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Browse files- main/README.md +2 -0
- main/pipeline_faithdiff_stable_diffusion_xl.py +12 -0
- main/pipeline_flux_kontext_multiple_images.py +13 -0
- main/pipeline_flux_rf_inversion.py +25 -0
- main/pipeline_flux_semantic_guidance.py +13 -0
- main/pipeline_flux_with_cfg.py +25 -0
- main/pipeline_stable_diffusion_3_differential_img2img.py +1 -5
- main/pipeline_stable_diffusion_boxdiff.py +24 -0
- main/pipeline_stable_diffusion_pag.py +24 -0
- main/pipeline_stg_hunyuan_video.py +25 -1
- main/pipeline_stg_mochi.py +25 -5
main/README.md
CHANGED
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@@ -88,6 +88,8 @@ PIXART-α Controlnet pipeline | Implementation of the controlnet model for pixar
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| FaithDiff Stable Diffusion XL Pipeline | Implementation of [(CVPR 2025) FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolutionUnleashing Diffusion Priors for Faithful Image Super-resolution](https://huggingface.co/papers/2411.18824) - FaithDiff is a faithful image super-resolution method that leverages latent diffusion models by actively adapting the diffusion prior and jointly fine-tuning its components (encoder and diffusion model) with an alignment module to ensure high fidelity and structural consistency. | [FaithDiff Stable Diffusion XL Pipeline](#faithdiff-stable-diffusion-xl-pipeline) | [](https://huggingface.co/jychen9811/FaithDiff) | [Junyang Chen, Jinshan Pan, Jiangxin Dong, IMAG Lab, (Adapted by Eliseu Silva)](https://github.com/JyChen9811/FaithDiff) |
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| Stable Diffusion 3 InstructPix2Pix Pipeline | Implementation of Stable Diffusion 3 InstructPix2Pix Pipeline | [Stable Diffusion 3 InstructPix2Pix Pipeline](#stable-diffusion-3-instructpix2pix-pipeline) | [](https://huggingface.co/BleachNick/SD3_UltraEdit_freeform) [](https://huggingface.co/CaptainZZZ/sd3-instructpix2pix) | [Jiayu Zhang](https://github.com/xduzhangjiayu) and [Haozhe Zhao](https://github.com/HaozheZhao)|
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| Flux Kontext multiple images | A modified version of the `FluxKontextPipeline` that supports calling Flux Kontext with multiple reference images.| [Flux Kontext multiple input Pipeline](#flux-kontext-multiple-images) | - | [Net-Mist](https://github.com/Net-Mist) |
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To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
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```py
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| FaithDiff Stable Diffusion XL Pipeline | Implementation of [(CVPR 2025) FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolutionUnleashing Diffusion Priors for Faithful Image Super-resolution](https://huggingface.co/papers/2411.18824) - FaithDiff is a faithful image super-resolution method that leverages latent diffusion models by actively adapting the diffusion prior and jointly fine-tuning its components (encoder and diffusion model) with an alignment module to ensure high fidelity and structural consistency. | [FaithDiff Stable Diffusion XL Pipeline](#faithdiff-stable-diffusion-xl-pipeline) | [](https://huggingface.co/jychen9811/FaithDiff) | [Junyang Chen, Jinshan Pan, Jiangxin Dong, IMAG Lab, (Adapted by Eliseu Silva)](https://github.com/JyChen9811/FaithDiff) |
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| Stable Diffusion 3 InstructPix2Pix Pipeline | Implementation of Stable Diffusion 3 InstructPix2Pix Pipeline | [Stable Diffusion 3 InstructPix2Pix Pipeline](#stable-diffusion-3-instructpix2pix-pipeline) | [](https://huggingface.co/BleachNick/SD3_UltraEdit_freeform) [](https://huggingface.co/CaptainZZZ/sd3-instructpix2pix) | [Jiayu Zhang](https://github.com/xduzhangjiayu) and [Haozhe Zhao](https://github.com/HaozheZhao)|
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| Flux Kontext multiple images | A modified version of the `FluxKontextPipeline` that supports calling Flux Kontext with multiple reference images.| [Flux Kontext multiple input Pipeline](#flux-kontext-multiple-images) | - | [Net-Mist](https://github.com/Net-Mist) |
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To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
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```py
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main/pipeline_faithdiff_stable_diffusion_xl.py
CHANGED
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@@ -1705,6 +1705,12 @@ class FaithDiffStableDiffusionXLPipeline(
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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processing larger images.
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"""
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self.vae.enable_tiling()
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self.unet.denoise_encoder.enable_tiling()
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@@ -1713,6 +1719,12 @@ class FaithDiffStableDiffusionXLPipeline(
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_tiling()
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self.unet.denoise_encoder.disable_tiling()
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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processing larger images.
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"""
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+
depr_message = f"Calling `enable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_tiling()`."
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deprecate(
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"enable_vae_tiling",
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"0.40.0",
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depr_message,
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)
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self.vae.enable_tiling()
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self.unet.denoise_encoder.enable_tiling()
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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+
depr_message = f"Calling `disable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_tiling()`."
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deprecate(
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"disable_vae_tiling",
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"0.40.0",
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depr_message,
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)
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self.vae.disable_tiling()
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self.unet.denoise_encoder.disable_tiling()
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main/pipeline_flux_kontext_multiple_images.py
CHANGED
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@@ -35,6 +35,7 @@ from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
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from diffusers.utils import (
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USE_PEFT_BACKEND,
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is_torch_xla_available,
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logging,
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replace_example_docstring,
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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processing larger images.
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"""
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self.vae.enable_tiling()
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# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.disable_vae_tiling
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_tiling()
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def preprocess_image(self, image: PipelineImageInput, _auto_resize: bool, multiple_of: int) -> torch.Tensor:
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from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
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from diffusers.utils import (
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USE_PEFT_BACKEND,
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deprecate,
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is_torch_xla_available,
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logging,
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replace_example_docstring,
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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processing larger images.
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"""
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+
depr_message = f"Calling `enable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_tiling()`."
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deprecate(
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"enable_vae_tiling",
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"0.40.0",
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depr_message,
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)
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self.vae.enable_tiling()
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# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.disable_vae_tiling
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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depr_message = f"Calling `disable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_tiling()`."
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deprecate(
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"disable_vae_tiling",
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"0.40.0",
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depr_message,
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)
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self.vae.disable_tiling()
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def preprocess_image(self, image: PipelineImageInput, _auto_resize: bool, multiple_of: int) -> torch.Tensor:
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main/pipeline_flux_rf_inversion.py
CHANGED
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@@ -30,6 +30,7 @@ from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
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from diffusers.utils import (
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USE_PEFT_BACKEND,
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is_torch_xla_available,
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logging,
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replace_example_docstring,
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Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
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compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
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"""
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self.vae.enable_slicing()
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def disable_vae_slicing(self):
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Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_slicing()
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def enable_vae_tiling(self):
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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processing larger images.
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"""
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self.vae.enable_tiling()
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def disable_vae_tiling(self):
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_tiling()
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def prepare_latents_inversion(
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from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
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from diffusers.utils import (
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USE_PEFT_BACKEND,
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+
deprecate,
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is_torch_xla_available,
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| 35 |
logging,
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| 36 |
replace_example_docstring,
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| 527 |
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
| 528 |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
| 529 |
"""
|
| 530 |
+
depr_message = f"Calling `enable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_slicing()`."
|
| 531 |
+
deprecate(
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+
"enable_vae_slicing",
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+
"0.40.0",
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+
depr_message,
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+
)
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self.vae.enable_slicing()
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def disable_vae_slicing(self):
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Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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+
depr_message = f"Calling `disable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_slicing()`."
|
| 544 |
+
deprecate(
|
| 545 |
+
"disable_vae_slicing",
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| 546 |
+
"0.40.0",
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+
depr_message,
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+
)
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self.vae.disable_slicing()
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def enable_vae_tiling(self):
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| 554 |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
| 555 |
processing larger images.
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| 556 |
"""
|
| 557 |
+
depr_message = f"Calling `enable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_tiling()`."
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| 558 |
+
deprecate(
|
| 559 |
+
"enable_vae_tiling",
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| 560 |
+
"0.40.0",
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+
depr_message,
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+
)
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self.vae.enable_tiling()
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def disable_vae_tiling(self):
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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| 570 |
+
depr_message = f"Calling `disable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_tiling()`."
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+
deprecate(
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+
"disable_vae_tiling",
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+
"0.40.0",
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+
depr_message,
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+
)
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self.vae.disable_tiling()
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def prepare_latents_inversion(
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main/pipeline_flux_semantic_guidance.py
CHANGED
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@@ -35,6 +35,7 @@ from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
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from diffusers.utils import (
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USE_PEFT_BACKEND,
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is_torch_xla_available,
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logging,
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replace_example_docstring,
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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processing larger images.
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"""
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self.vae.enable_tiling()
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# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.disable_vae_tiling
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_tiling()
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# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.prepare_latents
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from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
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| 36 |
from diffusers.utils import (
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USE_PEFT_BACKEND,
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+
deprecate,
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| 39 |
is_torch_xla_available,
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logging,
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replace_example_docstring,
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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| 704 |
processing larger images.
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| 705 |
"""
|
| 706 |
+
depr_message = f"Calling `enable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_tiling()`."
|
| 707 |
+
deprecate(
|
| 708 |
+
"enable_vae_tiling",
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| 709 |
+
"0.40.0",
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| 710 |
+
depr_message,
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+
)
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self.vae.enable_tiling()
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# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.disable_vae_tiling
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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| 719 |
"""
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| 720 |
+
depr_message = f"Calling `disable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_tiling()`."
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| 721 |
+
deprecate(
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| 722 |
+
"disable_vae_tiling",
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| 723 |
+
"0.40.0",
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depr_message,
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+
)
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self.vae.disable_tiling()
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| 728 |
# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.prepare_latents
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main/pipeline_flux_with_cfg.py
CHANGED
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@@ -28,6 +28,7 @@ from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
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from diffusers.utils import (
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USE_PEFT_BACKEND,
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is_torch_xla_available,
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logging,
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replace_example_docstring,
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Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
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| 504 |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
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"""
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self.vae.enable_slicing()
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def disable_vae_slicing(self):
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Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_slicing()
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def enable_vae_tiling(self):
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| 518 |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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| 519 |
processing larger images.
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"""
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self.vae.enable_tiling()
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def disable_vae_tiling(self):
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@@ -525,6 +544,12 @@ class FluxCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixi
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| 525 |
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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| 526 |
computing decoding in one step.
|
| 527 |
"""
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| 528 |
self.vae.disable_tiling()
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| 529 |
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| 530 |
def prepare_latents(
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| 28 |
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
|
| 29 |
from diffusers.utils import (
|
| 30 |
USE_PEFT_BACKEND,
|
| 31 |
+
deprecate,
|
| 32 |
is_torch_xla_available,
|
| 33 |
logging,
|
| 34 |
replace_example_docstring,
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|
| 504 |
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
| 505 |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
| 506 |
"""
|
| 507 |
+
depr_message = f"Calling `enable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_slicing()`."
|
| 508 |
+
deprecate(
|
| 509 |
+
"enable_vae_slicing",
|
| 510 |
+
"0.40.0",
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| 511 |
+
depr_message,
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| 512 |
+
)
|
| 513 |
self.vae.enable_slicing()
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| 514 |
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| 515 |
def disable_vae_slicing(self):
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| 517 |
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
| 518 |
computing decoding in one step.
|
| 519 |
"""
|
| 520 |
+
depr_message = f"Calling `disable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_slicing()`."
|
| 521 |
+
deprecate(
|
| 522 |
+
"disable_vae_slicing",
|
| 523 |
+
"0.40.0",
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| 524 |
+
depr_message,
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| 525 |
+
)
|
| 526 |
self.vae.disable_slicing()
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| 527 |
|
| 528 |
def enable_vae_tiling(self):
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| 531 |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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| 532 |
processing larger images.
|
| 533 |
"""
|
| 534 |
+
depr_message = f"Calling `enable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_tiling()`."
|
| 535 |
+
deprecate(
|
| 536 |
+
"enable_vae_tiling",
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| 537 |
+
"0.40.0",
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| 538 |
+
depr_message,
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| 539 |
+
)
|
| 540 |
self.vae.enable_tiling()
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| 541 |
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| 542 |
def disable_vae_tiling(self):
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|
| 544 |
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
| 545 |
computing decoding in one step.
|
| 546 |
"""
|
| 547 |
+
depr_message = f"Calling `disable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_tiling()`."
|
| 548 |
+
deprecate(
|
| 549 |
+
"disable_vae_tiling",
|
| 550 |
+
"0.40.0",
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| 551 |
+
depr_message,
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| 552 |
+
)
|
| 553 |
self.vae.disable_tiling()
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| 554 |
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| 555 |
def prepare_latents(
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main/pipeline_stable_diffusion_3_differential_img2img.py
CHANGED
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@@ -29,11 +29,7 @@ from diffusers.models.transformers import SD3Transformer2DModel
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| 29 |
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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| 30 |
from diffusers.pipelines.stable_diffusion_3.pipeline_output import StableDiffusion3PipelineOutput
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| 31 |
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
|
| 32 |
-
from diffusers.utils import
|
| 33 |
-
is_torch_xla_available,
|
| 34 |
-
logging,
|
| 35 |
-
replace_example_docstring,
|
| 36 |
-
)
|
| 37 |
from diffusers.utils.torch_utils import randn_tensor
|
| 38 |
|
| 39 |
|
|
|
|
| 29 |
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
|
| 30 |
from diffusers.pipelines.stable_diffusion_3.pipeline_output import StableDiffusion3PipelineOutput
|
| 31 |
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
|
| 32 |
+
from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
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|
| 33 |
from diffusers.utils.torch_utils import randn_tensor
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| 34 |
|
| 35 |
|
main/pipeline_stable_diffusion_boxdiff.py
CHANGED
|
@@ -504,6 +504,12 @@ class StableDiffusionBoxDiffPipeline(
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|
| 504 |
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
| 505 |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
| 506 |
"""
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| 507 |
self.vae.enable_slicing()
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| 508 |
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| 509 |
def disable_vae_slicing(self):
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@@ -511,6 +517,12 @@ class StableDiffusionBoxDiffPipeline(
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|
| 511 |
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
| 512 |
computing decoding in one step.
|
| 513 |
"""
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|
| 514 |
self.vae.disable_slicing()
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| 515 |
|
| 516 |
def enable_vae_tiling(self):
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@@ -519,6 +531,12 @@ class StableDiffusionBoxDiffPipeline(
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|
| 519 |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
| 520 |
processing larger images.
|
| 521 |
"""
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|
| 522 |
self.vae.enable_tiling()
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| 523 |
|
| 524 |
def disable_vae_tiling(self):
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@@ -526,6 +544,12 @@ class StableDiffusionBoxDiffPipeline(
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|
| 526 |
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
| 527 |
computing decoding in one step.
|
| 528 |
"""
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|
| 529 |
self.vae.disable_tiling()
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| 530 |
|
| 531 |
def _encode_prompt(
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|
| 504 |
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
| 505 |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
| 506 |
"""
|
| 507 |
+
depr_message = f"Calling `enable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_slicing()`."
|
| 508 |
+
deprecate(
|
| 509 |
+
"enable_vae_slicing",
|
| 510 |
+
"0.40.0",
|
| 511 |
+
depr_message,
|
| 512 |
+
)
|
| 513 |
self.vae.enable_slicing()
|
| 514 |
|
| 515 |
def disable_vae_slicing(self):
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|
|
| 517 |
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
| 518 |
computing decoding in one step.
|
| 519 |
"""
|
| 520 |
+
depr_message = f"Calling `disable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_slicing()`."
|
| 521 |
+
deprecate(
|
| 522 |
+
"disable_vae_slicing",
|
| 523 |
+
"0.40.0",
|
| 524 |
+
depr_message,
|
| 525 |
+
)
|
| 526 |
self.vae.disable_slicing()
|
| 527 |
|
| 528 |
def enable_vae_tiling(self):
|
|
|
|
| 531 |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
| 532 |
processing larger images.
|
| 533 |
"""
|
| 534 |
+
depr_message = f"Calling `enable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_tiling()`."
|
| 535 |
+
deprecate(
|
| 536 |
+
"enable_vae_tiling",
|
| 537 |
+
"0.40.0",
|
| 538 |
+
depr_message,
|
| 539 |
+
)
|
| 540 |
self.vae.enable_tiling()
|
| 541 |
|
| 542 |
def disable_vae_tiling(self):
|
|
|
|
| 544 |
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
| 545 |
computing decoding in one step.
|
| 546 |
"""
|
| 547 |
+
depr_message = f"Calling `disable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_tiling()`."
|
| 548 |
+
deprecate(
|
| 549 |
+
"disable_vae_tiling",
|
| 550 |
+
"0.40.0",
|
| 551 |
+
depr_message,
|
| 552 |
+
)
|
| 553 |
self.vae.disable_tiling()
|
| 554 |
|
| 555 |
def _encode_prompt(
|
main/pipeline_stable_diffusion_pag.py
CHANGED
|
@@ -471,6 +471,12 @@ class StableDiffusionPAGPipeline(
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|
| 471 |
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
| 472 |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
| 473 |
"""
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|
| 474 |
self.vae.enable_slicing()
|
| 475 |
|
| 476 |
def disable_vae_slicing(self):
|
|
@@ -478,6 +484,12 @@ class StableDiffusionPAGPipeline(
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|
| 478 |
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
| 479 |
computing decoding in one step.
|
| 480 |
"""
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|
| 481 |
self.vae.disable_slicing()
|
| 482 |
|
| 483 |
def enable_vae_tiling(self):
|
|
@@ -486,6 +498,12 @@ class StableDiffusionPAGPipeline(
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|
| 486 |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
| 487 |
processing larger images.
|
| 488 |
"""
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|
| 489 |
self.vae.enable_tiling()
|
| 490 |
|
| 491 |
def disable_vae_tiling(self):
|
|
@@ -493,6 +511,12 @@ class StableDiffusionPAGPipeline(
|
|
| 493 |
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
| 494 |
computing decoding in one step.
|
| 495 |
"""
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|
| 496 |
self.vae.disable_tiling()
|
| 497 |
|
| 498 |
def _encode_prompt(
|
|
|
|
| 471 |
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
| 472 |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
| 473 |
"""
|
| 474 |
+
depr_message = f"Calling `enable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_slicing()`."
|
| 475 |
+
deprecate(
|
| 476 |
+
"enable_vae_slicing",
|
| 477 |
+
"0.40.0",
|
| 478 |
+
depr_message,
|
| 479 |
+
)
|
| 480 |
self.vae.enable_slicing()
|
| 481 |
|
| 482 |
def disable_vae_slicing(self):
|
|
|
|
| 484 |
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
| 485 |
computing decoding in one step.
|
| 486 |
"""
|
| 487 |
+
depr_message = f"Calling `disable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_slicing()`."
|
| 488 |
+
deprecate(
|
| 489 |
+
"disable_vae_slicing",
|
| 490 |
+
"0.40.0",
|
| 491 |
+
depr_message,
|
| 492 |
+
)
|
| 493 |
self.vae.disable_slicing()
|
| 494 |
|
| 495 |
def enable_vae_tiling(self):
|
|
|
|
| 498 |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
| 499 |
processing larger images.
|
| 500 |
"""
|
| 501 |
+
depr_message = f"Calling `enable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_tiling()`."
|
| 502 |
+
deprecate(
|
| 503 |
+
"enable_vae_tiling",
|
| 504 |
+
"0.40.0",
|
| 505 |
+
depr_message,
|
| 506 |
+
)
|
| 507 |
self.vae.enable_tiling()
|
| 508 |
|
| 509 |
def disable_vae_tiling(self):
|
|
|
|
| 511 |
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
| 512 |
computing decoding in one step.
|
| 513 |
"""
|
| 514 |
+
depr_message = f"Calling `disable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_tiling()`."
|
| 515 |
+
deprecate(
|
| 516 |
+
"disable_vae_tiling",
|
| 517 |
+
"0.40.0",
|
| 518 |
+
depr_message,
|
| 519 |
+
)
|
| 520 |
self.vae.disable_tiling()
|
| 521 |
|
| 522 |
def _encode_prompt(
|
main/pipeline_stg_hunyuan_video.py
CHANGED
|
@@ -26,7 +26,7 @@ from diffusers.models import AutoencoderKLHunyuanVideo, HunyuanVideoTransformer3
|
|
| 26 |
from diffusers.pipelines.hunyuan_video.pipeline_output import HunyuanVideoPipelineOutput
|
| 27 |
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
|
| 28 |
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
|
| 29 |
-
from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
|
| 30 |
from diffusers.utils.torch_utils import randn_tensor
|
| 31 |
from diffusers.video_processor import VideoProcessor
|
| 32 |
|
|
@@ -481,6 +481,12 @@ class HunyuanVideoSTGPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin):
|
|
| 481 |
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
| 482 |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
| 483 |
"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 484 |
self.vae.enable_slicing()
|
| 485 |
|
| 486 |
def disable_vae_slicing(self):
|
|
@@ -488,6 +494,12 @@ class HunyuanVideoSTGPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin):
|
|
| 488 |
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
| 489 |
computing decoding in one step.
|
| 490 |
"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 491 |
self.vae.disable_slicing()
|
| 492 |
|
| 493 |
def enable_vae_tiling(self):
|
|
@@ -496,6 +508,12 @@ class HunyuanVideoSTGPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin):
|
|
| 496 |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
| 497 |
processing larger images.
|
| 498 |
"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 499 |
self.vae.enable_tiling()
|
| 500 |
|
| 501 |
def disable_vae_tiling(self):
|
|
@@ -503,6 +521,12 @@ class HunyuanVideoSTGPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin):
|
|
| 503 |
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
| 504 |
computing decoding in one step.
|
| 505 |
"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 506 |
self.vae.disable_tiling()
|
| 507 |
|
| 508 |
@property
|
|
|
|
| 26 |
from diffusers.pipelines.hunyuan_video.pipeline_output import HunyuanVideoPipelineOutput
|
| 27 |
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
|
| 28 |
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
|
| 29 |
+
from diffusers.utils import deprecate, is_torch_xla_available, logging, replace_example_docstring
|
| 30 |
from diffusers.utils.torch_utils import randn_tensor
|
| 31 |
from diffusers.video_processor import VideoProcessor
|
| 32 |
|
|
|
|
| 481 |
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
| 482 |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
| 483 |
"""
|
| 484 |
+
depr_message = f"Calling `enable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_slicing()`."
|
| 485 |
+
deprecate(
|
| 486 |
+
"enable_vae_slicing",
|
| 487 |
+
"0.40.0",
|
| 488 |
+
depr_message,
|
| 489 |
+
)
|
| 490 |
self.vae.enable_slicing()
|
| 491 |
|
| 492 |
def disable_vae_slicing(self):
|
|
|
|
| 494 |
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
| 495 |
computing decoding in one step.
|
| 496 |
"""
|
| 497 |
+
depr_message = f"Calling `disable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_slicing()`."
|
| 498 |
+
deprecate(
|
| 499 |
+
"disable_vae_slicing",
|
| 500 |
+
"0.40.0",
|
| 501 |
+
depr_message,
|
| 502 |
+
)
|
| 503 |
self.vae.disable_slicing()
|
| 504 |
|
| 505 |
def enable_vae_tiling(self):
|
|
|
|
| 508 |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
| 509 |
processing larger images.
|
| 510 |
"""
|
| 511 |
+
depr_message = f"Calling `enable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_tiling()`."
|
| 512 |
+
deprecate(
|
| 513 |
+
"enable_vae_tiling",
|
| 514 |
+
"0.40.0",
|
| 515 |
+
depr_message,
|
| 516 |
+
)
|
| 517 |
self.vae.enable_tiling()
|
| 518 |
|
| 519 |
def disable_vae_tiling(self):
|
|
|
|
| 521 |
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
| 522 |
computing decoding in one step.
|
| 523 |
"""
|
| 524 |
+
depr_message = f"Calling `disable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_tiling()`."
|
| 525 |
+
deprecate(
|
| 526 |
+
"disable_vae_tiling",
|
| 527 |
+
"0.40.0",
|
| 528 |
+
depr_message,
|
| 529 |
+
)
|
| 530 |
self.vae.disable_tiling()
|
| 531 |
|
| 532 |
@property
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main/pipeline_stg_mochi.py
CHANGED
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@@ -26,11 +26,7 @@ from diffusers.models import AutoencoderKLMochi, MochiTransformer3DModel
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from diffusers.pipelines.mochi.pipeline_output import MochiPipelineOutput
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
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from diffusers.utils import
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is_torch_xla_available,
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logging,
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replace_example_docstring,
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)
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.video_processor import VideoProcessor
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@@ -458,6 +454,12 @@ class MochiSTGPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin):
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Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
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compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
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"""
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self.vae.enable_slicing()
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def disable_vae_slicing(self):
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@@ -465,6 +467,12 @@ class MochiSTGPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin):
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Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_slicing()
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def enable_vae_tiling(self):
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@@ -473,6 +481,12 @@ class MochiSTGPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin):
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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processing larger images.
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"""
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self.vae.enable_tiling()
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def disable_vae_tiling(self):
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@@ -480,6 +494,12 @@ class MochiSTGPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin):
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_tiling()
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def prepare_latents(
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from diffusers.pipelines.mochi.pipeline_output import MochiPipelineOutput
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
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+
from diffusers.utils import deprecate, is_torch_xla_available, logging, replace_example_docstring
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.video_processor import VideoProcessor
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Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
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compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
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"""
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+
depr_message = f"Calling `enable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_slicing()`."
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deprecate(
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"enable_vae_slicing",
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"0.40.0",
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depr_message,
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)
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self.vae.enable_slicing()
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def disable_vae_slicing(self):
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Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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+
depr_message = f"Calling `disable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_slicing()`."
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deprecate(
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"disable_vae_slicing",
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"0.40.0",
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depr_message,
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)
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self.vae.disable_slicing()
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def enable_vae_tiling(self):
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compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
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processing larger images.
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"""
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+
depr_message = f"Calling `enable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_tiling()`."
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deprecate(
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"enable_vae_tiling",
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"0.40.0",
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depr_message,
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)
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self.vae.enable_tiling()
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def disable_vae_tiling(self):
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Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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+
depr_message = f"Calling `disable_vae_tiling()` on a `{self.__class__.__name__}` is deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_tiling()`."
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deprecate(
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"disable_vae_tiling",
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"0.40.0",
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depr_message,
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)
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self.vae.disable_tiling()
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def prepare_latents(
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