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make style

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Aryan
2024-10-25 11:58:12 +02:00
parent 72741ec503
commit cae2801bfb
2 changed files with 1090 additions and 8 deletions
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1077
src/diffusers/models/transformers/transformer_mochi_original.py Normal file
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21
src/diffusers/pipelines/mochi/pipeline_mochi.py
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@@ -13,7 +13,7 @@
# limitations under the License.
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
from typing import Callable, Dict, List, Optional, Union
import numpy as np
import torch
@@ -255,7 +255,7 @@ class MochiPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1)
@@ -377,7 +377,7 @@ class MochiPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if prompt_embeds is not None and prompt_attention_mask is None:
raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
@@ -397,7 +397,7 @@ class MochiPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
f" {negative_prompt_attention_mask.shape}."
)
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
@@ -563,13 +563,13 @@ class MochiPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
Returns:
[`~pipelines.mochi.MochiPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.mochi.MochiPipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images.
If `return_dict` is `True`, [`~pipelines.mochi.MochiPipelineOutput`] is returned, otherwise a `tuple`
is returned where the first element is a list with the generated images.
"""
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
height = height or self.default_height
width = width or self.default_width
@@ -599,7 +599,12 @@ class MochiPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
device = self._execution_device
# 3. Prepare text embeddings
(prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask) = self.encode_prompt(
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt=prompt,
negative_prompt=negative_prompt,
do_classifier_free_guidance=self.do_classifier_free_guidance,