QwenTest
/
pythonProject
/diffusers-main
/src
/diffusers
/modular_pipelines
/wan
/modular_pipeline.py
| # Copyright 2025 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from ...loaders import WanLoraLoaderMixin | |
| from ...pipelines.pipeline_utils import StableDiffusionMixin | |
| from ...utils import logging | |
| from ..modular_pipeline import ModularPipeline | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| class WanModularPipeline( | |
| ModularPipeline, | |
| StableDiffusionMixin, | |
| WanLoraLoaderMixin, | |
| ): | |
| """ | |
| A ModularPipeline for Wan. | |
| <Tip warning={true}> | |
| This is an experimental feature and is likely to change in the future. | |
| </Tip> | |
| """ | |
| def default_height(self): | |
| return self.default_sample_height * self.vae_scale_factor_spatial | |
| def default_width(self): | |
| return self.default_sample_width * self.vae_scale_factor_spatial | |
| def default_num_frames(self): | |
| return (self.default_sample_num_frames - 1) * self.vae_scale_factor_temporal + 1 | |
| def default_sample_height(self): | |
| return 60 | |
| def default_sample_width(self): | |
| return 104 | |
| def default_sample_num_frames(self): | |
| return 21 | |
| def vae_scale_factor_spatial(self): | |
| vae_scale_factor = 8 | |
| if hasattr(self, "vae") and self.vae is not None: | |
| vae_scale_factor = 2 ** len(self.vae.temperal_downsample) | |
| return vae_scale_factor | |
| def vae_scale_factor_temporal(self): | |
| vae_scale_factor = 4 | |
| if hasattr(self, "vae") and self.vae is not None: | |
| vae_scale_factor = 2 ** sum(self.vae.temperal_downsample) | |
| return vae_scale_factor | |
| def num_channels_transformer(self): | |
| num_channels_transformer = 16 | |
| if hasattr(self, "transformer") and self.transformer is not None: | |
| num_channels_transformer = self.transformer.config.in_channels | |
| return num_channels_transformer | |
| def num_channels_latents(self): | |
| num_channels_latents = 16 | |
| if hasattr(self, "vae") and self.vae is not None: | |
| num_channels_latents = self.vae.config.z_dim | |
| return num_channels_latents | |