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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 unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel
from diffusers.utils.testing_utils import load_numpy, nightly, require_torch_gpu, slow, torch_device
from ...pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = LDMTextToImagePipeline
params = TEXT_TO_IMAGE_PARAMS - {
"negative_prompt",
"negative_prompt_embeds",
"cross_attention_kwargs",
"prompt_embeds",
}
required_optional_params = PipelineTesterMixin.required_optional_params - {
"num_images_per_prompt",
"callback",
"callback_steps",
}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
test_cpu_offload = False
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,
)
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,
"scheduler": scheduler,
"vqvae": vae,
"bert": text_encoder,
"tokenizer": tokenizer,
}
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 painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_inference_text2img(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = LDMTextToImagePipeline(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
expected_slice = np.array([0.59450, 0.64078, 0.55509, 0.51229, 0.69640, 0.36960, 0.59296, 0.60801, 0.49332])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@slow
@require_torch_gpu
class LDMTextToImagePipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, dtype=torch.float32, seed=0):
generator = torch.manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 32, 32))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_ldm_default_ddim(self):
pipe = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878])
max_diff = np.abs(expected_slice - image_slice).max()
assert max_diff < 1e-3
@nightly
@require_torch_gpu
class LDMTextToImagePipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, dtype=torch.float32, seed=0):
generator = torch.manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 32, 32))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def test_ldm_default_ddim(self):
pipe = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
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