Cleanup ControlnetXS (#7701)

* update

* update

src/diffusers/models/controlnet_xs.py

@@ -22,7 +22,14 @@ from torch import FloatTensor, nn
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..utils import BaseOutput, is_torch_version, logging
 from ..utils.torch_utils import apply_freeu
-from .attention_processor import Attention, AttentionProcessor
+from .attention_processor import (
+    ADDED_KV_ATTENTION_PROCESSORS,
+    CROSS_ATTENTION_PROCESSORS,
+    Attention,
+    AttentionProcessor,
+    AttnAddedKVProcessor,
+    AttnProcessor,
+)
 from .controlnet import ControlNetConditioningEmbedding
 from .embeddings import TimestepEmbedding, Timesteps
 from .modeling_utils import ModelMixin
@@ -869,7 +876,7 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
 
         return processors
 
-    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
     def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
         r"""
         Sets the attention processor to use to compute attention.
@@ -904,7 +911,23 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
         for name, module in self.named_children():
             fn_recursive_attn_processor(name, module, processor)
 
-    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel
+    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnAddedKVProcessor()
+        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnProcessor()
+        else:
+            raise ValueError(
+                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+            )
+
+        self.set_attn_processor(processor)
+
+    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu
     def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
         r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
 
@@ -929,7 +952,7 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
             setattr(upsample_block, "b1", b1)
             setattr(upsample_block, "b2", b2)
 
-    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel
+    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu
     def disable_freeu(self):
         """Disables the FreeU mechanism."""
         freeu_keys = {"s1", "s2", "b1", "b2"}
@@ -938,7 +961,7 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
                 if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
                     setattr(upsample_block, k, None)
 
-    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel
+    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
     def fuse_qkv_projections(self):
         """
         Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
@@ -962,7 +985,7 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
             if isinstance(module, Attention):
                 module.fuse_projections(fuse=True)
 
-    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel
+    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
     def unfuse_qkv_projections(self):
         """Disables the fused QKV projection if enabled.
 

src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs_sd_xl.py

@@ -41,7 +41,6 @@ from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
-    deprecate,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -462,7 +461,6 @@ class StableDiffusionXLControlNetXSPipeline(
         prompt,
         prompt_2,
         image,
-        callback_steps,
         negative_prompt=None,
         negative_prompt_2=None,
         prompt_embeds=None,
@@ -474,12 +472,6 @@ class StableDiffusionXLControlNetXSPipeline(
         control_guidance_end=1.0,
         callback_on_step_end_tensor_inputs=None,
     ):
-        if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
         if callback_on_step_end_tensor_inputs is not None and not all(
             k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
         ):
@@ -749,7 +741,6 @@ class StableDiffusionXLControlNetXSPipeline(
         clip_skip: Optional[int] = None,
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        **kwargs,
     ):
         r"""
         The call function to the pipeline for generation.
@@ -878,22 +869,6 @@ class StableDiffusionXLControlNetXSPipeline(
                 returned, otherwise a `tuple` is returned containing the output images.
         """
 
-        callback = kwargs.pop("callback", None)
-        callback_steps = kwargs.pop("callback_steps", None)
-
-        if callback is not None:
-            deprecate(
-                "callback",
-                "1.0.0",
-                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
-            )
-        if callback_steps is not None:
-            deprecate(
-                "callback_steps",
-                "1.0.0",
-                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
-            )
-
         unet = self.unet._orig_mod if is_compiled_module(self.unet) else self.unet
 
         # 1. Check inputs. Raise error if not correct
@@ -901,7 +876,6 @@ class StableDiffusionXLControlNetXSPipeline(
             prompt,
             prompt_2,
             image,
-            callback_steps,
             negative_prompt,
             negative_prompt_2,
             prompt_embeds,
@@ -1089,9 +1063,6 @@ class StableDiffusionXLControlNetXSPipeline(
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        step_idx = i // getattr(self.scheduler, "order", 1)
-                        callback(step_idx, t, latents)
 
         # manually for max memory savings
         if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:

tests/pipelines/controlnet_xs/test_controlnetxs.py

@@ -69,6 +69,13 @@ from ..test_pipelines_common import (
 enable_full_determinism()
 
 
+def to_np(tensor):
+    if isinstance(tensor, torch.Tensor):
+        tensor = tensor.detach().cpu().numpy()
+
+    return tensor
+
+
 # Will be run via run_test_in_subprocess
 def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
     error = None
@@ -299,6 +306,31 @@ class ControlNetXSPipelineFastTests(
 
             assert out_vae_np.shape == out_np.shape
 
+    @unittest.skipIf(torch_device != "cuda", reason="CUDA and CPU are required to switch devices")
+    def test_to_device(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.set_progress_bar_config(disable=None)
+
+        pipe.to("cpu")
+        # pipeline creates a new UNetControlNetXSModel under the hood. So we need to check the device from pipe.components
+        model_devices = [
+            component.device.type for component in pipe.components.values() if hasattr(component, "device")
+        ]
+        self.assertTrue(all(device == "cpu" for device in model_devices))
+
+        output_cpu = pipe(**self.get_dummy_inputs("cpu"))[0]
+        self.assertTrue(np.isnan(output_cpu).sum() == 0)
+
+        pipe.to("cuda")
+        model_devices = [
+            component.device.type for component in pipe.components.values() if hasattr(component, "device")
+        ]
+        self.assertTrue(all(device == "cuda" for device in model_devices))
+
+        output_cuda = pipe(**self.get_dummy_inputs("cuda"))[0]
+        self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)
+
 
 @slow
 @require_torch_gpu