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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 ( | |
enable_full_determinism, | |
load_numpy, | |
nightly, | |
require_torch_gpu, | |
torch_device, | |
) | |
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS | |
from ..test_pipelines_common import PipelineTesterMixin | |
enable_full_determinism() | |
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 | |
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([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
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 | |
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 | |