Consistent SDXL Controlnet callback tensor inputs (#7958)

* make _callback_tensor_inputs consistent between sdxl pipelines

* forgot this one

* fix failing test

* fix test_components_function

* fix controlnet inpaint tests

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py

@@ -198,8 +198,26 @@ class StableDiffusionXLControlNetInpaintPipeline(
     """
 
     model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
-    _optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"]
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+    _optional_components = [
+        "tokenizer",
+        "tokenizer_2",
+        "text_encoder",
+        "text_encoder_2",
+        "image_encoder",
+        "feature_extractor",
+    ]
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+        "add_text_embeds",
+        "add_time_ids",
+        "negative_pooled_prompt_embeds",
+        "add_neg_time_ids",
+        "mask",
+        "masked_image_latents",
+    ]
 
     def __init__(
         self,

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -236,7 +236,15 @@ class StableDiffusionXLControlNetPipeline(
         "feature_extractor",
         "image_encoder",
     ]
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+        "add_text_embeds",
+        "add_time_ids",
+        "negative_pooled_prompt_embeds",
+        "negative_add_time_ids",
+    ]
 
     def __init__(
         self,
@@ -1528,6 +1536,12 @@ class StableDiffusionXLControlNetPipeline(
                     latents = callback_outputs.pop("latents", latents)
                     prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                     negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -228,7 +228,15 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
         "feature_extractor",
         "image_encoder",
     ]
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+        "add_text_embeds",
+        "add_time_ids",
+        "negative_pooled_prompt_embeds",
+        "add_neg_time_ids",
+    ]
 
     def __init__(
         self,
@@ -1584,6 +1592,12 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
                     latents = callback_outputs.pop("latents", latents)
                     prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                     negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    add_neg_time_ids = callback_outputs.pop("add_neg_time_ids", add_neg_time_ids)
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):

src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs_sd_xl.py

@@ -158,7 +158,15 @@ class StableDiffusionXLControlNetXSPipeline(
         "text_encoder_2",
         "feature_extractor",
     ]
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+        "add_text_embeds",
+        "add_time_ids",
+        "negative_pooled_prompt_embeds",
+        "negative_add_time_ids",
+    ]
 
     def __init__(
         self,

tests/pipelines/controlnet/test_controlnet_inpaint_sdxl.py

@@ -19,7 +19,15 @@ import unittest
 import numpy as np
 import torch
 from PIL import Image
-from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextConfig,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionConfig,
+    CLIPVisionModelWithProjection,
+)
 
 from diffusers import (
     AutoencoderKL,
@@ -34,6 +42,7 @@ from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor
 from ..pipeline_params import (
     IMAGE_TO_IMAGE_IMAGE_PARAMS,
     TEXT_TO_IMAGE_BATCH_PARAMS,
+    TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
     TEXT_TO_IMAGE_IMAGE_PARAMS,
     TEXT_TO_IMAGE_PARAMS,
 )
@@ -55,6 +64,14 @@ class ControlNetPipelineSDXLFastTests(
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
     image_params = frozenset(IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"mask_image", "control_image"}))
     image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union(
+        {
+            "add_text_embeds",
+            "add_time_ids",
+            "mask",
+            "masked_image_latents",
+        }
+    )
 
     def get_dummy_components(self):
         torch.manual_seed(0)
@@ -129,6 +146,30 @@ class ControlNetPipelineSDXLFastTests(
         text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
         tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
 
+        image_encoder_config = CLIPVisionConfig(
+            hidden_size=32,
+            image_size=224,
+            projection_dim=32,
+            intermediate_size=37,
+            num_attention_heads=4,
+            num_channels=3,
+            num_hidden_layers=5,
+            patch_size=14,
+        )
+
+        image_encoder = CLIPVisionModelWithProjection(image_encoder_config)
+
+        feature_extractor = CLIPImageProcessor(
+            crop_size=224,
+            do_center_crop=True,
+            do_normalize=True,
+            do_resize=True,
+            image_mean=[0.48145466, 0.4578275, 0.40821073],
+            image_std=[0.26862954, 0.26130258, 0.27577711],
+            resample=3,
+            size=224,
+        )
+
         components = {
             "unet": unet,
             "controlnet": controlnet,
@@ -138,6 +179,8 @@ class ControlNetPipelineSDXLFastTests(
             "tokenizer": tokenizer,
             "text_encoder_2": text_encoder_2,
             "tokenizer_2": tokenizer_2,
+            "image_encoder": image_encoder,
+            "feature_extractor": feature_extractor,
         }
         return components
 

tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py

@@ -34,6 +34,7 @@ from ..pipeline_params import (
     IMAGE_TO_IMAGE_IMAGE_PARAMS,
     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
     TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
+    TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
 )
 from ..test_pipelines_common import (
     IPAdapterTesterMixin,
@@ -55,9 +56,13 @@ class ControlNetPipelineSDXLImg2ImgFastTests(
 ):
     pipeline_class = StableDiffusionXLControlNetImg2ImgPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
+    required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
     batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
     image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
+    callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union(
+        {"add_text_embeds", "add_time_ids", "add_neg_time_ids"}
+    )
 
     def get_dummy_components(self, skip_first_text_encoder=False):
         torch.manual_seed(0)