Make sure all pipelines can run with batched input

src/diffusers/models/unet_1d.py

@@ -218,6 +218,7 @@ def forward(
         else:
             timestep_embed = timestep_embed[..., None]
             timestep_embed = timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
+            timestep_embed = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))
 
         # 2. down
         down_block_res_samples = ()

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py

@@ -249,9 +249,9 @@ def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_fr
             return_tensors="pt",
         )
         text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
 
-        if not torch.equal(text_input_ids, untruncated_ids):
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
             removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
             logger.warning(
                 "The following part of your input was truncated because CLIP can only handle sequences up to"

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -44,13 +44,24 @@
 
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
 def preprocess(image):
-    w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-    return 2.0 * image - 1.0
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [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
+
+        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = 2.0 * image - 1.0
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        image = torch.cat(image, dim=0)
+    return image
 
 
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker
@@ -81,7 +92,7 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
-    _optional_components = ["safety_checker", "feature_extractor"]
+    _optional_components = ["safety_checker"]
 
     def __init__(
         self,
@@ -246,9 +257,9 @@ def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_fr
             return_tensors="pt",
         )
         text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
 
-        if not torch.equal(text_input_ids, untruncated_ids):
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
             removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
             logger.warning(
                 "The following part of your input was truncated because CLIP can only handle sequences up to"
@@ -510,8 +521,7 @@ def __call__(
         )
 
         # 4. Preprocess image
-        if isinstance(image, PIL.Image.Image):
-            image = preprocess(image)
+        image = preprocess(image)
 
         # 5. set timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -46,7 +46,6 @@ def __call__(
         use_clipped_model_output: Optional[bool] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
-        **kwargs,
     ) -> Union[ImagePipelineOutput, Tuple]:
         r"""
         Args:

src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py

@@ -109,16 +109,18 @@ def prepare_mask_and_masked_image(image, mask):
         raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
     else:
         if isinstance(image, PIL.Image.Image):
-            image = np.array(image.convert("RGB"))
+            image = [image]
 
-        image = image[None].transpose(0, 3, 1, 2)
+        image = np.concatenate([np.array(i.convert("RGB"))[None, :] for i in image], axis=0)
+        image = image.transpose(0, 3, 1, 2)
         image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
 
+        # preprocess mask
         if isinstance(mask, PIL.Image.Image):
-            mask = np.array(mask.convert("L"))
-            mask = mask.astype(np.float32) / 255.0
+            mask = [mask]
 
-        mask = mask[None, None]
+        mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
+        mask = mask.astype(np.float32) / 255.0
 
         # paint-by-example inverses the mask
         mask = 1 - mask
@@ -159,7 +161,7 @@ class PaintByExamplePipeline(DiffusionPipeline):
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
-    _optional_components = ["safety_checker", "feature_extractor"]
+    _optional_components = ["safety_checker"]
 
     def __init__(
         self,
@@ -323,8 +325,22 @@ def prepare_mask_latents(
         masked_image_latents = 0.18215 * masked_image_latents
 
         # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
-        mask = mask.repeat(batch_size, 1, 1, 1)
-        masked_image_latents = masked_image_latents.repeat(batch_size, 1, 1, 1)
+        if mask.shape[0] < batch_size:
+            if not batch_size % mask.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+        if masked_image_latents.shape[0] < batch_size:
+            if not batch_size % masked_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
 
         mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
         masked_image_latents = (
@@ -351,7 +367,7 @@ def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free
 
         if do_classifier_free_guidance:
             uncond_embeddings = self.image_encoder.uncond_vector
-            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.repeat(1, image_embeddings.shape[0], 1)
             uncond_embeddings = uncond_embeddings.view(bs_embed * num_images_per_prompt, 1, -1)
 
             # For classifier free guidance, we need to do two forward passes.

src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py

@@ -35,14 +35,26 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
 def preprocess(image):
-    w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-    return 2.0 * image - 1.0
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [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
+
+        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = 2.0 * image - 1.0
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        image = torch.cat(image, dim=0)
+    return image
 
 
 def posterior_sample(scheduler, latents, timestep, clean_latents, generator, eta):
@@ -279,9 +291,9 @@ def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_fr
             return_tensors="pt",
         )
         text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
 
-        if not torch.equal(text_input_ids, untruncated_ids):
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
             removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
             logger.warning(
                 "The following part of your input was truncated because CLIP can only handle sequences up to"
@@ -551,8 +563,7 @@ def __call__(
         )
 
         # 4. Preprocess image
-        if isinstance(image, PIL.Image.Image):
-            image = preprocess(image)
+        image = preprocess(image)
 
         # 5. Prepare timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -32,13 +32,26 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
 def preprocess(image):
-    w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    return 2.0 * image - 1.0
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [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
+
+        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = 2.0 * image - 1.0
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        image = torch.cat(image, dim=0)
+    return image
 
 
 class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
@@ -77,7 +90,7 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
     safety_checker: OnnxRuntimeModel
     feature_extractor: CLIPFeatureExtractor
 
-    _optional_components = ["safety_checker", "feature_extractor"]
+    _optional_components = ["safety_checker"]
 
     def __init__(
         self,
@@ -325,8 +338,7 @@ def __call__(
         # set timesteps
         self.scheduler.set_timesteps(num_inference_steps)
 
-        if isinstance(image, PIL.Image.Image):
-            image = preprocess(image)
+        image = preprocess(image)
 
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -248,9 +248,9 @@ def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_fr
             return_tensors="pt",
         )
         text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
 
-        if not torch.equal(text_input_ids, untruncated_ids):
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
             removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
             logger.warning(
                 "The following part of your input was truncated because CLIP can only handle sequences up to"

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py

@@ -41,14 +41,26 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
 def preprocess(image):
-    w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-    return 2.0 * image - 1.0
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [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
+
+        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = 2.0 * image - 1.0
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        image = torch.cat(image, dim=0)
+    return image
 
 
 class StableDiffusionDepth2ImgPipeline(DiffusionPipeline):
@@ -189,9 +201,9 @@ def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_fr
             return_tensors="pt",
         )
         text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
 
-        if not torch.equal(text_input_ids, untruncated_ids):
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
             removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
             logger.warning(
                 "The following part of your input was truncated because CLIP can only handle sequences up to"
@@ -366,12 +378,13 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
 
     def prepare_depth_map(self, image, depth_map, batch_size, do_classifier_free_guidance, dtype, device):
         if isinstance(image, PIL.Image.Image):
-            width, height = image.size
-            width, height = map(lambda dim: dim - dim % 32, (width, height))  # resize to integer multiple of 32
-            image = image.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
-            width, height = image.size
+            image = [image]
         else:
             image = [img for img in image]
+
+        if isinstance(image[0], PIL.Image.Image):
+            width, height = image[0].size
+        else:
             width, height = image[0].shape[-2:]
 
         if depth_map is None:
@@ -493,7 +506,7 @@ def __call__(
             prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
         )
 
-        # 4. Prepare depth mask
+        # 4. Preprocess image
         depth_mask = self.prepare_depth_map(
             image,
             depth_map,
@@ -503,11 +516,8 @@ def __call__(
             device,
         )
 
-        # 5. Preprocess image
-        if isinstance(image, PIL.Image.Image):
-            image = preprocess(image)
-        else:
-            image = 2.0 * (image / 255.0) - 1.0
+        # 5. Prepare depth mask
+        image = preprocess(image)
 
         # 6. set timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py

@@ -65,7 +65,7 @@ class StableDiffusionImageVariationPipeline(DiffusionPipeline):
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
-    _optional_components = ["safety_checker", "feature_extractor"]
+    _optional_components = ["safety_checker"]
 
     def __init__(
         self,