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yiyixuxuyiyixuxupatrickvonplaten
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add StableDiffusionXLKDiffusionPipeline (huggingface#6447)
 --------- Co-authored-by: yiyixuxu <yixu310@gmail,com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
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‎docs/source/en/_toctree.yml‎

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title: Latent upscaler
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- local: api/pipelines/stable_diffusion/upscale
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title: Super-resolution
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- local: api/pipelines/stable_diffusion/k_diffusion
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title: K-Diffusion
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- local: api/pipelines/stable_diffusion/ldm3d_diffusion
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title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler
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- local: api/pipelines/stable_diffusion/adapter

‎docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md‎

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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# K-Diffusion
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[k-diffusion](https://github.com/crowsonkb/k-diffusion) is a popular library created by [Katherine Crowson](https://github.com/crowsonkb/). We provide `StableDiffusionKDiffusionPipeline` and `StableDiffusionXLKDiffusionPipeline` that allow you to run Stable DIffusion with samplers from k-diffusion.
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Note that most the samplers from k-diffusion are implemented in Diffusers and we recommend using existing schedulers. You can find a mapping between k-diffusion samplers and schedulers in Diffusers [here](https://huggingface.co/docs/diffusers/api/schedulers/overview)
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## StableDiffusionKDiffusionPipeline
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[[autodoc]] StableDiffusionKDiffusionPipeline
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## StableDiffusionXLKDiffusionPipeline
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[[autodoc]] StableDiffusionXLKDiffusionPipeline

‎src/diffusers/__init__.py‎

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]
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else:
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_import_structure["pipelines"].extend(["StableDiffusionKDiffusionPipeline"])
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_import_structure["pipelines"].extend(["StableDiffusionKDiffusionPipeline", "StableDiffusionXLKDiffusionPipeline"])
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try:
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if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
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except OptionalDependencyNotAvailable:
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from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
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else:
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from .pipelines import StableDiffusionKDiffusionPipeline
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from .pipelines import StableDiffusionKDiffusionPipeline, StableDiffusionXLKDiffusionPipeline
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try:
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if not (is_torch_available() and is_transformers_available() and is_onnx_available()):

‎src/diffusers/pipelines/__init__.py‎

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_dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_and_k_diffusion_objects))
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else:
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_import_structure["stable_diffusion_k_diffusion"] = ["StableDiffusionKDiffusionPipeline"]
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_import_structure["stable_diffusion_k_diffusion"] = [
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"StableDiffusionKDiffusionPipeline",
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"StableDiffusionXLKDiffusionPipeline",
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]
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try:
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if not is_flax_available():
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raise OptionalDependencyNotAvailable()
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except OptionalDependencyNotAvailable:
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from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *
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else:
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from .stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
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from .stable_diffusion_k_diffusion import (
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StableDiffusionKDiffusionPipeline,
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StableDiffusionXLKDiffusionPipeline,
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)
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try:
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if not is_flax_available():

‎src/diffusers/pipelines/stable_diffusion_k_diffusion/__init__.py‎

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_dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_and_k_diffusion_objects))
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else:
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_import_structure["pipeline_stable_diffusion_k_diffusion"] = ["StableDiffusionKDiffusionPipeline"]
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_import_structure["pipeline_stable_diffusion_xl_k_diffusion"] = ["StableDiffusionXLKDiffusionPipeline"]
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if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
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try:
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from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *
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else:
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from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
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from .pipeline_stable_diffusion_xl_k_diffusion import StableDiffusionXLKDiffusionPipeline
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else:
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import sys

‎src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py‎

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def set_scheduler(self, scheduler_type: str):
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library = importlib.import_module("k_diffusion")
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sampling = getattr(library, "sampling")
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self.sampler = getattr(sampling, scheduler_type)
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try:
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self.sampler = getattr(sampling, scheduler_type)
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except Exception:
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valid_samplers = []
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for s in dir(sampling):
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if "sample_" in s:
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valid_samplers.append(s)
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raise ValueError(f"Invalid scheduler type {scheduler_type}. Please choose one of {valid_samplers}.")
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
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def _encode_prompt(

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