Merge remote-tracking branch 'origin/main'

MANIFEST.in

+include LICENSE
 include src/diffusers/utils/model_card_template.md

README.md

 <p align="center">
     <br>
-    <img src="docs/source/imgs/diffusers_library.jpg" width="400"/>
+    <img src="https://github.com/huggingface/diffusers/raw/main/docs/source/imgs/diffusers_library.jpg" width="400"/>
     <br>
 <p>
 <p align="center">

src/diffusers/pipeline_utils.py

@@ -50,6 +50,7 @@ if is_transformers_available():
 
 INDEX_FILE = "diffusion_pytorch_model.bin"
 CUSTOM_PIPELINE_FILE_NAME = "pipeline.py"
+DUMMY_MODULES_FOLDER = "diffusers.utils"
 
 
 logger = logging.get_logger(__name__)
@@ -473,9 +474,20 @@ class DiffusionPipeline(ConfigMixin):
                     if issubclass(class_obj, class_candidate):
                         load_method_name = importable_classes[class_name][1]
 
-                load_method = getattr(class_obj, load_method_name)
+                if load_method_name is None:
+                    none_module = class_obj.__module__
+                    if none_module.startswith(DUMMY_MODULES_FOLDER) and "dummy" in none_module:
+                        # call class_obj for nice error message of missing requirements
+                        class_obj()
+
+                    raise ValueError(
+                        f"The component {class_obj} of {pipeline_class} cannot be loaded as it does not seem to have"
+                        f" any of the loading methods defined in {ALL_IMPORTABLE_CLASSES}."
+                    )
 
+                load_method = getattr(class_obj, load_method_name)
                 loading_kwargs = {}
+
                 if issubclass(class_obj, torch.nn.Module):
                     loading_kwargs["torch_dtype"] = torch_dtype
                 if issubclass(class_obj, diffusers.OnnxRuntimeModel):

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -195,6 +195,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         """
         if isinstance(prompt, str):
             batch_size = 1
+            prompt = [prompt]
         elif isinstance(prompt, list):
             batch_size = len(prompt)
         else:
@@ -284,8 +285,23 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         init_latents = init_latent_dist.sample(generator=generator)
         init_latents = 0.18215 * init_latents
 
-        # expand init_latents for batch_size
-        init_latents = torch.cat([init_latents] * batch_size * num_images_per_prompt, dim=0)
+        if len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            deprecation_message = (
+                f"You have passed {len(prompt)} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
+                " images (`init_image`). Initial images are now duplicating to match the number of text prompts. Note"
+                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
+                " your script to pass as many init images as text prompts to suppress this warning."
+            )
+            deprecate("len(prompt) != len(init_image)", "1.0.0", deprecation_message, standard_warn=False)
+            additional_image_per_prompt = len(prompt) // init_latents.shape[0]
+            init_latents = torch.cat([init_latents] * additional_image_per_prompt * num_images_per_prompt, dim=0)
+        elif len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `init_image` of batch size {init_latents.shape[0]} to {len(prompt)} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)
 
         # get the original timestep using init_timestep
         offset = self.scheduler.config.get("steps_offset", 0)

src/diffusers/utils/dummy_flax_and_transformers_objects.py

@@ -9,3 +9,11 @@ class FlaxStableDiffusionPipeline(metaclass=DummyObject):
 
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax", "transformers"])

src/diffusers/utils/dummy_flax_objects.py

@@ -10,6 +10,14 @@ class FlaxModelMixin(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
 
 class FlaxUNet2DConditionModel(metaclass=DummyObject):
     _backends = ["flax"]
@@ -17,6 +25,14 @@ class FlaxUNet2DConditionModel(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
 
 class FlaxAutoencoderKL(metaclass=DummyObject):
     _backends = ["flax"]
@@ -24,6 +40,14 @@ class FlaxAutoencoderKL(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
 
 class FlaxDiffusionPipeline(metaclass=DummyObject):
     _backends = ["flax"]
@@ -31,6 +55,14 @@ class FlaxDiffusionPipeline(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
 
 class FlaxDDIMScheduler(metaclass=DummyObject):
     _backends = ["flax"]
@@ -38,6 +70,14 @@ class FlaxDDIMScheduler(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
 
 class FlaxDDPMScheduler(metaclass=DummyObject):
     _backends = ["flax"]
@@ -45,6 +85,14 @@ class FlaxDDPMScheduler(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
 
 class FlaxKarrasVeScheduler(metaclass=DummyObject):
     _backends = ["flax"]
@@ -52,6 +100,14 @@ class FlaxKarrasVeScheduler(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
 
 class FlaxLMSDiscreteScheduler(metaclass=DummyObject):
     _backends = ["flax"]
@@ -59,6 +115,14 @@ class FlaxLMSDiscreteScheduler(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
 
 class FlaxPNDMScheduler(metaclass=DummyObject):
     _backends = ["flax"]
@@ -66,6 +130,14 @@ class FlaxPNDMScheduler(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
 
 class FlaxSchedulerMixin(metaclass=DummyObject):
     _backends = ["flax"]
@@ -73,9 +145,25 @@ class FlaxSchedulerMixin(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
 
 class FlaxScoreSdeVeScheduler(metaclass=DummyObject):
     _backends = ["flax"]
 
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])

src/diffusers/utils/dummy_pt_objects.py

@@ -10,6 +10,14 @@ class ModelMixin(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class AutoencoderKL(metaclass=DummyObject):
     _backends = ["torch"]
@@ -17,6 +25,14 @@ class AutoencoderKL(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class UNet2DConditionModel(metaclass=DummyObject):
     _backends = ["torch"]
@@ -24,6 +40,14 @@ class UNet2DConditionModel(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class UNet2DModel(metaclass=DummyObject):
     _backends = ["torch"]
@@ -31,6 +55,14 @@ class UNet2DModel(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class VQModel(metaclass=DummyObject):
     _backends = ["torch"]
@@ -38,6 +70,14 @@ class VQModel(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 def get_constant_schedule(*args, **kwargs):
     requires_backends(get_constant_schedule, ["torch"])
@@ -73,6 +113,14 @@ class DiffusionPipeline(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class DDIMPipeline(metaclass=DummyObject):
     _backends = ["torch"]
@@ -80,6 +128,14 @@ class DDIMPipeline(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class DDPMPipeline(metaclass=DummyObject):
     _backends = ["torch"]
@@ -87,6 +143,14 @@ class DDPMPipeline(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class KarrasVePipeline(metaclass=DummyObject):
     _backends = ["torch"]
@@ -94,6 +158,14 @@ class KarrasVePipeline(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class LDMPipeline(metaclass=DummyObject):
     _backends = ["torch"]
@@ -101,6 +173,14 @@ class LDMPipeline(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class PNDMPipeline(metaclass=DummyObject):
     _backends = ["torch"]
@@ -108,6 +188,14 @@ class PNDMPipeline(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class ScoreSdeVePipeline(metaclass=DummyObject):
     _backends = ["torch"]
@@ -115,6 +203,14 @@ class ScoreSdeVePipeline(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class DDIMScheduler(metaclass=DummyObject):
     _backends = ["torch"]
@@ -122,6 +218,14 @@ class DDIMScheduler(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class DDPMScheduler(metaclass=DummyObject):
     _backends = ["torch"]
@@ -129,6 +233,14 @@ class DDPMScheduler(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class KarrasVeScheduler(metaclass=DummyObject):
     _backends = ["torch"]
@@ -136,6 +248,14 @@ class KarrasVeScheduler(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class PNDMScheduler(metaclass=DummyObject):
     _backends = ["torch"]
@@ -143,6 +263,14 @@ class PNDMScheduler(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class SchedulerMixin(metaclass=DummyObject):
     _backends = ["torch"]
@@ -150,6 +278,14 @@ class SchedulerMixin(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class ScoreSdeVeScheduler(metaclass=DummyObject):
     _backends = ["torch"]
@@ -157,9 +293,25 @@ class ScoreSdeVeScheduler(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
 
 class EMAModel(metaclass=DummyObject):
     _backends = ["torch"]
 
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])

src/diffusers/utils/dummy_torch_and_scipy_objects.py

@@ -9,3 +9,11 @@ class LMSDiscreteScheduler(metaclass=DummyObject):
 
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch", "scipy"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "scipy"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "scipy"])

src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py

@@ -9,3 +9,11 @@ class StableDiffusionOnnxPipeline(metaclass=DummyObject):
 
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch", "transformers", "onnx"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers", "onnx"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers", "onnx"])

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -10,6 +10,14 @@ class LDMTextToImagePipeline(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch", "transformers"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
 
 class StableDiffusionImg2ImgPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
@@ -17,6 +25,14 @@ class StableDiffusionImg2ImgPipeline(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch", "transformers"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
 
 class StableDiffusionInpaintPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
@@ -24,9 +40,25 @@ class StableDiffusionInpaintPipeline(metaclass=DummyObject):
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch", "transformers"])
 
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
 
 class StableDiffusionPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])

tests/test_pipelines.py

@@ -492,6 +492,12 @@ class PipelineFastTests(unittest.TestCase):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
 
+    def test_from_pretrained_error_message_uninstalled_packages(self):
+        # TODO(Patrick, Pedro) - need better test here for the future
+        pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-lms-pipe")
+        assert isinstance(pipe, StableDiffusionPipeline)
+        assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
+
     def test_stable_diffusion_k_lms(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         unet = self.dummy_cond_unet
@@ -698,6 +704,48 @@ class PipelineFastTests(unittest.TestCase):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
 
+    def test_stable_diffusion_img2img_multiple_init_images(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        scheduler = PNDMScheduler(skip_prk_steps=True)
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        init_image = self.dummy_image.to(device).repeat(2, 1, 1, 1)
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionImg2ImgPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=self.dummy_safety_checker,
+            feature_extractor=self.dummy_extractor,
+        )
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = 2 * ["A painting of a squirrel eating a burger"]
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe(
+            prompt,
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+        )
+
+        image = output.images
+
+        image_slice = image[-1, -3:, -3:, -1]
+
+        assert image.shape == (2, 32, 32, 3)
+        expected_slice = np.array([0.5144, 0.4447, 0.4735, 0.6676, 0.5526, 0.5454, 0.645, 0.5149, 0.4689])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
     def test_stable_diffusion_img2img_k_lms(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         unet = self.dummy_cond_unet

utils/check_dummies.py

@@ -38,6 +38,14 @@ class {0}(metaclass=DummyObject):
 
     def __init__(self, *args, **kwargs):
         requires_backends(self, {1})
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, {1})
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, {1})
 """