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# 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 tempfile | |
import unittest | |
import numpy as np | |
import torch | |
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
from diffusers import ( | |
AutoencoderKL, | |
ControlNetModel, | |
DDIMScheduler, | |
StableDiffusionControlNetPipeline, | |
UNet2DConditionModel, | |
) | |
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel | |
from diffusers.utils import 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_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS | |
from ...test_pipelines_common import PipelineTesterMixin | |
class StableDiffusionControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
pipeline_class = StableDiffusionControlNetPipeline | |
params = TEXT_TO_IMAGE_PARAMS | |
batch_params = TEXT_TO_IMAGE_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=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 | |
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) | |
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 = StableDiffusionControlNetPipeline | |
params = TEXT_TO_IMAGE_PARAMS | |
batch_params = TEXT_TO_IMAGE_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=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 | |
images = [ | |
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), | |
), | |
] | |
inputs = { | |
"prompt": "A painting of a squirrel eating a burger", | |
"generator": generator, | |
"num_inference_steps": 2, | |
"guidance_scale": 6.0, | |
"output_type": "numpy", | |
"image": images, | |
} | |
return inputs | |
def test_attention_slicing_forward_pass(self): | |
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3) | |
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 | |
def test_save_load_float16(self): | |
... | |
# override PipelineTesterMixin | |
def test_save_load_local(self): | |
... | |
# override PipelineTesterMixin | |
def test_save_load_optional_components(self): | |
... | |
class StableDiffusionControlNetPipelineSlowTests(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 = StableDiffusionControlNetPipeline.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 = "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) | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out.npy" | |
) | |
assert np.abs(expected_image - image).max() < 5e-3 | |
def test_depth(self): | |
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-depth") | |
pipe = StableDiffusionControlNetPipeline.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 = "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) | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth_out.npy" | |
) | |
assert np.abs(expected_image - image).max() < 5e-3 | |
def test_hed(self): | |
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-hed") | |
pipe = StableDiffusionControlNetPipeline.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 = "oil painting of handsome old man, masterpiece" | |
image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed.png" | |
) | |
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
image = output.images[0] | |
assert image.shape == (704, 512, 3) | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed_out.npy" | |
) | |
assert np.abs(expected_image - image).max() < 5e-3 | |
def test_mlsd(self): | |
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-mlsd") | |
pipe = StableDiffusionControlNetPipeline.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 = "room" | |
image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd.png" | |
) | |
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
image = output.images[0] | |
assert image.shape == (704, 512, 3) | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd_out.npy" | |
) | |
assert np.abs(expected_image - image).max() < 5e-3 | |
def test_normal(self): | |
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-normal") | |
pipe = StableDiffusionControlNetPipeline.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 = "cute toy" | |
image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal.png" | |
) | |
output = pipe(prompt, 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/cute_toy_normal_out.npy" | |
) | |
assert np.abs(expected_image - image).max() < 5e-3 | |
def test_openpose(self): | |
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose") | |
pipe = StableDiffusionControlNetPipeline.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 = "Chef in the kitchen" | |
image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" | |
) | |
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
image = output.images[0] | |
assert image.shape == (768, 512, 3) | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/chef_pose_out.npy" | |
) | |
assert np.abs(expected_image - image).max() < 5e-3 | |
def test_scribble(self): | |
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble") | |
pipe = StableDiffusionControlNetPipeline.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(5) | |
prompt = "bag" | |
image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble.png" | |
) | |
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) | |
image = output.images[0] | |
assert image.shape == (640, 512, 3) | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble_out.npy" | |
) | |
assert np.abs(expected_image - image).max() < 5e-3 | |
def test_seg(self): | |
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg") | |
pipe = StableDiffusionControlNetPipeline.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(5) | |
prompt = "house" | |
image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png" | |
) | |
output = pipe(prompt, 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/house_seg_out.npy" | |
) | |
assert np.abs(expected_image - image).max() < 5e-3 | |
def test_sequential_cpu_offloading(self): | |
torch.cuda.empty_cache() | |
torch.cuda.reset_max_memory_allocated() | |
torch.cuda.reset_peak_memory_stats() | |
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg") | |
pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet | |
) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
pipe.enable_sequential_cpu_offload() | |
prompt = "house" | |
image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png" | |
) | |
_ = pipe( | |
prompt, | |
image, | |
num_inference_steps=2, | |
output_type="np", | |
) | |
mem_bytes = torch.cuda.max_memory_allocated() | |
# make sure that less than 7 GB is allocated | |
assert mem_bytes < 4 * 10**9 | |
class StableDiffusionMultiControlNetPipelineSlowTests(unittest.TestCase): | |
def tearDown(self): | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def test_pose_and_canny(self): | |
controlnet_canny = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny") | |
controlnet_pose = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose") | |
pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=[controlnet_pose, controlnet_canny] | |
) | |
pipe.enable_model_cpu_offload() | |
pipe.set_progress_bar_config(disable=None) | |
generator = torch.Generator(device="cpu").manual_seed(0) | |
prompt = "bird and Chef" | |
image_canny = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" | |
) | |
image_pose = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" | |
) | |
output = pipe(prompt, [image_pose, image_canny], generator=generator, output_type="np", num_inference_steps=3) | |
image = output.images[0] | |
assert image.shape == (768, 512, 3) | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose_canny_out.npy" | |
) | |
assert np.abs(expected_image - image).max() < 5e-2 | |