Modular APG

hlky · hlky · commit d89a3f1d2b67 · 2024-12-10T14:17:07.000Z
diff --git a/src/diffusers/guider.py b/src/diffusers/guider.py
@@ -188,6 +188,7 @@ def apply_guidance(
         self,
         model_output: torch.Tensor,
         timestep: int = None,
+        latents: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         if not self.do_classifier_free_guidance:
             return model_output
@@ -476,6 +477,7 @@ def apply_guidance(
         self,
         model_output: torch.Tensor,
         timestep: int,
+        latents: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         if not self.do_perturbed_attention_guidance:
             return model_output
@@ -501,3 +503,231 @@ def apply_guidance(
             noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
 
         return noise_pred
+
+
+class MomentumBuffer:
+    def __init__(self, momentum: float):
+        self.momentum = momentum
+        self.running_average = 0
+
+    def update(self, update_value: torch.Tensor):
+        new_average = self.momentum * self.running_average
+        self.running_average = update_value + new_average
+
+
+class APGGuider:
+    """
+    This class is used to guide the pipeline with APG (Adaptive Projected Guidance).
+    """
+
+    def normalized_guidance(
+        self,
+        pred_cond: torch.Tensor,
+        pred_uncond: torch.Tensor,
+        guidance_scale: float,
+        momentum_buffer: MomentumBuffer = None,
+        norm_threshold: float = 0.0,
+        eta: float = 1.0,
+    ):
+        """
+        Based on the findings of [Eliminating Oversaturation and Artifacts of High Guidance Scales
+        in Diffusion Models](https://arxiv.org/pdf/2410.02416)
+        """
+        diff = pred_cond - pred_uncond
+        if momentum_buffer is not None:
+            momentum_buffer.update(diff)
+            diff = momentum_buffer.running_average
+        if norm_threshold > 0:
+            ones = torch.ones_like(diff)
+            diff_norm = diff.norm(p=2, dim=[-1, -2, -3], keepdim=True)
+            scale_factor = torch.minimum(ones, norm_threshold / diff_norm)
+            diff = diff * scale_factor
+        v0, v1 = diff.double(), pred_cond.double()
+        v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
+        v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
+        v0_orthogonal = v0 - v0_parallel
+        diff_parallel, diff_orthogonal = v0_parallel.to(diff.dtype), v0_orthogonal.to(diff.dtype)
+        normalized_update = diff_orthogonal + eta * diff_parallel
+        pred_guided = pred_cond + (guidance_scale - 1) * normalized_update
+        return pred_guided
+
+    @property
+    def adaptive_projected_guidance_momentum(self):
+        return self._adaptive_projected_guidance_momentum
+
+    @property
+    def adaptive_projected_guidance_rescale_factor(self):
+        return self._adaptive_projected_guidance_rescale_factor
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1.0 and not self._disable_guidance
+
+    @property
+    def guidance_rescale(self):
+        return self._guidance_rescale
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def batch_size(self):
+        return self._batch_size
+
+    def set_guider(self, pipeline, guider_kwargs: Dict[str, Any]):
+        disable_guidance = guider_kwargs.get("disable_guidance", False)
+        guidance_scale = guider_kwargs.get("guidance_scale", None)
+        if guidance_scale is None:
+            raise ValueError("guidance_scale is not provided in guider_kwargs")
+        adaptive_projected_guidance_momentum = guider_kwargs.get("adaptive_projected_guidance_momentum", None)
+        adaptive_projected_guidance_rescale_factor = guider_kwargs.get(
+            "adaptive_projected_guidance_rescale_factor", 15.0
+        )
+        guidance_rescale = guider_kwargs.get("guidance_rescale", 0.0)
+        batch_size = guider_kwargs.get("batch_size", None)
+        if batch_size is None:
+            raise ValueError("batch_size is not provided in guider_kwargs")
+        self._adaptive_projected_guidance_momentum = adaptive_projected_guidance_momentum
+        self._adaptive_projected_guidance_rescale_factor = adaptive_projected_guidance_rescale_factor
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._batch_size = batch_size
+        self._disable_guidance = disable_guidance
+        if adaptive_projected_guidance_momentum is not None:
+            self.momentum_buffer = MomentumBuffer(adaptive_projected_guidance_momentum)
+        else:
+            self.momentum_buffer = None
+        self.scheduler = pipeline.scheduler
+
+    def reset_guider(self, pipeline):
+        pass
+
+    def maybe_update_guider(self, pipeline, timestep):
+        pass
+
+    def maybe_update_input(self, pipeline, cond_input):
+        pass
+
+    def _maybe_split_prepared_input(self, cond):
+        """
+        Process and potentially split the conditional input for Classifier-Free Guidance (CFG).
+
+        This method handles inputs that may already have CFG applied (i.e. when `cond` is output of `prepare_input`).
+        It determines whether to split the input based on its batch size relative to the expected batch size.
+
+        Args:
+            cond (torch.Tensor): The conditional input tensor to process.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]: A tuple containing:
+                - The negative conditional input (uncond_input)
+                - The positive conditional input (cond_input)
+        """
+        if cond.shape[0] == self.batch_size * 2:
+            neg_cond = cond[0 : self.batch_size]
+            cond = cond[self.batch_size :]
+            return neg_cond, cond
+        elif cond.shape[0] == self.batch_size:
+            return cond, cond
+        else:
+            raise ValueError(f"Unsupported input shape: {cond.shape}")
+
+    def _is_prepared_input(self, cond):
+        """
+        Check if the input is already prepared for Classifier-Free Guidance (CFG).
+
+        Args:
+            cond (torch.Tensor): The conditional input tensor to check.
+
+        Returns:
+            bool: True if the input is already prepared, False otherwise.
+        """
+        cond_tensor = cond[0] if isinstance(cond, (list, tuple)) else cond
+
+        return cond_tensor.shape[0] == self.batch_size * 2
+
+    def prepare_input(
+        self,
+        cond_input: Union[torch.Tensor, List[torch.Tensor]],
+        negative_cond_input: Optional[Union[torch.Tensor, List[torch.Tensor]]] = None,
+    ) -> Union[torch.Tensor, List[torch.Tensor]]:
+        """
+        Prepare the input for CFG.
+
+        Args:
+            cond_input (Union[torch.Tensor, List[torch.Tensor]]):
+                The conditional input. It can be a single tensor or a
+            list of tensors. It must have the same length as `negative_cond_input`.
+            negative_cond_input (Union[torch.Tensor, List[torch.Tensor]]): The negative conditional input. It can be a
+                single tensor or a list of tensors. It must have the same length as `cond_input`.
+
+        Returns:
+            Union[torch.Tensor, List[torch.Tensor]]: The prepared input.
+        """
+
+        # we check if cond_input already has CFG applied, and split if it is the case.
+        if self._is_prepared_input(cond_input) and self.do_classifier_free_guidance:
+            return cond_input
+
+        if self._is_prepared_input(cond_input) and not self.do_classifier_free_guidance:
+            if isinstance(cond_input, list):
+                negative_cond_input, cond_input = zip(*[self._maybe_split_prepared_input(cond) for cond in cond_input])
+            else:
+                negative_cond_input, cond_input = self._maybe_split_prepared_input(cond_input)
+
+        if not self._is_prepared_input(cond_input) and negative_cond_input is None:
+            raise ValueError(
+                "`negative_cond_input` is required when cond_input does not already contains negative conditional input"
+            )
+
+        if isinstance(cond_input, (list, tuple)):
+            if not self.do_classifier_free_guidance:
+                return cond_input
+
+            if len(negative_cond_input) != len(cond_input):
+                raise ValueError("The length of negative_cond_input and cond_input must be the same.")
+            prepared_input = []
+            for neg_cond, cond in zip(negative_cond_input, cond_input):
+                if neg_cond.shape[0] != cond.shape[0]:
+                    raise ValueError("The batch size of negative_cond_input and cond_input must be the same.")
+                prepared_input.append(torch.cat([neg_cond, cond], dim=0))
+            return prepared_input
+
+        elif isinstance(cond_input, torch.Tensor):
+            if not self.do_classifier_free_guidance:
+                return cond_input
+            else:
+                return torch.cat([negative_cond_input, cond_input], dim=0)
+
+        else:
+            raise ValueError(f"Unsupported input type: {type(cond_input)}")
+
+    def apply_guidance(
+        self,
+        model_output: torch.Tensor,
+        timestep: int = None,
+        latents: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if not self.do_classifier_free_guidance:
+            return model_output
+
+        if latents is None:
+            raise ValueError("APG requires `latents` to convert model output to denoised prediction (x0).")
+
+        sigma = self.scheduler.sigmas[self.scheduler.step_index]
+        noise_pred = latents - sigma * model_output
+        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+        noise_pred = self.normalized_guidance(
+            noise_pred_text,
+            noise_pred_uncond,
+            self.guidance_scale,
+            self.momentum_buffer,
+            self.adaptive_projected_guidance_rescale_factor,
+        )
+        noise_pred = (latents - noise_pred) / sigma
+
+        if self.guidance_rescale > 0.0:
+            # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+            noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+        return noise_pred
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_modular.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_modular.py
@@ -926,6 +926,7 @@ def __call__(self, pipeline, state: PipelineState) -> PipelineState:
                 noise_pred = pipeline.guider.apply_guidance(
                     noise_pred,
                     timestep=t,
+                    latents=latents,
                 )
                 # compute the previous noisy sample x_t -> x_t-1
                 latents_dtype = latents.dtype
@@ -1213,7 +1214,7 @@ def __call__(self, pipeline, state: PipelineState) -> PipelineState:
                     return_dict=False,
                 )[0]
                 # perform guidance
-                noise_pred = pipeline.guider.apply_guidance(noise_pred, timestep=t)
+                noise_pred = pipeline.guider.apply_guidance(noise_pred, timestep=t, latents=latents)
                 # compute the previous noisy sample x_t -> x_t-1
                 latents_dtype = latents.dtype
                 latents = pipeline.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
