@@ -336,8 +342,7 @@ def custom_forward(*inputs):
                     temb,
                     **ckpt_kwargs,
                 )
-
-            else:
+            elif not is_skip:
                 encoder_hidden_states, hidden_states = block(
                     hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb
                 )

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py

@@ -642,6 +642,10 @@ def prepare_latents(
     def guidance_scale(self):
         return self._guidance_scale
 
+    @property
+    def skip_guidance_layers(self):
+        return self._skip_guidance_layers
+
     @property
     def clip_skip(self):
         return self._clip_skip
@@ -694,6 +698,10 @@ def __call__(
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 256,
+        skip_guidance_layers: List[int] = None,
+        skip_layer_guidance_scale: int = 2.8,
+        skip_layer_guidance_stop: int = 0.2,
+        skip_layer_guidance_start: int = 0.01,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -778,6 +786,22 @@ def __call__(
                 will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                 `._callback_tensor_inputs` attribute of your pipeline class.
             max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
+            skip_guidance_layers (`List[int]`, *optional*):
+                A list of integers that specify layers to skip during guidance. If not provided, all layers will be
+                used for guidance. If provided, the guidance will only be applied to the layers specified in the list.
+                Recommended value by StabiltyAI for Stable Diffusion 3.5 Medium is [7, 8, 9].
+            skip_layer_guidance_scale (`int`, *optional*): The scale of the guidance for the layers specified in
+                `skip_guidance_layers`. The guidance will be applied to the layers specified in `skip_guidance_layers`
+                with a scale of `skip_layer_guidance_scale`. The guidance will be applied to the rest of the layers
+                with a scale of `1`.
+            skip_layer_guidance_stop (`int`, *optional*): The step at which the guidance for the layers specified in
+                `skip_guidance_layers` will stop. The guidance will be applied to the layers specified in
+                `skip_guidance_layers` until the fraction specified in `skip_layer_guidance_stop`. Recommended value by
+                StabiltyAI for Stable Diffusion 3.5 Medium is 0.2.
+            skip_layer_guidance_start (`int`, *optional*): The step at which the guidance for the layers specified in
+                `skip_guidance_layers` will start. The guidance will be applied to the layers specified in
+                `skip_guidance_layers` from the fraction specified in `skip_layer_guidance_start`. Recommended value by
+                StabiltyAI for Stable Diffusion 3.5 Medium is 0.01.
 
         Examples:
 
@@ -809,6 +833,7 @@ def __call__(
         )
 
         self._guidance_scale = guidance_scale
+        self._skip_layer_guidance_scale = skip_layer_guidance_scale
         self._clip_skip = clip_skip
         self._joint_attention_kwargs = joint_attention_kwargs
         self._interrupt = False
@@ -851,6 +876,9 @@ def __call__(
         )
 
         if self.do_classifier_free_guidance:
+            if skip_guidance_layers is not None:
+                original_prompt_embeds = prompt_embeds
+                original_pooled_prompt_embeds = pooled_prompt_embeds
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
 
@@ -879,7 +907,11 @@ def __call__(
                     continue
 
                 # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                latent_model_input = (
+                    torch.cat([latents] * 2)
+                    if self.do_classifier_free_guidance and skip_guidance_layers is None
+                    else latents
+                )
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 timestep = t.expand(latent_model_input.shape[0])
 
@@ -896,6 +928,25 @@ def __call__(
                 if self.do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                     noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+                    should_skip_layers = (
+                        True
+                        if i > num_inference_steps * skip_layer_guidance_start
+                        and i < num_inference_steps * skip_layer_guidance_stop
+                        else False
+                    )
+                    if skip_guidance_layers is not None and should_skip_layers:
+                        noise_pred_skip_layers = self.transformer(
+                            hidden_states=latent_model_input,
+                            timestep=timestep,
+                            encoder_hidden_states=original_prompt_embeds,
+                            pooled_projections=original_pooled_prompt_embeds,
+                            joint_attention_kwargs=self.joint_attention_kwargs,
+                            return_dict=False,
+                            skip_layers=skip_guidance_layers,
+                        )[0]
+                        noise_pred = (
+                            noise_pred + (noise_pred_text - noise_pred_skip_layers) * self._skip_layer_guidance_scale
+                        )
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents_dtype = latents.dtype

tests/models/transformers/test_models_transformer_sd3.py

@@ -147,3 +147,23 @@ def test_set_attn_processor_for_determinism(self):
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"SD3Transformer2DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+
+    def test_skip_layers(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).to(torch_device)
+
+        # Forward pass without skipping layers
+        output_full = model(**inputs_dict).sample
+
+        # Forward pass with skipping layers 0 (since there's only one layer in this test setup)
+        inputs_dict_with_skip = inputs_dict.copy()
+        inputs_dict_with_skip["skip_layers"] = [0]
+        output_skip = model(**inputs_dict_with_skip).sample
+
+        # Check that the outputs are different
+        self.assertFalse(
+            torch.allclose(output_full, output_skip, atol=1e-5), "Outputs should differ when layers are skipped"
+        )
+
+        # Check that the outputs have the same shape
+        self.assertEqual(output_full.shape, output_skip.shape, "Outputs should have the same shape")