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Unverified Commit c1e8bdf1 authored by Dhruv Nair's avatar Dhruv Nair Committed by GitHub
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Move ControlNetXS into Community Folder (#6316) 



* update

* update

* update

* update

* update

* make style

* remove docs

* update

* move to research folder.

* fix-copies

* remove _toctree entry.

---------
Co-authored-by: default avatarSayak Paul <spsayakpaul@gmail.com>
parent 78b87dc2
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docs/source/en/_toctree.yml
View file @ c1e8bdf1
......@@ -266,10 +266,6 @@
title: ControlNet
- local: api/pipelines/controlnet_sdxl
title: ControlNet with Stable Diffusion XL
- local: api/pipelines/controlnetxs
title: ControlNet-XS
- local: api/pipelines/controlnetxs_sdxl
title: ControlNet-XS with Stable Diffusion XL
- local: api/pipelines/dance_diffusion
title: Dance Diffusion
- local: api/pipelines/ddim
......
docs/source/en/api/pipelines/controlnetxs.md → examples/research_projects/controlnetxs/README.md
View file @ c1e8bdf1
<!--Copyright 2023 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.
-->
# ControlNet-XS
ControlNet-XS was introduced in [ControlNet-XS](https://vislearn.github.io/ControlNet-XS/) by Denis Zavadski and Carsten Rother. It is based on the observation that the control model in the [original ControlNet](https://huggingface.co/papers/2302.05543) can be made much smaller and still produce good results.
......@@ -24,16 +12,5 @@ Here's the overview from the [project page](https://vislearn.github.io/ControlNe
This model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
</Tip>
## StableDiffusionControlNetXSPipeline
[[autodoc]] StableDiffusionControlNetXSPipeline
- all
- __call__
## StableDiffusionPipelineOutput
[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
> 🧠 Make sure to check out the Schedulers [guide](https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
\ No newline at end of file
docs/source/en/api/pipelines/controlnetxs_sdxl.md → examples/research_projects/controlnetxs/README_sdxl.md
View file @ c1e8bdf1
<!--Copyright 2023 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.
-->
# ControlNet-XS with Stable Diffusion XL
ControlNet-XS was introduced in [ControlNet-XS](https://vislearn.github.io/ControlNet-XS/) by Denis Zavadski and Carsten Rother. It is based on the observation that the control model in the [original ControlNet](https://huggingface.co/papers/2302.05543) can be made much smaller and still produce good results.
......@@ -24,22 +12,4 @@ Here's the overview from the [project page](https://vislearn.github.io/ControlNe
This model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️
<Tip warning={true}>
🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on how we can improve!
</Tip>
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
</Tip>
## StableDiffusionXLControlNetXSPipeline
[[autodoc]] StableDiffusionXLControlNetXSPipeline
- all
- __call__
## StableDiffusionPipelineOutput
[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
> 🧠 Make sure to check out the Schedulers [guide](https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
\ No newline at end of file
src/diffusers/models/controlnetxs.py → examples/research_projects/controlnetxs/controlnetxs.py
View file @ c1e8bdf1
......@@ -21,13 +21,12 @@ from torch import nn
from torch.nn import functional as F
from torch.nn.modules.normalization import GroupNorm
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging
from .attention_processor import USE_PEFT_BACKEND, AttentionProcessor
from .autoencoders import AutoencoderKL
from .lora import LoRACompatibleConv
from .modeling_utils import ModelMixin
from .unet_2d_blocks import (
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.attention_processor import USE_PEFT_BACKEND, AttentionProcessor
from diffusers.models.autoencoders import AutoencoderKL
from diffusers.models.lora import LoRACompatibleConv
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.unet_2d_blocks import (
CrossAttnDownBlock2D,
CrossAttnUpBlock2D,
DownBlock2D,
......@@ -37,7 +36,8 @@ from .unet_2d_blocks import (
UpBlock2D,
Upsample2D,
)
from .unet_2d_condition import UNet2DConditionModel
from diffusers.models.unet_2d_condition import UNet2DConditionModel
from diffusers.utils import BaseOutput, logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
......
examples/research_projects/controlnetxs/infer_sd_controlnetxs.py 0 → 100644
View file @ c1e8bdf1
# !pip install opencv-python transformers accelerate
import argparse
import cv2
import numpy as np
import torch
from controlnetxs import ControlNetXSModel
from PIL import Image
from pipeline_controlnet_xs import StableDiffusionControlNetXSPipeline
from diffusers.utils import load_image
parser = argparse.ArgumentParser()
parser.add_argument(
"--prompt", type=str, default="aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
)
parser.add_argument("--negative_prompt", type=str, default="low quality, bad quality, sketches")
parser.add_argument("--controlnet_conditioning_scale", type=float, default=0.7)
parser.add_argument(
"--image_path",
type=str,
default="https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png",
)
parser.add_argument("--num_inference_steps", type=int, default=50)
args = parser.parse_args()
prompt = args.prompt
negative_prompt = args.negative_prompt
# download an image
image = load_image(args.image_path)
# initialize the models and pipeline
controlnet_conditioning_scale = args.controlnet_conditioning_scale
controlnet = ControlNetXSModel.from_pretrained("UmerHA/ConrolNetXS-SD2.1-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetXSPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
# get canny image
image = np.array(image)
image = cv2.Canny(image, 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
num_inference_steps = args.num_inference_steps
# generate image
image = pipe(
prompt,
controlnet_conditioning_scale=controlnet_conditioning_scale,
image=canny_image,
num_inference_steps=num_inference_steps,
).images[0]
image.save("cnxs_sd.canny.png")
examples/research_projects/controlnetxs/infer_sdxl_controlnetxs.py 0 → 100644
View file @ c1e8bdf1
# !pip install opencv-python transformers accelerate
import argparse
import cv2
import numpy as np
import torch
from controlnetxs import ControlNetXSModel
from PIL import Image
from pipeline_controlnet_xs import StableDiffusionControlNetXSPipeline
from diffusers.utils import load_image
parser = argparse.ArgumentParser()
parser.add_argument(
"--prompt", type=str, default="aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
)
parser.add_argument("--negative_prompt", type=str, default="low quality, bad quality, sketches")
parser.add_argument("--controlnet_conditioning_scale", type=float, default=0.7)
parser.add_argument(
"--image_path",
type=str,
default="https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png",
)
parser.add_argument("--num_inference_steps", type=int, default=50)
args = parser.parse_args()
prompt = args.prompt
negative_prompt = args.negative_prompt
# download an image
image = load_image(args.image_path)
# initialize the models and pipeline
controlnet_conditioning_scale = args.controlnet_conditioning_scale
controlnet = ControlNetXSModel.from_pretrained("UmerHA/ConrolNetXS-SDXL-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetXSPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
# get canny image
image = np.array(image)
image = cv2.Canny(image, 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
num_inference_steps = args.num_inference_steps
# generate image
image = pipe(
prompt,
controlnet_conditioning_scale=controlnet_conditioning_scale,
image=canny_image,
num_inference_steps=num_inference_steps,
).images[0]
image.save("cnxs_sdxl.canny.png")
src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py → examples/research_projects/controlnetxs/pipeline_controlnet_xs.py
View file @ c1e8bdf1
......@@ -19,74 +19,30 @@ import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from controlnetxs import ControlNetXSModel
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, ControlNetXSModel, UNet2DConditionModel
from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import (
from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
USE_PEFT_BACKEND,
deprecate,
logging,
replace_example_docstring,
scale_lora_layers,
unscale_lora_layers,
)
from ...utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
from ..pipeline_utils import DiffusionPipeline
from ..stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> # !pip install opencv-python transformers accelerate
>>> from diffusers import StableDiffusionControlNetXSPipeline, ControlNetXSModel
>>> from diffusers.utils import load_image
>>> import numpy as np
>>> import torch
>>> import cv2
>>> from PIL import Image
>>> prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
>>> negative_prompt = "low quality, bad quality, sketches"
>>> # download an image
>>> image = load_image(
... "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
... )
>>> # initialize the models and pipeline
>>> controlnet_conditioning_scale = 0.5
>>> controlnet = ControlNetXSModel.from_pretrained(
... "UmerHA/ConrolNetXS-SD2.1-canny", torch_dtype=torch.float16
... )
>>> pipe = StableDiffusionControlNetXSPipeline.from_pretrained(
... "stabilityai/stable-diffusion-2-1", controlnet=controlnet, torch_dtype=torch.float16
... )
>>> pipe.enable_model_cpu_offload()
>>> # get canny image
>>> image = np.array(image)
>>> image = cv2.Canny(image, 100, 200)
>>> image = image[:, :, None]
>>> image = np.concatenate([image, image, image], axis=2)
>>> canny_image = Image.fromarray(image)
>>> # generate image
>>> image = pipe(
... prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image
... ).images[0]
```
"""
class StableDiffusionControlNetXSPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
):
......@@ -669,7 +625,6 @@ class StableDiffusionControlNetXSPipeline(
self.unet.disable_freeu()
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
......
src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs_sd_xl.py → examples/research_projects/controlnetxs/pipeline_controlnet_xs_sd_xl.py
View file @ c1e8bdf1
......@@ -21,76 +21,36 @@ import torch
import torch.nn.functional as F
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers.utils.import_utils import is_invisible_watermark_available
from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, ControlNetXSModel, UNet2DConditionModel
from ...models.attention_processor import (
from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL, ControlNetXSModel, UNet2DConditionModel
from diffusers.models.attention_processor import (
AttnProcessor2_0,
LoRAAttnProcessor2_0,
LoRAXFormersAttnProcessor,
XFormersAttnProcessor,
)
from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import USE_PEFT_BACKEND, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
from ...utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
from ..pipeline_utils import DiffusionPipeline
from ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
USE_PEFT_BACKEND,
logging,
scale_lora_layers,
unscale_lora_layers,
)
from diffusers.utils.import_utils import is_invisible_watermark_available
from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
if is_invisible_watermark_available():
from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> # !pip install opencv-python transformers accelerate
>>> from diffusers import StableDiffusionXLControlNetXSPipeline, ControlNetXSModel, AutoencoderKL
>>> from diffusers.utils import load_image
>>> import numpy as np
>>> import torch
>>> import cv2
>>> from PIL import Image
>>> prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
>>> negative_prompt = "low quality, bad quality, sketches"
>>> # download an image
>>> image = load_image(
... "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
... )
>>> # initialize the models and pipeline
>>> controlnet_conditioning_scale = 0.5 # recommended for good generalization
>>> controlnet = ControlNetXSModel.from_pretrained("UmerHA/ConrolNetXS-SDXL-canny", torch_dtype=torch.float16)
>>> vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
>>> pipe = StableDiffusionXLControlNetXSPipeline.from_pretrained(
... "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16
... )
>>> pipe.enable_model_cpu_offload()
>>> # get canny image
>>> image = np.array(image)
>>> image = cv2.Canny(image, 100, 200)
>>> image = image[:, :, None]
>>> image = np.concatenate([image, image, image], axis=2)
>>> canny_image = Image.fromarray(image)
>>> # generate image
>>> image = pipe(
... prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image
... ).images[0]
```
"""
class StableDiffusionXLControlNetXSPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin
):
......@@ -730,7 +690,6 @@ class StableDiffusionXLControlNetXSPipeline(
self.unet.disable_freeu()
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
......
src/diffusers/__init__.py
View file @ c1e8bdf1
......@@ -80,7 +80,6 @@ else:
"AutoencoderTiny",
"ConsistencyDecoderVAE",
"ControlNetModel",
"ControlNetXSModel",
"Kandinsky3UNet",
"ModelMixin",
"MotionAdapter",
......@@ -256,7 +255,6 @@ else:
"StableDiffusionControlNetImg2ImgPipeline",
"StableDiffusionControlNetInpaintPipeline",
"StableDiffusionControlNetPipeline",
"StableDiffusionControlNetXSPipeline",
"StableDiffusionDepth2ImgPipeline",
"StableDiffusionDiffEditPipeline",
"StableDiffusionGLIGENPipeline",
......@@ -280,7 +278,6 @@ else:
"StableDiffusionXLControlNetImg2ImgPipeline",
"StableDiffusionXLControlNetInpaintPipeline",
"StableDiffusionXLControlNetPipeline",
"StableDiffusionXLControlNetXSPipeline",
"StableDiffusionXLImg2ImgPipeline",
"StableDiffusionXLInpaintPipeline",
"StableDiffusionXLInstructPix2PixPipeline",
......@@ -462,7 +459,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
AutoencoderTiny,
ConsistencyDecoderVAE,
ControlNetModel,
ControlNetXSModel,
Kandinsky3UNet,
ModelMixin,
MotionAdapter,
......@@ -617,7 +613,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
StableDiffusionControlNetImg2ImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionControlNetXSPipeline,
StableDiffusionDepth2ImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionGLIGENPipeline,
......@@ -641,7 +636,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
StableDiffusionXLControlNetImg2ImgPipeline,
StableDiffusionXLControlNetInpaintPipeline,
StableDiffusionXLControlNetPipeline,
StableDiffusionXLControlNetXSPipeline,
StableDiffusionXLImg2ImgPipeline,
StableDiffusionXLInpaintPipeline,
StableDiffusionXLInstructPix2PixPipeline,
......
src/diffusers/models/__init__.py
View file @ c1e8bdf1
......@@ -32,7 +32,6 @@ if is_torch_available():
_import_structure["autoencoders.autoencoder_tiny"] = ["AutoencoderTiny"]
_import_structure["autoencoders.consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
_import_structure["controlnet"] = ["ControlNetModel"]
_import_structure["controlnetxs"] = ["ControlNetXSModel"]
_import_structure["dual_transformer_2d"] = ["DualTransformer2DModel"]
_import_structure["embeddings"] = ["ImageProjection"]
_import_structure["modeling_utils"] = ["ModelMixin"]
......@@ -67,7 +66,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
ConsistencyDecoderVAE,
)
from .controlnet import ControlNetModel
from .controlnetxs import ControlNetXSModel
from .dual_transformer_2d import DualTransformer2DModel
from .embeddings import ImageProjection
from .modeling_utils import ModelMixin
......
src/diffusers/pipelines/__init__.py
View file @ c1e8bdf1
......@@ -128,12 +128,6 @@ else:
"StableDiffusionXLControlNetPipeline",
]
)
_import_structure["controlnet_xs"].extend(
[
"StableDiffusionControlNetXSPipeline",
"StableDiffusionXLControlNetXSPipeline",
]
)
_import_structure["deepfloyd_if"] = [
"IFImg2ImgPipeline",
"IFImg2ImgSuperResolutionPipeline",
......@@ -361,10 +355,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
StableDiffusionXLControlNetInpaintPipeline,
StableDiffusionXLControlNetPipeline,
)
from .controlnet_xs import (
StableDiffusionControlNetXSPipeline,
StableDiffusionXLControlNetXSPipeline,
)
from .deepfloyd_if import (
IFImg2ImgPipeline,
IFImg2ImgSuperResolutionPipeline,
......
src/diffusers/pipelines/controlnet_xs/__init__.py deleted 100644 → 0
View file @ 78b87dc2
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_flax_available,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils import dummy_torch_and_transformers_objects # noqa F403
_dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
else:
_import_structure["pipeline_controlnet_xs"] = ["StableDiffusionControlNetXSPipeline"]
_import_structure["pipeline_controlnet_xs_sd_xl"] = ["StableDiffusionXLControlNetXSPipeline"]
try:
if not (is_transformers_available() and is_flax_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils import dummy_flax_and_transformers_objects # noqa F403
_dummy_objects.update(get_objects_from_module(dummy_flax_and_transformers_objects))
else:
pass # _import_structure["pipeline_flax_controlnet"] = ["FlaxStableDiffusionControlNetPipeline"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import *
else:
from .pipeline_controlnet_xs import StableDiffusionControlNetXSPipeline
from .pipeline_controlnet_xs_sd_xl import StableDiffusionXLControlNetXSPipeline
try:
if not (is_transformers_available() and is_flax_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
pass # from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
module_spec=__spec__,
)
for name, value in _dummy_objects.items():
setattr(sys.modules[__name__], name, value)
src/diffusers/utils/dummy_pt_objects.py
View file @ c1e8bdf1
......@@ -92,21 +92,6 @@ class ControlNetModel(metaclass=DummyObject):
requires_backends(cls, ["torch"])
class ControlNetXSModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
class Kandinsky3UNet(metaclass=DummyObject):
_backends = ["torch"]
......
src/diffusers/utils/dummy_torch_and_transformers_objects.py
View file @ c1e8bdf1
......@@ -782,21 +782,6 @@ class StableDiffusionControlNetPipeline(metaclass=DummyObject):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionControlNetXSPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionDepth2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
......@@ -1142,21 +1127,6 @@ class StableDiffusionXLControlNetPipeline(metaclass=DummyObject):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionXLControlNetXSPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class StableDiffusionXLImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
......
tests/pipelines/controlnetxs/test_controlnetxs.py deleted 100644 → 0
View file @ 78b87dc2
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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.
import gc
import traceback
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetXSModel,
DDIMScheduler,
LCMScheduler,
StableDiffusionControlNetXSPipeline,
UNet2DConditionModel,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_image,
load_numpy,
numpy_cosine_similarity_distance,
require_python39_or_higher,
require_torch_2,
require_torch_gpu,
run_test_in_subprocess,
slow,
torch_device,
)
from diffusers.utils.torch_utils import randn_tensor
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
# Will be run via run_test_in_subprocess
def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
error = None
try:
_ = in_queue.get(timeout=timeout)
controlnet = ControlNetXSModel.from_pretrained("UmerHA/ConrolNetXS-SD2.1-canny")
pipe = StableDiffusionControlNetXSPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", safety_checker=None, controlnet=controlnet
)
pipe.to("cuda")
pipe.set_progress_bar_config(disable=None)
pipe.unet.to(memory_format=torch.channels_last)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
pipe.controlnet.to(memory_format=torch.channels_last)
pipe.controlnet = torch.compile(pipe.controlnet, mode="reduce-overhead", fullgraph=True)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
).resize((512, 512))
output = pipe(prompt, image, num_inference_steps=10, generator=generator, output_type="np")
image = output.images[0]
assert image.shape == (512, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out_full.npy"
)
expected_image = np.resize(expected_image, (512, 512, 3))
assert np.abs(expected_image - image).max() < 1.0
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
class ControlNetXSPipelineFastTests(
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionControlNetXSPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(4, 8),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
norm_num_groups=1,
time_cond_proj_dim=time_cond_proj_dim,
)
torch.manual_seed(0)
controlnet = ControlNetXSModel.from_unet(
unet=unet,
time_embedding_mix=0.95,
learn_embedding=True,
size_ratio=0.5,
conditioning_embedding_out_channels=(16, 32),
num_attention_heads=2,
)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
image = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
"image": image,
}
return inputs
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
def test_controlnet_lcm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(time_cond_proj_dim=256)
sd_pipe = StableDiffusionControlNetXSPipeline(**components)
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[0.52700454, 0.3930534, 0.25509018, 0.7132304, 0.53696585, 0.46568912, 0.7095368, 0.7059624, 0.4744786]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch_gpu
class ControlNetXSPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_canny(self):
controlnet = ControlNetXSModel.from_pretrained("UmerHA/ConrolNetXS-SD2.1-canny")
pipe = StableDiffusionControlNetXSPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
image = output.images[0]
assert image.shape == (768, 512, 3)
original_image = image[-3:, -3:, -1].flatten()
expected_image = np.array([0.1274, 0.1401, 0.147, 0.1185, 0.1555, 0.1492, 0.1565, 0.1474, 0.1701])
max_diff = numpy_cosine_similarity_distance(original_image, expected_image)
assert max_diff < 1e-4
def test_depth(self):
controlnet = ControlNetXSModel.from_pretrained("UmerHA/ConrolNetXS-SD2.1-depth")
pipe = StableDiffusionControlNetXSPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", safety_checker=None, controlnet=controlnet
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "Stormtrooper's lecture"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
image = output.images[0]
assert image.shape == (512, 512, 3)
original_image = image[-3:, -3:, -1].flatten()
expected_image = np.array([0.1098, 0.1025, 0.1211, 0.1129, 0.1165, 0.1262, 0.1185, 0.1261, 0.1703])
max_diff = numpy_cosine_similarity_distance(original_image, expected_image)
assert max_diff < 1e-4
@require_python39_or_higher
@require_torch_2
def test_stable_diffusion_compile(self):
run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=None)
tests/pipelines/controlnetxs/test_controlnetxs_sdxl.py deleted 100644 → 0
View file @ 78b87dc2
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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.
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetXSModel,
EulerDiscreteScheduler,
StableDiffusionXLControlNetXSPipeline,
UNet2DConditionModel,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, slow, torch_device
from diffusers.utils.torch_utils import randn_tensor
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_BATCH_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
SDXLOptionalComponentsTesterMixin,
)
enable_full_determinism()
class StableDiffusionXLControlNetXSPipelineFastTests(
PipelineLatentTesterMixin,
PipelineKarrasSchedulerTesterMixin,
PipelineTesterMixin,
SDXLOptionalComponentsTesterMixin,
unittest.TestCase,
):
pipeline_class = StableDiffusionXLControlNetXSPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=80, # 6 * 8 + 32
cross_attention_dim=64,
)
torch.manual_seed(0)
controlnet = ControlNetXSModel.from_unet(
unet,
time_embedding_mix=0.95,
learn_embedding=True,
size_ratio=0.5,
conditioning_embedding_out_channels=(16, 32),
)
torch.manual_seed(0)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
}
return components
# copied from test_controlnet_sdxl.py
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
controlnet_embedder_scale_factor = 2
image = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "np",
"image": image,
}
return inputs
# copied from test_controlnet_sdxl.py
def test_attention_slicing_forward_pass(self):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
# copied from test_controlnet_sdxl.py
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
# copied from test_controlnet_sdxl.py
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
# copied from test_controlnet_sdxl.py
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
# copied from test_controlnet_sdxl.py
@require_torch_gpu
def test_stable_diffusion_xl_offloads(self):
pipes = []
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe.enable_model_cpu_offload()
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe.enable_sequential_cpu_offload()
pipes.append(sd_pipe)
image_slices = []
for pipe in pipes:
pipe.unet.set_default_attn_processor()
inputs = self.get_dummy_inputs(torch_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
# copied from test_controlnet_sdxl.py
def test_stable_diffusion_xl_multi_prompts(self):
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components).to(torch_device)
# forward with single prompt
inputs = self.get_dummy_inputs(torch_device)
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = inputs["prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = "different prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# manually set a negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same negative_prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = inputs["negative_prompt"]
output = sd_pipe(**inputs)
image_slice_2 = output.images[0, -3:, -3:, -1]
# ensure the results are equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different negative_prompt
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = "different negative prompt"
output = sd_pipe(**inputs)
image_slice_3 = output.images[0, -3:, -3:, -1]
# ensure the results are not equal
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# copied from test_stable_diffusion_xl.py
def test_stable_diffusion_xl_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 2 * [inputs["prompt"]]
inputs["num_images_per_prompt"] = 2
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
inputs = self.get_dummy_inputs(torch_device)
prompt = 2 * [inputs.pop("prompt")]
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = sd_pipe.encode_prompt(prompt)
output = sd_pipe(
**inputs,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
image_slice_2 = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1.1e-4
@slow
@require_torch_gpu
class ControlNetSDXLPipelineXSSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_canny(self):
controlnet = ControlNetXSModel.from_pretrained("UmerHA/ConrolNetXS-SDXL-canny")
pipe = StableDiffusionXLControlNetXSPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet
)
pipe.enable_sequential_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)
images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images
assert images[0].shape == (768, 512, 3)
original_image = images[0, -3:, -3:, -1].flatten()
expected_image = np.array([0.4359, 0.4335, 0.4609, 0.4515, 0.4669, 0.4494, 0.452, 0.4493, 0.4382])
assert np.allclose(original_image, expected_image, atol=1e-04)
def test_depth(self):
controlnet = ControlNetXSModel.from_pretrained("UmerHA/ConrolNetXS-SDXL-depth")
pipe = StableDiffusionXLControlNetXSPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet
)
pipe.enable_sequential_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "Stormtrooper's lecture"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png"
)
images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images
assert images[0].shape == (512, 512, 3)
original_image = images[0, -3:, -3:, -1].flatten()
expected_image = np.array([0.4411, 0.3617, 0.2654, 0.266, 0.3449, 0.3898, 0.3745, 0.353, 0.326])
assert np.allclose(original_image, expected_image, atol=1e-04)
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