[SD Img2Img] resize source images to multiple of 8 instead of 32 (#1571)

* [Stable Diffusion Img2Img] resize source images to integer multiple of 8 instead of 32

* [Alt Diffusion Img2Img] resize source images to multiple of 8 instead of 32

* [Img2Img] fix AltDiffusion Img2Img resolution test

* [Img2Img] add Stable Diffusion Img2Img resolution test

* [Cycle Diffusion] round resolution to multiplies of 8 instead of 32

* [ONNX SD Img2Img] round resolution to multiplies of 64 instead of 32

* [SD Depth2Img] round resolution to multiplies of 8 instead of 32

* [Repaint] round resolution to multiplies of 8 instead of 32

* fix make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -80,7 +80,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/repaint/pipeline_repaint.py

@@ -38,7 +38,7 @@ def _preprocess_image(image: Union[List, PIL.Image.Image, torch.Tensor]):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py

@@ -44,7 +44,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -32,7 +32,7 @@ from . import StableDiffusionPipelineOutput
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess with 8->64
 def preprocess(image):
     if isinstance(image, torch.Tensor):
         return image
@@ -41,7 +41,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+        w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 64
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py

@@ -49,7 +49,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -84,7 +84,7 @@ def preprocess(image):
 
     if isinstance(image[0], PIL.Image.Image):
         w, h = image[0].size
-        w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+        w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
 
         image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
         image = np.concatenate(image, axis=0)

tests/pipelines/altdiffusion/test_alt_diffusion_img2img.py

@@ -207,6 +207,43 @@ class AltDiffusionImg2ImgPipelineFastTests(unittest.TestCase):
 
         assert image.shape == (1, 32, 32, 3)
 
+    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
+    def test_stable_diffusion_img2img_pipeline_multiple_of_8(self):
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/img2img/sketch-mountains-input.jpg"
+        )
+        # resize to resolution that is divisible by 8 but not 16 or 32
+        init_image = init_image.resize((760, 504))
+
+        model_id = "BAAI/AltDiffusion"
+        pipe = AltDiffusionImg2ImgPipeline.from_pretrained(
+            model_id,
+            safety_checker=None,
+        )
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "A fantasy landscape, trending on artstation"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output = pipe(
+            prompt=prompt,
+            image=init_image,
+            strength=0.75,
+            guidance_scale=7.5,
+            generator=generator,
+            output_type="np",
+        )
+        image = output.images[0]
+
+        image_slice = image[255:258, 383:386, -1]
+
+        assert image.shape == (504, 760, 3)
+        expected_slice = np.array([0.3252, 0.3340, 0.3418, 0.3263, 0.3346, 0.3300, 0.3163, 0.3470, 0.3427])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
+
 
 @slow
 @require_torch_gpu

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -333,6 +333,42 @@ class StableDiffusionImg2ImgPipelineSlowTests(unittest.TestCase):
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9
 
+    def test_stable_diffusion_img2img_pipeline_multiple_of_8(self):
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/img2img/sketch-mountains-input.jpg"
+        )
+        # resize to resolution that is divisible by 8 but not 16 or 32
+        init_image = init_image.resize((760, 504))
+
+        model_id = "CompVis/stable-diffusion-v1-4"
+        pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+            model_id,
+            safety_checker=None,
+        )
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "A fantasy landscape, trending on artstation"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output = pipe(
+            prompt=prompt,
+            image=init_image,
+            strength=0.75,
+            guidance_scale=7.5,
+            generator=generator,
+            output_type="np",
+        )
+        image = output.images[0]
+
+        image_slice = image[255:258, 383:386, -1]
+
+        assert image.shape == (504, 760, 3)
+        expected_slice = np.array([0.7124, 0.7105, 0.6993, 0.7140, 0.7106, 0.6945, 0.7198, 0.7172, 0.7031])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
+
 
 @nightly
 @require_torch_gpu