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Unverified Commit 886575ee authored by Patrick von Platen's avatar Patrick von Platen Committed by GitHub
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Refactor controlnet and add img2img and inpaint (#3386) 

* refactor controlnet and add img2img and inpaint

* First draft to get pipelines to work

* make style

* Fix more

* Fix more

* More tests

* Fix more

* Make inpainting work

* make style and more tests

* Apply suggestions from code review

* up

* make style

* Fix imports

* Fix more

* Fix more

* Improve examples

* add test

* Make sure import is correctly deprecated

* Make sure everything works in compile mode

* make sure authorship is correctly attributed
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tests/pipelines/controlnet/test_controlnet_img2img.py 0 → 100644
View file @ 886575ee
# 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.
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImg2ImgPipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import require_torch_gpu
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
torch.use_deterministic_algorithms(True)
class ControlNetImg2ImgPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionControlNetImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
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"),
cross_attention_dim=32,
)
torch.manual_seed(0)
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
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=[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,
)
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
control_image = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
)
image = floats_tensor(control_image.shape, rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
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,
"control_image": control_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)
class StableDiffusionMultiControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionControlNetImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
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"),
cross_attention_dim=32,
)
torch.manual_seed(0)
controlnet1 = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
torch.manual_seed(0)
controlnet2 = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
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=[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,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
controlnet = MultiControlNetModel([controlnet1, controlnet2])
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
control_image = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
]
image = floats_tensor(control_image[0].shape, rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
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,
"control_image": control_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_save_pretrained_raise_not_implemented_exception(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(tmpdir)
except NotImplementedError:
pass
# override PipelineTesterMixin
@unittest.skip("save pretrained not implemented")
def test_save_load_float16(self):
...
# override PipelineTesterMixin
@unittest.skip("save pretrained not implemented")
def test_save_load_local(self):
...
# override PipelineTesterMixin
@unittest.skip("save pretrained not implemented")
def test_save_load_optional_components(self):
...
@slow
@require_torch_gpu
class ControlNetImg2ImgPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_canny(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", 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 = "evil space-punk bird"
control_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
).resize((512, 512))
image = load_image(
"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
).resize((512, 512))
output = pipe(
prompt,
image,
control_image=control_image,
generator=generator,
output_type="np",
num_inference_steps=50,
strength=0.6,
)
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/img2img.npy"
)
assert np.abs(expected_image - image).max() < 9e-2
tests/pipelines/controlnet/test_controlnet_inpaint.py 0 → 100644
View file @ 886575ee
# 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.
# This model implementation is heavily based on:
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetInpaintPipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import require_torch_gpu
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
torch.use_deterministic_algorithms(True)
class ControlNetInpaintPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionControlNetInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = frozenset([])
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=9,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
torch.manual_seed(0)
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
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=[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,
)
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
control_image = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
)
init_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
init_image = init_image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(init_image)).convert("RGB").resize((64, 64))
mask_image = Image.fromarray(np.uint8(init_image + 4)).convert("RGB").resize((64, 64))
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,
"mask_image": mask_image,
"control_image": control_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)
class MultiControlNetInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionControlNetInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_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=9,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
torch.manual_seed(0)
controlnet1 = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
torch.manual_seed(0)
controlnet2 = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
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=[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,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
controlnet = MultiControlNetModel([controlnet1, controlnet2])
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
control_image = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
generator=generator,
device=torch.device(device),
),
]
init_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
init_image = init_image.cpu().permute(0, 2, 3, 1)[0]
image = Image.fromarray(np.uint8(init_image)).convert("RGB").resize((64, 64))
mask_image = Image.fromarray(np.uint8(init_image + 4)).convert("RGB").resize((64, 64))
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,
"mask_image": mask_image,
"control_image": control_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_save_pretrained_raise_not_implemented_exception(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(tmpdir)
except NotImplementedError:
pass
# override PipelineTesterMixin
@unittest.skip("save pretrained not implemented")
def test_save_load_float16(self):
...
# override PipelineTesterMixin
@unittest.skip("save pretrained not implemented")
def test_save_load_local(self):
...
# override PipelineTesterMixin
@unittest.skip("save pretrained not implemented")
def test_save_load_optional_components(self):
...
@slow
@require_torch_gpu
class ControlNetInpaintPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_canny(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", 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)
image = load_image(
"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
).resize((512, 512))
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
).resize((512, 512))
prompt = "pitch black hole"
control_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=image,
mask_image=mask_image,
control_image=control_image,
generator=generator,
output_type="np",
num_inference_steps=3,
)
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/inpaint.npy"
)
assert np.abs(expected_image - image).max() < 9e-2
tests/pipelines/stable_diffusion/test_stable_diffusion_flax_controlnet.py → tests/pipelines/controlnet/test_flax_controlnet.py
View file @ 886575ee
......@@ -30,7 +30,7 @@ if is_flax_available():
@slow
@require_flax
class FlaxStableDiffusionControlNetPipelineIntegrationTests(unittest.TestCase):
class FlaxControlNetPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
......
tests/pipelines/stable_diffusion/test_stable_diffusion_image_variation.py
View file @ 886575ee
......@@ -46,9 +46,8 @@ class StableDiffusionImageVariationPipelineFastTests(
pipeline_class = StableDiffusionImageVariationPipeline
params = IMAGE_VARIATION_PARAMS
batch_params = IMAGE_VARIATION_BATCH_PARAMS
image_params = frozenset(
[]
) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
image_params = frozenset([])
# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
def get_dummy_components(self):
torch.manual_seed(0)
......
tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py
View file @ 886575ee
......@@ -47,9 +47,8 @@ class StableDiffusionInpaintPipelineFastTests(PipelineLatentTesterMixin, Pipelin
pipeline_class = StableDiffusionInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = frozenset(
[]
) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
image_params = frozenset([])
# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
def get_dummy_components(self):
torch.manual_seed(0)
......
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