[K Diffusion] Add k diffusion sampler natively (huggingface#1603)

* uP

* uP

examples/community/README.md

@@ -686,7 +686,7 @@ pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom
 pipe = pipe.to("cuda")
 
 prompt = "an astronaut riding a horse on mars"
-pipe.set_sampler("sample_heun")
+pipe.set_scheduler("sample_heun")
 generator = torch.Generator(device="cuda").manual_seed(seed)
 image = pipe(prompt, generator=generator, num_inference_steps=20).images[0]
 
@@ -721,7 +721,7 @@ pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom
 pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
 pipe = pipe.to("cuda")
 
-pipe.set_sampler("sample_euler")
+pipe.set_scheduler("sample_euler")
 generator = torch.Generator(device="cuda").manual_seed(seed)
 image = pipe(prompt, generator=generator, num_inference_steps=50).images[0]
 ```

examples/community/sd_text2img_k_diffusion.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import importlib
+import warnings
 from typing import Callable, List, Optional, Union
 
 import torch
@@ -111,6 +112,10 @@ def __init__(
             self.k_diffusion_model = CompVisDenoiser(model)
 
     def set_sampler(self, scheduler_type: str):
+        warnings.warn("The `set_sampler` method is deprecated, please use `set_scheduler` instead.")
+        return self.set_scheduler(scheduler_type)
+
+    def set_scheduler(self, scheduler_type: str):
         library = importlib.import_module("k_diffusion")
         sampling = getattr(library, "sampling")
         self.sampler = getattr(sampling, scheduler_type)

hi

@@ -0,0 +1 @@
+

setup.py

@@ -91,6 +91,7 @@
     "isort>=5.5.4",
     "jax>=0.2.8,!=0.3.2",
     "jaxlib>=0.1.65",
+    "k-diffusion",
     "librosa",
     "modelcards>=0.1.4",
     "numpy",
@@ -182,6 +183,7 @@ def run(self):
 extras["training"] = deps_list("accelerate", "datasets", "tensorboard", "modelcards")
 extras["test"] = deps_list(
     "datasets",
+    "k-diffusion",
     "librosa",
     "parameterized",
     "pytest",

src/diffusers/__init__.py

@@ -5,6 +5,7 @@
 from .utils import (
     is_flax_available,
     is_inflect_available,
+    is_k_diffusion_available,
     is_onnx_available,
     is_scipy_available,
     is_torch_available,
@@ -90,6 +91,11 @@
 else:
     from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
 
+if is_torch_available() and is_transformers_available() and is_k_diffusion_available():
+    from .pipelines import StableDiffusionKDiffusionPipeline
+else:
+    from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
+
 if is_torch_available() and is_transformers_available() and is_onnx_available():
     from .pipelines import (
         OnnxStableDiffusionImg2ImgPipeline,

src/diffusers/dependency_versions_table.py

@@ -15,6 +15,7 @@
     "isort": "isort>=5.5.4",
     "jax": "jax>=0.2.8,!=0.3.2",
     "jaxlib": "jaxlib>=0.1.65",
+    "k-diffusion": "k-diffusion",
     "librosa": "librosa",
     "modelcards": "modelcards>=0.1.4",
     "numpy": "numpy",

src/diffusers/pipelines/__init__.py

@@ -1,5 +1,6 @@
 from ..utils import (
     is_flax_available,
+    is_k_diffusion_available,
     is_librosa_available,
     is_onnx_available,
     is_torch_available,
@@ -56,5 +57,8 @@
         StableDiffusionOnnxPipeline,
     )
 
+if is_torch_available() and is_transformers_available() and is_k_diffusion_available():
+    from .stable_diffusion import StableDiffusionKDiffusionPipeline
+
 if is_transformers_available() and is_flax_available():
     from .stable_diffusion import FlaxStableDiffusionPipeline

src/diffusers/pipelines/stable_diffusion/__init__.py

@@ -9,6 +9,7 @@
 from ...utils import (
     BaseOutput,
     is_flax_available,
+    is_k_diffusion_available,
     is_onnx_available,
     is_torch_available,
     is_transformers_available,
@@ -48,6 +49,9 @@ class StableDiffusionPipelineOutput(BaseOutput):
 else:
     from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
 
+if is_transformers_available() and is_torch_available() and is_k_diffusion_available():
+    from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
+
 if is_transformers_available() and is_onnx_available():
     from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
     from .pipeline_onnx_stable_diffusion_img2img import OnnxStableDiffusionImg2ImgPipeline