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main/README.md

@@ -4865,7 +4865,7 @@ python -m pip install intel_extension_for_pytorch
 ```
 python -m pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
 ```
-2. After pipeline initialization, `prepare_for_ipex()` should be called to enable IPEX accelaration. Supported inference datatypes are Float32 and BFloat16.
+2. After pipeline initialization, `prepare_for_ipex()` should be called to enable IPEX acceleration. Supported inference datatypes are Float32 and BFloat16.
 
 ```python
 pipe = AnimateDiffPipelineIpex.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device)

main/dps_pipeline.py

@@ -336,13 +336,13 @@ if __name__ == "__main__":
                     expanded_kernel_width = np.ceil(kernel_width) + 2
 
                     # Determine a set of field_of_view for each each output position, these are the pixels in the input image
-                    # that the pixel in the output image 'sees'. We get a matrix whos horizontal dim is the output pixels (big) and the
+                    # that the pixel in the output image 'sees'. We get a matrix whose horizontal dim is the output pixels (big) and the
                     # vertical dim is the pixels it 'sees' (kernel_size + 2)
                     field_of_view = np.squeeze(
                         np.int16(np.expand_dims(left_boundary, axis=1) + np.arange(expanded_kernel_width) - 1)
                     )
 
-                    # Assign weight to each pixel in the field of view. A matrix whos horizontal dim is the output pixels and the
+                    # Assign weight to each pixel in the field of view. A matrix whose horizontal dim is the output pixels and the
                     # vertical dim is a list of weights matching to the pixel in the field of view (that are specified in
                     # 'field_of_view')
                     weights = fixed_kernel(1.0 * np.expand_dims(match_coordinates, axis=1) - field_of_view - 1)

main/hd_painter.py

@@ -201,16 +201,16 @@ class PAIntAAttnProcessor:
         # ================================================== #
         # We use a hack by running the code from the BasicTransformerBlock that is between Self and Cross attentions here
         # The other option would've been modifying the BasicTransformerBlock and adding this functionality here.
-        # I assumed that changing the BasicTransformerBlock would have been a bigger deal and decided to use this hack isntead.
+        # I assumed that changing the BasicTransformerBlock would have been a bigger deal and decided to use this hack instead.
 
-        # The SelfAttention block recieves the normalized latents from the BasicTransformerBlock,
+        # The SelfAttention block receives the normalized latents from the BasicTransformerBlock,
         # But the residual of the output is the non-normalized version.
         # Therefore we unnormalize the input hidden state here
         unnormalized_input_hidden_states = (
             input_hidden_states + self.transformer_block.norm1.bias
         ) * self.transformer_block.norm1.weight
 
-        # TODO: return if neccessary
+        # TODO: return if necessary
         # if self.use_ada_layer_norm_zero:
         #     attn_output = gate_msa.unsqueeze(1) * attn_output
         # elif self.use_ada_layer_norm_single:
@@ -220,7 +220,7 @@ class PAIntAAttnProcessor:
         if transformer_hidden_states.ndim == 4:
             transformer_hidden_states = transformer_hidden_states.squeeze(1)
 
-        # TODO: return if neccessary
+        # TODO: return if necessary
         # 2.5 GLIGEN Control
         # if gligen_kwargs is not None:
         #     transformer_hidden_states = self.fuser(transformer_hidden_states, gligen_kwargs["objs"])
@@ -266,7 +266,7 @@ class PAIntAAttnProcessor:
             ) = cross_attention_input_hidden_states.chunk(2)
 
             # Same split for the encoder_hidden_states i.e. the tokens
-            # Since the SelfAttention processors don't get the encoder states as input, we inject them into the processor in the begining.
+            # Since the SelfAttention processors don't get the encoder states as input, we inject them into the processor in the beginning.
             _encoder_hidden_states_unconditional, encoder_hidden_states_conditional = self.encoder_hidden_states.chunk(
                 2
             )
@@ -896,7 +896,7 @@ class StableDiffusionHDPainterPipeline(StableDiffusionInpaintPipeline):
 class GaussianSmoothing(nn.Module):
     """
     Apply gaussian smoothing on a
-    1d, 2d or 3d tensor. Filtering is performed seperately for each channel
+    1d, 2d or 3d tensor. Filtering is performed separately for each channel
     in the input using a depthwise convolution.
 
     Args:

main/img2img_inpainting.py

@@ -161,7 +161,7 @@ class ImageToImageInpaintingPipeline(DiffusionPipeline):
                 `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
                 be masked out with `mask_image` and repainted according to `prompt`.
             inner_image (`torch.Tensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch which will be overlayed onto `image`. Non-transparent
+                `Image`, or tensor representing an image batch which will be overlaid onto `image`. Non-transparent
                 regions of `inner_image` must fit inside white pixels in `mask_image`. Expects four channels, with
                 the last channel representing the alpha channel, which will be used to blend `inner_image` with
                 `image`. If not provided, it will be forcibly cast to RGBA.

main/latent_consistency_img2img.py

@@ -647,7 +647,7 @@ class LCMSchedulerWithTimestamp(SchedulerMixin, ConfigMixin):
         return sample
 
     def set_timesteps(
-        self, stength, num_inference_steps: int, lcm_origin_steps: int, device: Union[str, torch.device] = None
+        self, strength, num_inference_steps: int, lcm_origin_steps: int, device: Union[str, torch.device] = None
     ):
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
@@ -668,7 +668,7 @@ class LCMSchedulerWithTimestamp(SchedulerMixin, ConfigMixin):
         # LCM Timesteps Setting:  # Linear Spacing
         c = self.config.num_train_timesteps // lcm_origin_steps
         lcm_origin_timesteps = (
-            np.asarray(list(range(1, int(lcm_origin_steps * stength) + 1))) * c - 1
+            np.asarray(list(range(1, int(lcm_origin_steps * strength) + 1))) * c - 1
         )  # LCM Training  Steps Schedule
         skipping_step = len(lcm_origin_timesteps) // num_inference_steps
         timesteps = lcm_origin_timesteps[::-skipping_step][:num_inference_steps]  # LCM Inference Steps Schedule

main/magic_mix.py

@@ -129,7 +129,7 @@ class MagicMixPipeline(DiffusionPipeline):
 
                     input = (
                         (mix_factor * latents) + (1 - mix_factor) * orig_latents
-                    )  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
+                    )  # interpolating between layout noise and conditionally generated noise to preserve layout semantics
                     input = torch.cat([input] * 2)
 
                 else:  # content generation phase

main/mixture_tiling.py

@@ -196,9 +196,9 @@ class StableDiffusionTilingPipeline(DiffusionPipeline, StableDiffusionExtrasMixi
             guidance_scale_tiles: specific weights for classifier-free guidance in each tile.
             guidance_scale_tiles: specific weights for classifier-free guidance in each tile. If None, the value provided in guidance_scale will be used.
             seed_tiles: specific seeds for the initialization latents in each tile. These will override the latents generated for the whole canvas using the standard seed parameter.
-            seed_tiles_mode: either "full" "exclusive". If "full", all the latents affected by the tile be overriden. If "exclusive", only the latents that are affected exclusively by this tile (and no other tiles) will be overriden.
-            seed_reroll_regions: a list of tuples in the form (start row, end row, start column, end column, seed) defining regions in pixel space for which the latents will be overriden using the given seed. Takes priority over seed_tiles.
-            cpu_vae: the decoder from latent space to pixel space can require too mucho GPU RAM for large images. If you find out of memory errors at the end of the generation process, try setting this parameter to True to run the decoder in CPU. Slower, but should run without memory issues.
+            seed_tiles_mode: either "full" "exclusive". If "full", all the latents affected by the tile be overridden. If "exclusive", only the latents that are affected exclusively by this tile (and no other tiles) will be overridden.
+            seed_reroll_regions: a list of tuples in the form (start row, end row, start column, end column, seed) defining regions in pixel space for which the latents will be overridden using the given seed. Takes priority over seed_tiles.
+            cpu_vae: the decoder from latent space to pixel space can require too much GPU RAM for large images. If you find out of memory errors at the end of the generation process, try setting this parameter to True to run the decoder in CPU. Slower, but should run without memory issues.
 
         Examples:
 

main/pipeline_controlnet_xl_kolors.py

@@ -1258,7 +1258,7 @@ class KolorsControlNetPipeline(
                     )
 
                     if guess_mode and self.do_classifier_free_guidance:
-                        # Infered ControlNet only for the conditional batch.
+                        # Inferred ControlNet only for the conditional batch.
                         # To apply the output of ControlNet to both the unconditional and conditional batches,
                         # add 0 to the unconditional batch to keep it unchanged.
                         down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

main/pipeline_controlnet_xl_kolors_img2img.py

@@ -1462,7 +1462,7 @@ class KolorsControlNetImg2ImgPipeline(
                     )
 
                     if guess_mode and self.do_classifier_free_guidance:
-                        # Infered ControlNet only for the conditional batch.
+                        # Inferred ControlNet only for the conditional batch.
                         # To apply the output of ControlNet to both the unconditional and conditional batches,
                         # add 0 to the unconditional batch to keep it unchanged.
                         down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

main/pipeline_controlnet_xl_kolors_inpaint.py

@@ -1782,7 +1782,7 @@ class KolorsControlNetInpaintPipeline(
                     )
 
                     if guess_mode and self.do_classifier_free_guidance:
-                        # Infered ControlNet only for the conditional batch.
+                        # Inferred ControlNet only for the conditional batch.
                         # To apply the output of ControlNet to both the unconditional and conditional batches,
                         # add 0 to the unconditional batch to keep it unchanged.
                         down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]

main/pipeline_fabric.py

@@ -559,7 +559,7 @@ class FabricPipeline(DiffusionPipeline):
                 End point for providing feedback (between 0 and 1).
             min_weight (`float`, *optional*, defaults to `.05`):
                 Minimum weight for feedback.
-            max_weight (`float`, *optional*, defults tp `1.0`):
+            max_weight (`float`, *optional*, defaults tp `1.0`):
                 Maximum weight for feedback.
             neg_scale (`float`, *optional*, defaults to `.5`):
                 Scale factor for negative feedback.

main/pipeline_faithdiff_stable_diffusion_xl.py

@@ -118,7 +118,7 @@ EXAMPLE_DOC_STRING = """
         >>> # Here we need use pipeline internal unet model
         >>> pipe.unet = pipe.unet_model.from_pretrained(model_id, subfolder="unet", variant="fp16", use_safetensors=True)
         >>>
-        >>> # Load aditional layers to the model
+        >>> # Load additional layers to the model
         >>> pipe.unet.load_additional_layers(weight_path="proc_data/faithdiff/FaithDiff.bin", dtype=dtype)
         >>>
         >>> # Enable vae tiling

main/pipeline_stable_diffusion_boxdiff.py

@@ -72,7 +72,7 @@ class GaussianSmoothing(nn.Module):
     """
     Copied from official repo: https://github.com/showlab/BoxDiff/blob/master/utils/gaussian_smoothing.py
     Apply gaussian smoothing on a
-    1d, 2d or 3d tensor. Filtering is performed seperately for each channel
+    1d, 2d or 3d tensor. Filtering is performed separately for each channel
     in the input using a depthwise convolution.
     Arguments:
         channels (int, sequence): Number of channels of the input tensors. Output will

main/pipeline_stable_diffusion_xl_attentive_eraser.py

@@ -1509,7 +1509,7 @@ class StableDiffusionXL_AE_Pipeline(
 
         add_time_ids = add_time_ids.repeat(batch_size, 1).to(DEVICE)
 
-        # interative sampling
+        # interactive sampling
         self.scheduler.set_timesteps(num_inference_steps)
         latents_list = [latents]
         pred_x0_list = []
@@ -1548,7 +1548,7 @@ class StableDiffusionXL_AE_Pipeline(
         x: torch.FloatTensor,
     ):
         """
-        predict the sampe the next step in the denoise process.
+        predict the sample the next step in the denoise process.
         """
         ref_noise = model_output[:1, :, :, :].expand(model_output.shape)
         alpha_prod_t = self.scheduler.alphas_cumprod[timestep]

main/pipeline_stable_diffusion_xl_controlnet_adapter.py

@@ -132,7 +132,7 @@ def _preprocess_adapter_image(image, height, width):
             image = torch.cat(image, dim=0)
         else:
             raise ValueError(
-                f"Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but recive: {image[0].ndim}"
+                f"Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but receive: {image[0].ndim}"
             )
     return image
 

main/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py

@@ -150,7 +150,7 @@ def _preprocess_adapter_image(image, height, width):
             image = torch.cat(image, dim=0)
         else:
             raise ValueError(
-                f"Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but recive: {image[0].ndim}"
+                f"Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but receive: {image[0].ndim}"
             )
     return image
 

main/regional_prompting_stable_diffusion.py

@@ -220,7 +220,7 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
             revers = True
 
             def pcallback(s_self, step: int, timestep: int, latents: torch.Tensor, selfs=None):
-                if "PRO" in mode:  # in Prompt mode, make masks from sum of attension maps
+                if "PRO" in mode:  # in Prompt mode, make masks from sum of attention maps
                     self.step = step
 
                     if len(self.attnmaps_sizes) > 3:
@@ -552,9 +552,9 @@ def get_attn_maps(self, attn):
 
 def reset_attnmaps(self):  # init parameters in every batch
     self.step = 0
-    self.attnmaps = {}  # maked from attention maps
+    self.attnmaps = {}  # made from attention maps
     self.attnmaps_sizes = []  # height,width set of u-net blocks
-    self.attnmasks = {}  # maked from attnmaps for regions
+    self.attnmasks = {}  # made from attnmaps for regions
     self.maskready = False
     self.history = {}
 

main/sde_drag.py

@@ -97,7 +97,7 @@ class SdeDragPipeline(DiffusionPipeline):
             steps (`int`, *optional*, defaults to 200):
                 The number of sampling iterations.
             step_size (`int`, *optional*, defaults to 2):
-                The drag diatance of each drag step.
+                The drag distance of each drag step.
             image_scale (`float`, *optional*, defaults to 0.3):
                 To avoid duplicating the content, use image_scale to perturbs the source.
             adapt_radius (`int`, *optional*, defaults to 5):

main/unclip_image_interpolation.py

@@ -284,7 +284,7 @@ class UnCLIPImageInterpolationPipeline(DiffusionPipeline):
                 )
         else:
             raise AssertionError(
-                f"Expected 'image' or 'image_embeddings' to be not None with types List[PIL.Image] or torch.Tensor respectively. Received {type(image)} and {type(image_embeddings)} repsectively"
+                f"Expected 'image' or 'image_embeddings' to be not None with types List[PIL.Image] or torch.Tensor respectively. Received {type(image)} and {type(image_embeddings)} respectively"
             )
 
         original_image_embeddings = self._encode_image(