mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-27 17:22:53 +03:00
7aa6af1138
* update
* update
* update
* update
* update
* merge main
* Revert "merge main"
This reverts commit 65efbcead5.
377 lines
14 KiB
Python
377 lines
14 KiB
Python
# coding=utf-8
|
|
# Copyright 2025 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 tempfile
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
from diffusers import (
|
|
DDIMScheduler,
|
|
DPMSolverMultistepScheduler,
|
|
EulerAncestralDiscreteScheduler,
|
|
EulerDiscreteScheduler,
|
|
LMSDiscreteScheduler,
|
|
OnnxStableDiffusionPipeline,
|
|
PNDMScheduler,
|
|
)
|
|
|
|
from ...testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
|
|
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
|
|
|
|
|
|
if is_onnx_available():
|
|
import onnxruntime as ort
|
|
|
|
|
|
class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
|
|
hub_checkpoint = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
|
|
|
|
def get_dummy_inputs(self, seed=0):
|
|
generator = np.random.RandomState(seed)
|
|
inputs = {
|
|
"prompt": "A painting of a squirrel eating a burger",
|
|
"generator": generator,
|
|
"num_inference_steps": 2,
|
|
"guidance_scale": 7.5,
|
|
"output_type": "np",
|
|
}
|
|
return inputs
|
|
|
|
def test_pipeline_default_ddim(self):
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
expected_slice = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_pipeline_pndm(self):
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
|
|
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
expected_slice = np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_pipeline_lms(self):
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
|
|
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_pipeline_euler(self):
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
|
|
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_pipeline_euler_ancestral(self):
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
|
|
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
expected_slice = np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_pipeline_dpm_multistep(self):
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
|
|
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 128, 128, 3)
|
|
expected_slice = np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_prompt_embeds(self):
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
inputs["prompt"] = 3 * [inputs["prompt"]]
|
|
|
|
# forward
|
|
output = pipe(**inputs)
|
|
image_slice_1 = output.images[0, -3:, -3:, -1]
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
prompt = 3 * [inputs.pop("prompt")]
|
|
|
|
text_inputs = pipe.tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=pipe.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="np",
|
|
)
|
|
text_inputs = text_inputs["input_ids"]
|
|
|
|
prompt_embeds = pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0]
|
|
|
|
inputs["prompt_embeds"] = prompt_embeds
|
|
|
|
# forward
|
|
output = pipe(**inputs)
|
|
image_slice_2 = output.images[0, -3:, -3:, -1]
|
|
|
|
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
|
|
|
|
def test_stable_diffusion_negative_prompt_embeds(self):
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider="CPUExecutionProvider")
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
negative_prompt = 3 * ["this is a negative prompt"]
|
|
inputs["negative_prompt"] = negative_prompt
|
|
inputs["prompt"] = 3 * [inputs["prompt"]]
|
|
|
|
# forward
|
|
output = pipe(**inputs)
|
|
image_slice_1 = output.images[0, -3:, -3:, -1]
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
prompt = 3 * [inputs.pop("prompt")]
|
|
|
|
embeds = []
|
|
for p in [prompt, negative_prompt]:
|
|
text_inputs = pipe.tokenizer(
|
|
p,
|
|
padding="max_length",
|
|
max_length=pipe.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="np",
|
|
)
|
|
text_inputs = text_inputs["input_ids"]
|
|
|
|
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0])
|
|
|
|
inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
|
|
|
|
# forward
|
|
output = pipe(**inputs)
|
|
image_slice_2 = output.images[0, -3:, -3:, -1]
|
|
|
|
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
|
|
|
|
|
|
@nightly
|
|
@require_onnxruntime
|
|
@require_torch_gpu
|
|
class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
|
|
@property
|
|
def gpu_provider(self):
|
|
return (
|
|
"CUDAExecutionProvider",
|
|
{
|
|
"gpu_mem_limit": "15000000000", # 15GB
|
|
"arena_extend_strategy": "kSameAsRequested",
|
|
},
|
|
)
|
|
|
|
@property
|
|
def gpu_options(self):
|
|
options = ort.SessionOptions()
|
|
options.enable_mem_pattern = False
|
|
return options
|
|
|
|
def test_inference_default_pndm(self):
|
|
# using the PNDM scheduler by default
|
|
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4",
|
|
revision="onnx",
|
|
safety_checker=None,
|
|
feature_extractor=None,
|
|
provider=self.gpu_provider,
|
|
sess_options=self.gpu_options,
|
|
)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "A painting of a squirrel eating a burger"
|
|
np.random.seed(0)
|
|
output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=10, output_type="np")
|
|
image = output.images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
def test_inference_ddim(self):
|
|
ddim_scheduler = DDIMScheduler.from_pretrained(
|
|
"stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
|
|
)
|
|
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
|
|
"stable-diffusion-v1-5/stable-diffusion-v1-5",
|
|
revision="onnx",
|
|
scheduler=ddim_scheduler,
|
|
safety_checker=None,
|
|
feature_extractor=None,
|
|
provider=self.gpu_provider,
|
|
sess_options=self.gpu_options,
|
|
)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "open neural network exchange"
|
|
generator = np.random.RandomState(0)
|
|
output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np")
|
|
image = output.images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
def test_inference_k_lms(self):
|
|
lms_scheduler = LMSDiscreteScheduler.from_pretrained(
|
|
"stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler", revision="onnx"
|
|
)
|
|
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
|
|
"stable-diffusion-v1-5/stable-diffusion-v1-5",
|
|
revision="onnx",
|
|
scheduler=lms_scheduler,
|
|
safety_checker=None,
|
|
feature_extractor=None,
|
|
provider=self.gpu_provider,
|
|
sess_options=self.gpu_options,
|
|
)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "open neural network exchange"
|
|
generator = np.random.RandomState(0)
|
|
output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type="np")
|
|
image = output.images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
def test_intermediate_state(self):
|
|
number_of_steps = 0
|
|
|
|
def test_callback_fn(step: int, timestep: int, latents: np.ndarray) -> None:
|
|
test_callback_fn.has_been_called = True
|
|
nonlocal number_of_steps
|
|
number_of_steps += 1
|
|
if step == 0:
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array(
|
|
[-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]
|
|
)
|
|
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
|
|
elif step == 5:
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array(
|
|
[-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]
|
|
)
|
|
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
test_callback_fn.has_been_called = False
|
|
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(
|
|
"stable-diffusion-v1-5/stable-diffusion-v1-5",
|
|
revision="onnx",
|
|
safety_checker=None,
|
|
feature_extractor=None,
|
|
provider=self.gpu_provider,
|
|
sess_options=self.gpu_options,
|
|
)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "Andromeda galaxy in a bottle"
|
|
|
|
generator = np.random.RandomState(0)
|
|
pipe(
|
|
prompt=prompt,
|
|
num_inference_steps=5,
|
|
guidance_scale=7.5,
|
|
generator=generator,
|
|
callback=test_callback_fn,
|
|
callback_steps=1,
|
|
)
|
|
assert test_callback_fn.has_been_called
|
|
assert number_of_steps == 6
|
|
|
|
def test_stable_diffusion_no_safety_checker(self):
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(
|
|
"stable-diffusion-v1-5/stable-diffusion-v1-5",
|
|
revision="onnx",
|
|
safety_checker=None,
|
|
feature_extractor=None,
|
|
provider=self.gpu_provider,
|
|
sess_options=self.gpu_options,
|
|
)
|
|
assert isinstance(pipe, OnnxStableDiffusionPipeline)
|
|
assert pipe.safety_checker is None
|
|
|
|
image = pipe("example prompt", num_inference_steps=2).images[0]
|
|
assert image is not None
|
|
|
|
# check that there's no error when saving a pipeline with one of the models being None
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
pipe.save_pretrained(tmpdirname)
|
|
pipe = OnnxStableDiffusionPipeline.from_pretrained(tmpdirname)
|
|
|
|
# sanity check that the pipeline still works
|
|
assert pipe.safety_checker is None
|
|
image = pipe("example prompt", num_inference_steps=2).images[0]
|
|
assert image is not None
|