229 lines
8.2 KiB
Python
229 lines
8.2 KiB
Python
# 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, CLIPTokenizer
|
|
|
|
from MuseVdiffusers import (
|
|
AutoencoderKL,
|
|
DDIMParallelScheduler,
|
|
DDPMParallelScheduler,
|
|
StableDiffusionParadigmsPipeline,
|
|
UNet2DConditionModel,
|
|
)
|
|
from MuseVdiffusers.utils.testing_utils import (
|
|
enable_full_determinism,
|
|
nightly,
|
|
require_torch_gpu,
|
|
torch_device,
|
|
)
|
|
|
|
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
|
|
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
|
|
|
|
|
|
enable_full_determinism()
|
|
|
|
|
|
class StableDiffusionParadigmsPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
pipeline_class = StableDiffusionParadigmsPipeline
|
|
params = TEXT_TO_IMAGE_PARAMS
|
|
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
|
|
image_params = TEXT_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"),
|
|
cross_attention_dim=32,
|
|
# SD2-specific config below
|
|
attention_head_dim=(2, 4),
|
|
use_linear_projection=True,
|
|
)
|
|
scheduler = DDIMParallelScheduler(
|
|
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,
|
|
sample_size=128,
|
|
)
|
|
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=512,
|
|
)
|
|
text_encoder = CLIPTextModel(text_encoder_config)
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
components = {
|
|
"unet": unet,
|
|
"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)
|
|
inputs = {
|
|
"prompt": "a photograph of an astronaut riding a horse",
|
|
"generator": generator,
|
|
"num_inference_steps": 10,
|
|
"guidance_scale": 6.0,
|
|
"output_type": "numpy",
|
|
"parallel": 3,
|
|
"debug": True,
|
|
}
|
|
return inputs
|
|
|
|
def test_stable_diffusion_paradigms_default_case(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
components = self.get_dummy_components()
|
|
sd_pipe = StableDiffusionParadigmsPipeline(**components)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(device)
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
assert image.shape == (1, 64, 64, 3)
|
|
|
|
expected_slice = np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_stable_diffusion_paradigms_default_case_ddpm(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
components = self.get_dummy_components()
|
|
torch.manual_seed(0)
|
|
components["scheduler"] = DDPMParallelScheduler()
|
|
torch.manual_seed(0)
|
|
sd_pipe = StableDiffusionParadigmsPipeline(**components)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(device)
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
assert image.shape == (1, 64, 64, 3)
|
|
|
|
expected_slice = np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
# override to speed the overall test timing up.
|
|
def test_inference_batch_consistent(self):
|
|
super().test_inference_batch_consistent(batch_sizes=[1, 2])
|
|
|
|
# override to speed the overall test timing up.
|
|
def test_inference_batch_single_identical(self):
|
|
super().test_inference_batch_single_identical(batch_size=2, expected_max_diff=3e-3)
|
|
|
|
def test_stable_diffusion_paradigms_negative_prompt(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
components = self.get_dummy_components()
|
|
sd_pipe = StableDiffusionParadigmsPipeline(**components)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(device)
|
|
negative_prompt = "french fries"
|
|
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
|
|
image = output.images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 64, 64, 3)
|
|
|
|
expected_slice = np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
|
|
@nightly
|
|
@require_torch_gpu
|
|
class StableDiffusionParadigmsPipelineSlowTests(unittest.TestCase):
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def get_inputs(self, seed=0):
|
|
generator = torch.Generator(device=torch_device).manual_seed(seed)
|
|
inputs = {
|
|
"prompt": "a photograph of an astronaut riding a horse",
|
|
"generator": generator,
|
|
"num_inference_steps": 10,
|
|
"guidance_scale": 7.5,
|
|
"output_type": "numpy",
|
|
"parallel": 3,
|
|
"debug": True,
|
|
}
|
|
return inputs
|
|
|
|
def test_stable_diffusion_paradigms_default(self):
|
|
model_ckpt = "stabilityai/stable-diffusion-2-base"
|
|
scheduler = DDIMParallelScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
|
|
pipe = StableDiffusionParadigmsPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
inputs = self.get_inputs()
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1].flatten()
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
|
|
expected_slice = np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741])
|
|
|
|
assert np.abs(expected_slice - image_slice).max() < 1e-2
|