[Core] support for tiny autoencoder in img2img

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -75,6 +75,16 @@
 """
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
 def preprocess(image):
     deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead"
@@ -561,11 +571,12 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
 
             elif isinstance(generator, list):
                 init_latents = [
-                    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                    for i in range(batch_size)
                 ]
                 init_latents = torch.cat(init_latents, dim=0)
             else:
-                init_latents = self.vae.encode(image).latent_dist.sample(generator)
+                init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
 
             init_latents = self.vae.config.scaling_factor * init_latents
 

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -91,6 +91,16 @@
 """
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
 def prepare_image(image):
     if isinstance(image, torch.Tensor):
         # Batch single image
@@ -733,11 +743,12 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
 
             elif isinstance(generator, list):
                 init_latents = [
-                    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                    for i in range(batch_size)
                 ]
                 init_latents = torch.cat(init_latents, dim=0)
             else:
-                init_latents = self.vae.encode(image).latent_dist.sample(generator)
+                init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
 
             init_latents = self.vae.config.scaling_factor * init_latents
 

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -103,6 +103,16 @@
 """
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.prepare_mask_and_masked_image
 def prepare_mask_and_masked_image(image, mask, height, width, return_image=False):
     """
@@ -949,12 +959,12 @@ def prepare_mask_latents(
     def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
         if isinstance(generator, list):
             image_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
+                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                 for i in range(image.shape[0])
             ]
             image_latents = torch.cat(image_latents, dim=0)
         else:
-            image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
+            image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
 
         image_latents = self.vae.config.scaling_factor * image_latents
 

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -131,6 +131,16 @@
 """
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
 class StableDiffusionXLControlNetImg2ImgPipeline(
     DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin
 ):
@@ -806,11 +816,12 @@ def prepare_latents(
 
             elif isinstance(generator, list):
                 init_latents = [
-                    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                    for i in range(batch_size)
                 ]
                 init_latents = torch.cat(init_latents, dim=0)
             else:
-                init_latents = self.vae.encode(image).latent_dist.sample(generator)
+                init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
 
             if self.vae.config.force_upcast:
                 self.vae.to(dtype)

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py

@@ -43,6 +43,16 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
@@ -426,11 +436,12 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
 
             elif isinstance(generator, list):
                 init_latents = [
-                    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                    for i in range(batch_size)
                 ]
                 init_latents = torch.cat(init_latents, dim=0)
             else:
-                init_latents = self.vae.encode(image).latent_dist.sample(generator)
+                init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
 
             init_latents = self.vae.config.scaling_factor * init_latents
 

src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py

@@ -34,6 +34,16 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
 def prepare_mask_and_masked_image(image, mask):
     """
     Prepares a pair (image, mask) to be consumed by the Paint by Example pipeline. This means that those inputs will be
@@ -334,12 +344,12 @@ def prepare_mask_latents(
     def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
         if isinstance(generator, list):
             image_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
+                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                 for i in range(image.shape[0])
             ]
             image_latents = torch.cat(image_latents, dim=0)
         else:
-            image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
+            image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
 
         image_latents = self.vae.config.scaling_factor * image_latents
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py

@@ -36,6 +36,16 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
 def preprocess(image):
     deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead"
@@ -466,11 +476,12 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
 
             elif isinstance(generator, list):
                 init_latents = [
-                    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                    for i in range(batch_size)
                 ]
                 init_latents = torch.cat(init_latents, dim=0)
             else:
-                init_latents = self.vae.encode(image).latent_dist.sample(generator)
+                init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
 
             init_latents = self.vae.config.scaling_factor * init_latents
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -73,6 +73,15 @@
 """
 
 
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
 def preprocess(image):
     deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead"
     deprecate("preprocess", "1.0.0", deprecation_message, standard_warn=False)
@@ -555,11 +564,12 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
 
             elif isinstance(generator, list):
                 init_latents = [
-                    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                    for i in range(batch_size)
                 ]
                 init_latents = torch.cat(init_latents, dim=0)
             else:
-                init_latents = self.vae.encode(image).latent_dist.sample(generator)
+                init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
 
             init_latents = self.vae.config.scaling_factor * init_latents
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -159,6 +159,16 @@ def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool
     return mask, masked_image
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
 class StableDiffusionInpaintPipeline(
     DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
 ):
@@ -654,12 +664,12 @@ def prepare_latents(
     def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
         if isinstance(generator, list):
             image_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
+                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                 for i in range(image.shape[0])
             ]
             image_latents = torch.cat(image_latents, dim=0)
         else:
-            image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
+            image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
 
         image_latents = self.vae.config.scaling_factor * image_latents
 

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -92,6 +92,16 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     return noise_cfg
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
 class StableDiffusionXLImg2ImgPipeline(
     DiffusionPipeline, TextualInversionLoaderMixin, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin
 ):
@@ -604,11 +614,12 @@ def prepare_latents(
 
             elif isinstance(generator, list):
                 init_latents = [
-                    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
+                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                    for i in range(batch_size)
                 ]
                 init_latents = torch.cat(init_latents, dim=0)
             else:
-                init_latents = self.vae.encode(image).latent_dist.sample(generator)
+                init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
 
             if self.vae.config.force_upcast:
                 self.vae.to(dtype)

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -238,6 +238,16 @@ def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool
     return mask, masked_image
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(encoder_output, generator):
+    if hasattr(encoder_output, "latent_dist"):
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
 class StableDiffusionXLInpaintPipeline(
     DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin
 ):
@@ -750,12 +760,12 @@ def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
 
         if isinstance(generator, list):
             image_latents = [
-                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
+                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                 for i in range(image.shape[0])
             ]
             image_latents = torch.cat(image_latents, dim=0)
         else:
-            image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
+            image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
 
         if self.vae.config.force_upcast:
             self.vae.to(dtype)

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -24,6 +24,7 @@
 
 from diffusers import (
     AutoencoderKL,
+    AutoencoderTiny,
     DDIMScheduler,
     DPMSolverMultistepScheduler,
     HeunDiscreteScheduler,
@@ -148,6 +149,9 @@ def get_dummy_components(self):
         }
         return components
 
+    def get_dummy_tiny_autoencoder(self):
+        return AutoencoderTiny(in_channels=3, out_channels=3, latent_channels=4)
+
     def get_dummy_inputs(self, device, seed=0):
         image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
         image = image / 2 + 0.5
@@ -236,6 +240,23 @@ def test_stable_diffusion_img2img_k_lms(self):
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
+    def test_stable_diffusion_img2img_tiny_autoencoder(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionImg2ImgPipeline(**components)
+        sd_pipe.vae = self.get_dummy_tiny_autoencoder()
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = sd_pipe(**inputs).images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 32, 32, 3)
+        expected_slice = np.array([0.00669, 0.00669, 0.0, 0.00693, 0.00858, 0.0, 0.00567, 0.00515, 0.00125])
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
+
     @skip_mps
     def test_save_load_local(self):
         return super().test_save_load_local()