add PAG support

src/diffusers/models/attention_processor.py

@@ -2651,6 +2651,232 @@ def __call__(
         return hidden_states
 
 
+class PAGIdentitySelfAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        temb: Optional[torch.FloatTensor] = None,
+        *args,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        if len(args) > 0 or kwargs.get("scale", None) is not None:
+            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
+            deprecate("scale", "1.0.0", deprecation_message)
+
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        # chunk
+        hidden_states_org, hidden_states_ptb = hidden_states.chunk(2)
+
+        # original path
+        batch_size, sequence_length, _ = hidden_states_org.shape
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states_org = attn.group_norm(hidden_states_org.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states_org)
+        key = attn.to_k(hidden_states_org)
+        value = attn.to_v(hidden_states_org)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states_org = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states_org = hidden_states_org.to(query.dtype)
+
+        # linear proj
+        hidden_states_org = attn.to_out[0](hidden_states_org)
+        # dropout
+        hidden_states_org = attn.to_out[1](hidden_states_org)
+
+        if input_ndim == 4:
+            hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        # perturbed path (identity attention)
+        batch_size, sequence_length, _ = hidden_states_ptb.shape
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states_ptb = attn.group_norm(hidden_states_ptb.transpose(1, 2)).transpose(1, 2)
+
+        value = attn.to_v(hidden_states_ptb)
+
+        # hidden_states_ptb = torch.zeros(value.shape).to(value.get_device())
+        hidden_states_ptb = value
+
+        hidden_states_ptb = hidden_states_ptb.to(query.dtype)
+
+        # linear proj
+        hidden_states_ptb = attn.to_out[0](hidden_states_ptb)
+        # dropout
+        hidden_states_ptb = attn.to_out[1](hidden_states_ptb)
+
+        if input_ndim == 4:
+            hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        # cat
+        hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class PAGCFGIdentitySelfAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        temb: Optional[torch.FloatTensor] = None,
+        *args,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        if len(args) > 0 or kwargs.get("scale", None) is not None:
+            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
+            deprecate("scale", "1.0.0", deprecation_message)
+
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        # chunk
+        hidden_states_uncond, hidden_states_org, hidden_states_ptb = hidden_states.chunk(3)
+        hidden_states_org = torch.cat([hidden_states_uncond, hidden_states_org])
+
+        # original path
+        batch_size, sequence_length, _ = hidden_states_org.shape
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states_org = attn.group_norm(hidden_states_org.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states_org)
+        key = attn.to_k(hidden_states_org)
+        value = attn.to_v(hidden_states_org)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states_org = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states_org = hidden_states_org.to(query.dtype)
+
+        # linear proj
+        hidden_states_org = attn.to_out[0](hidden_states_org)
+        # dropout
+        hidden_states_org = attn.to_out[1](hidden_states_org)
+
+        if input_ndim == 4:
+            hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        # perturbed path (identity attention)
+        batch_size, sequence_length, _ = hidden_states_ptb.shape
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states_ptb = attn.group_norm(hidden_states_ptb.transpose(1, 2)).transpose(1, 2)
+
+        value = attn.to_v(hidden_states_ptb)
+        hidden_states_ptb = value
+        hidden_states_ptb = hidden_states_ptb.to(query.dtype)
+
+        # linear proj
+        hidden_states_ptb = attn.to_out[0](hidden_states_ptb)
+        # dropout
+        hidden_states_ptb = attn.to_out[1](hidden_states_ptb)
+
+        if input_ndim == 4:
+            hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        # cat
+        hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
 LORA_ATTENTION_PROCESSORS = (
     LoRAAttnProcessor,
     LoRAAttnProcessor2_0,
@@ -2691,6 +2917,8 @@ def __call__(
     CustomDiffusionAttnProcessor,
     CustomDiffusionXFormersAttnProcessor,
     CustomDiffusionAttnProcessor2_0,
+    PAGCFGIdentitySelfAttnProcessor2_0,
+    PAGIdentitySelfAttnProcessor2_0,
     # deprecated
     LoRAAttnProcessor,
     LoRAAttnProcessor2_0,

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -55,6 +55,7 @@
     unscale_lora_layers,
 )
 from ...utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
+from ..pag_utils import PAGMixin
 from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 
@@ -181,6 +182,7 @@ class StableDiffusionXLControlNetPipeline(
     StableDiffusionXLLoraLoaderMixin,
     IPAdapterMixin,
     FromSingleFileMixin,
+    PAGMixin,
 ):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
@@ -619,6 +621,7 @@ def check_inputs(
         prompt_2,
         image,
         callback_steps,
+        guidance_scale,
         negative_prompt=None,
         negative_prompt_2=None,
         prompt_embeds=None,
@@ -802,6 +805,11 @@ def check_inputs(
                     f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
                 )
 
+        if hasattr(self, "_pag_cfg") and self._pag_cfg is False and guidance_scale != 0:
+            raise ValueError(
+                f"Cannot use guidance scale {guidance_scale} with PAG unconditional guidance. Please set `guidance_scale` to 0."
+            )
+
     # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
     def check_image(self, image, prompt, prompt_embeds):
         image_is_pil = isinstance(image, PIL.Image.Image)
@@ -1223,6 +1231,7 @@ def __call__(
             prompt_2,
             image,
             callback_steps,
+            guidance_scale,
             negative_prompt,
             negative_prompt_2,
             prompt_embeds,
@@ -1405,6 +1414,11 @@ def __call__(
         else:
             negative_add_time_ids = add_time_ids
 
+        if self.do_perturbed_attention_guidance:
+            prompt_embeds = torch.cat([prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([add_text_embeds, add_text_embeds], dim=0)
+            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+
         if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
@@ -1442,8 +1456,8 @@ def __call__(
                 # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
                 if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:
                     torch._inductor.cudagraph_mark_step_begin()
-                # 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
+                # expand the latents if we are doing classifier free guidance or perturbed attention guidance
+                latent_model_input = torch.cat([latents] * (prompt_embeds.shape[0] // latents.shape[0]))
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
                 added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
@@ -1506,7 +1520,11 @@ def __call__(
                 )[0]
 
                 # perform guidance
-                if self.do_classifier_free_guidance:
+                if self.do_perturbed_attention_guidance:
+                    noise_pred = self._apply_perturbed_attention_guidance(
+                        noise_pred, self.do_classifier_free_guidance, self.guidance_scale, t
+                    )
+                elif self.do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)