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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 unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import DDPMWuerstchenScheduler, WuerstchenDecoderPipeline
from diffusers.pipelines.wuerstchen import PaellaVQModel, WuerstchenDiffNeXt
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class WuerstchenDecoderPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = WuerstchenDecoderPipeline
params = ["prompt"]
batch_params = ["image_embeddings", "prompt", "negative_prompt"]
required_optional_params = [
"num_images_per_prompt",
"num_inference_steps",
"latents",
"negative_prompt",
"guidance_scale",
"output_type",
"return_dict",
]
test_xformers_attention = False
callback_cfg_params = ["image_embeddings", "text_encoder_hidden_states"]
@property
def text_embedder_hidden_size(self):
return 32
@property
def time_input_dim(self):
return 32
@property
def block_out_channels_0(self):
return self.time_input_dim
@property
def time_embed_dim(self):
return self.time_input_dim * 4
@property
def dummy_tokenizer(self):
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
projection_dim=self.text_embedder_hidden_size,
hidden_size=self.text_embedder_hidden_size,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
return CLIPTextModel(config).eval()
@property
def dummy_vqgan(self):
torch.manual_seed(0)
model_kwargs = {
"bottleneck_blocks": 1,
"num_vq_embeddings": 2,
}
model = PaellaVQModel(**model_kwargs)
return model.eval()
@property
def dummy_decoder(self):
torch.manual_seed(0)
model_kwargs = {
"c_cond": self.text_embedder_hidden_size,
"c_hidden": [320],
"nhead": [-1],
"blocks": [4],
"level_config": ["CT"],
"clip_embd": self.text_embedder_hidden_size,
"inject_effnet": [False],
}
model = WuerstchenDiffNeXt(**model_kwargs)
return model.eval()
def get_dummy_components(self):
decoder = self.dummy_decoder
text_encoder = self.dummy_text_encoder
tokenizer = self.dummy_tokenizer
vqgan = self.dummy_vqgan
scheduler = DDPMWuerstchenScheduler()
components = {
"decoder": decoder,
"vqgan": vqgan,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"scheduler": scheduler,
"latent_dim_scale": 4.0,
}
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 = {
"image_embeddings": torch.ones((1, 4, 4, 4), device=device),
"prompt": "horse",
"generator": generator,
"guidance_scale": 1.0,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def test_wuerstchen_decoder(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(device))
image = output.images
image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.0000, 0.0000, 0.0089, 1.0000, 1.0000, 0.3927, 1.0000, 1.0000, 1.0000])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@skip_mps
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=1e-5)
@skip_mps
def test_attention_slicing_forward_pass(self):
test_max_difference = torch_device == "cpu"
test_mean_pixel_difference = False
self._test_attention_slicing_forward_pass(
test_max_difference=test_max_difference,
test_mean_pixel_difference=test_mean_pixel_difference,
)
@unittest.skip(reason="bf16 not supported and requires CUDA")
def test_float16_inference(self):
super().test_float16_inference()
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