Postprocessing refactor all others (#3337)

* add text2img

* fix-copies

* add

* add all other pipelines

* add

* add

* add

* add

* add

* make style

* style + fix copies

---------
Co-authored-by: yiyixuxu <yixu310@gmail,com>

src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py

@@ -13,12 +13,14 @@
 # limitations under the License.
 
 import inspect
+import warnings
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 from transformers.models.clip.modeling_clip import CLIPTextModelOutput
 
+from ...image_processor import VaeImageProcessor
 from ...loaders import TextualInversionLoaderMixin
 from ...models import AutoencoderKL, PriorTransformer, UNet2DConditionModel
 from ...models.embeddings import get_timestep_embedding
@@ -136,6 +138,7 @@ class StableUnCLIPPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
         )
 
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
     def enable_vae_slicing(self):
@@ -474,6 +477,11 @@ class StableUnCLIPPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
     def decode_latents(self, latents):
+        warnings.warn(
+            "The decode_latents method is deprecated and will be removed in a future version. Please"
+            " use VaeImageProcessor instead",
+            FutureWarning,
+        )
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents, return_dict=False)[0]
         image = (image / 2 + 0.5).clamp(0, 1)
@@ -918,17 +926,17 @@ class StableUnCLIPPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
             if callback is not None and i % callback_steps == 0:
                 callback(i, t, latents)
 
-        # 14. Post-processing
-        image = self.decode_latents(latents)
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+        else:
+            image = latents
+
+        image = self.image_processor.postprocess(image, output_type=output_type)
 
         # Offload last model to CPU
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.final_offload_hook.offload()
 
-        # 15. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
         if not return_dict:
             return (image,)
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import inspect
+import warnings
 from typing import Any, Callable, Dict, List, Optional, Union
 
 import PIL
@@ -21,6 +22,7 @@ from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPV
 
 from diffusers.utils.import_utils import is_accelerate_available
 
+from ...image_processor import VaeImageProcessor
 from ...loaders import TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.embeddings import get_timestep_embedding
@@ -138,6 +140,7 @@ class StableUnCLIPImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin
         )
 
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
     def enable_vae_slicing(self):
@@ -429,6 +432,11 @@ class StableUnCLIPImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
     def decode_latents(self, latents):
+        warnings.warn(
+            "The decode_latents method is deprecated and will be removed in a future version. Please"
+            " use VaeImageProcessor instead",
+            FutureWarning,
+        )
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents, return_dict=False)[0]
         image = (image / 2 + 0.5).clamp(0, 1)
@@ -814,16 +822,17 @@ class StableUnCLIPImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin
                 callback(i, t, latents)
 
         # 9. Post-processing
-        image = self.decode_latents(latents)
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+        else:
+            image = latents
+
+        image = self.image_processor.postprocess(image, output_type=output_type)
 
         # Offload last model to CPU
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.final_offload_hook.offload()
 
-        # 10. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
         if not return_dict:
             return (image,)
 

src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py

@@ -363,6 +363,11 @@ class StableDiffusionPipelineSafe(DiffusionPipeline):
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
     def decode_latents(self, latents):
+        warnings.warn(
+            "The decode_latents method is deprecated and will be removed in a future version. Please"
+            " use VaeImageProcessor instead",
+            FutureWarning,
+        )
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents, return_dict=False)[0]
         image = (image / 2 + 0.5).clamp(0, 1)

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import inspect
+import warnings
 from typing import Callable, List, Optional, Tuple, Union
 
 import numpy as np
@@ -26,6 +27,7 @@ from transformers import (
     CLIPVisionModelWithProjection,
 )
 
+from ...image_processor import VaeImageProcessor
 from ...models import AutoencoderKL, DualTransformer2DModel, Transformer2DModel, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import is_accelerate_available, logging, randn_tensor
@@ -88,6 +90,7 @@ class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):
             scheduler=scheduler,
         )
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
 
         if self.text_unet is not None and (
             "dual_cross_attention" not in self.image_unet.config or not self.image_unet.config.dual_cross_attention
@@ -329,6 +332,11 @@ class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
     def decode_latents(self, latents):
+        warnings.warn(
+            "The decode_latents method is deprecated and will be removed in a future version. Please"
+            " use VaeImageProcessor instead",
+            FutureWarning,
+        )
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents, return_dict=False)[0]
         image = (image / 2 + 0.5).clamp(0, 1)
@@ -572,12 +580,12 @@ class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):
             if callback is not None and i % callback_steps == 0:
                 callback(i, t, latents)
 
-        # 9. Post-processing
-        image = self.decode_latents(latents)
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+        else:
+            image = latents
 
-        # 10. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
+        image = self.image_processor.postprocess(image, output_type=output_type)
 
         if not return_dict:
             return (image,)

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import inspect
+import warnings
 from typing import Callable, List, Optional, Union
 
 import numpy as np
@@ -21,6 +22,7 @@ import torch
 import torch.utils.checkpoint
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
+from ...image_processor import VaeImageProcessor
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import is_accelerate_available, logging, randn_tensor
@@ -71,6 +73,7 @@ class VersatileDiffusionImageVariationPipeline(DiffusionPipeline):
             scheduler=scheduler,
         )
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
 
     def enable_sequential_cpu_offload(self, gpu_id=0):
         r"""
@@ -189,6 +192,11 @@ class VersatileDiffusionImageVariationPipeline(DiffusionPipeline):
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
     def decode_latents(self, latents):
+        warnings.warn(
+            "The decode_latents method is deprecated and will be removed in a future version. Please"
+            " use VaeImageProcessor instead",
+            FutureWarning,
+        )
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents, return_dict=False)[0]
         image = (image / 2 + 0.5).clamp(0, 1)
@@ -414,12 +422,12 @@ class VersatileDiffusionImageVariationPipeline(DiffusionPipeline):
             if callback is not None and i % callback_steps == 0:
                 callback(i, t, latents)
 
-        # 8. Post-processing
-        image = self.decode_latents(latents)
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+        else:
+            image = latents
 
-        # 9. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
+        image = self.image_processor.postprocess(image, output_type=output_type)
 
         if not return_dict:
             return (image,)

src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py

@@ -13,12 +13,14 @@
 # limitations under the License.
 
 import inspect
+import warnings
 from typing import Callable, List, Optional, Union
 
 import torch
 import torch.utils.checkpoint
 from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer
 
+from ...image_processor import VaeImageProcessor
 from ...models import AutoencoderKL, Transformer2DModel, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import is_accelerate_available, logging, randn_tensor
@@ -76,6 +78,7 @@ class VersatileDiffusionTextToImagePipeline(DiffusionPipeline):
             scheduler=scheduler,
         )
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
 
         if self.text_unet is not None:
             self._swap_unet_attention_blocks()
@@ -246,6 +249,11 @@ class VersatileDiffusionTextToImagePipeline(DiffusionPipeline):
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
     def decode_latents(self, latents):
+        warnings.warn(
+            "The decode_latents method is deprecated and will be removed in a future version. Please"
+            " use VaeImageProcessor instead",
+            FutureWarning,
+        )
         latents = 1 / self.vae.config.scaling_factor * latents
         image = self.vae.decode(latents, return_dict=False)[0]
         image = (image / 2 + 0.5).clamp(0, 1)
@@ -488,12 +496,12 @@ class VersatileDiffusionTextToImagePipeline(DiffusionPipeline):
             if callback is not None and i % callback_steps == 0:
                 callback(i, t, latents)
 
-        # 9. Post-processing
-        image = self.decode_latents(latents)
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+        else:
+            image = latents
 
-        # 10. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
+        image = self.image_processor.postprocess(image, output_type=output_type)
 
         if not return_dict:
             return (image,)

tests/pipelines/altdiffusion/test_alt_diffusion.py

@@ -28,17 +28,18 @@ from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
 from diffusers.utils import slow, torch_device
 from diffusers.utils.testing_utils import require_torch_gpu
 
-from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-class AltDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class AltDiffusionPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = AltDiffusionPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/altdiffusion/test_alt_diffusion_img2img.py

@@ -123,6 +123,7 @@ class AltDiffusionImg2ImgPipelineFastTests(unittest.TestCase):
         tokenizer.model_max_length = 77
 
         init_image = self.dummy_image.to(device)
+        init_image = init_image / 2 + 0.5
 
         # make sure here that pndm scheduler skips prk
         alt_pipe = AltDiffusionImg2ImgPipeline(
@@ -134,7 +135,7 @@ class AltDiffusionImg2ImgPipelineFastTests(unittest.TestCase):
             safety_checker=None,
             feature_extractor=self.dummy_extractor,
         )
-        alt_pipe.image_processor = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor, do_normalize=False)
+        alt_pipe.image_processor = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor, do_normalize=True)
         alt_pipe = alt_pipe.to(device)
         alt_pipe.set_progress_bar_config(disable=None)
 

tests/pipelines/paint_by_example/test_paint_by_example.py

@@ -38,6 +38,7 @@ class PaintByExamplePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = PaintByExamplePipeline
     params = IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
     batch_params = IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
+    image_params = frozenset([])  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/stable_diffusion/test_cycle_diffusion.py

@@ -26,13 +26,13 @@ from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_d
 from diffusers.utils.testing_utils import require_torch_gpu, skip_mps
 
 from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-class CycleDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class CycleDiffusionPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = CycleDiffusionPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         "negative_prompt",
@@ -42,6 +42,7 @@ class CycleDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     }
     required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
     batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"})
+    image_params = frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -42,17 +42,18 @@ from diffusers.utils import load_numpy, nightly, slow, torch_device
 from diffusers.utils.testing_utils import CaptureLogger, require_torch_gpu
 
 from ...models.test_models_unet_2d_condition import create_lora_layers
-from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-class StableDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class StableDiffusionPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = StableDiffusionPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/stable_diffusion/test_stable_diffusion_controlnet.py

@@ -35,13 +35,14 @@ from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import require_torch_gpu
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
-class StableDiffusionControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class StableDiffusionControlNetPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = StableDiffusionControlNetPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/stable_diffusion/test_stable_diffusion_image_variation.py

@@ -33,16 +33,21 @@ from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow
 from diffusers.utils.testing_utils import require_torch_gpu
 
 from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-class StableDiffusionImageVariationPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class StableDiffusionImageVariationPipelineFastTests(
+    PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
+):
     pipeline_class = StableDiffusionImageVariationPipeline
     params = IMAGE_VARIATION_PARAMS
     batch_params = IMAGE_VARIATION_BATCH_PARAMS
+    image_params = frozenset(
+        []
+    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -36,16 +36,19 @@ from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow
 from diffusers.utils.testing_utils import require_torch_gpu
 
 from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class StableDiffusionInpaintPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = StableDiffusionInpaintPipeline
     params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
+    image_params = frozenset(
+        []
+    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py

@@ -35,16 +35,21 @@ from diffusers.utils import floats_tensor, load_image, slow, torch_device
 from diffusers.utils.testing_utils import require_torch_gpu
 
 from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-class StableDiffusionInstructPix2PixPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class StableDiffusionInstructPix2PixPipelineFastTests(
+    PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
+):
     pipeline_class = StableDiffusionInstructPix2PixPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
     batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
+    image_params = frozenset(
+        []
+    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/stable_diffusion/test_stable_diffusion_model_editing.py

@@ -31,18 +31,19 @@ from diffusers import (
 from diffusers.utils import slow, torch_device
 from diffusers.utils.testing_utils import require_torch_gpu, skip_mps
 
-from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
 @skip_mps
-class StableDiffusionModelEditingPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class StableDiffusionModelEditingPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = StableDiffusionModelEditingPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/stable_diffusion/test_stable_diffusion_panorama.py

@@ -32,18 +32,19 @@ from diffusers import (
 from diffusers.utils import slow, torch_device
 from diffusers.utils.testing_utils import require_torch_gpu, skip_mps
 
-from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
 @skip_mps
-class StableDiffusionPanoramaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class StableDiffusionPanoramaPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = StableDiffusionPanoramaPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
 
     def get_dummy_components(self):
         torch.manual_seed(0)

tests/pipelines/stable_diffusion/test_stable_diffusion_pix2pix_zero.py

@@ -36,17 +36,20 @@ from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
 from diffusers.utils.testing_utils import load_image, load_pt, require_torch_gpu, skip_mps
 
 from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
 @skip_mps
-class StableDiffusionPix2PixZeroPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class StableDiffusionPix2PixZeroPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = StableDiffusionPix2PixZeroPipeline
     params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
     batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
+    image_params = frozenset(
+        []
+    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 
     @classmethod
     def setUpClass(cls):

tests/pipelines/stable_diffusion/test_stable_diffusion_sag.py

@@ -29,17 +29,18 @@ from diffusers import (
 from diffusers.utils import slow, torch_device
 from diffusers.utils.testing_utils import require_torch_gpu
 
-from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-class StableDiffusionSAGPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class StableDiffusionSAGPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = StableDiffusionSAGPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
     test_cpu_offload = False
 
     def get_dummy_components(self):

tests/pipelines/stable_diffusion_2/test_stable_diffusion.py

@@ -35,17 +35,18 @@ from diffusers import (
 from diffusers.utils import load_numpy, nightly, slow, torch_device
 from diffusers.utils.testing_utils import CaptureLogger, require_torch_gpu
 
-from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-class StableDiffusion2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+class StableDiffusion2PipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_class = StableDiffusionPipeline
     params = TEXT_TO_IMAGE_PARAMS
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
 
     def get_dummy_components(self):
         torch.manual_seed(0)