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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 (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNet3DConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
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
@skip_mps
class TextToVideoSDPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = TextToVideoSDPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
required_optional_params = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
]
)
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet3DConditionModel(
block_out_channels=(32, 64, 64, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D"),
up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"),
cross_attention_dim=32,
attention_head_dim=4,
)
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,
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,
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,
}
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": "pt",
}
return inputs
def test_text_to_video_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = TextToVideoSDPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
inputs["output_type"] = "np"
frames = sd_pipe(**inputs).frames
image_slice = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
expected_slice = np.array([166, 184, 167, 118, 102, 123, 108, 93, 114])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)
# (todo): sayakpaul
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline.")
def test_inference_batch_consistent(self):
pass
# (todo): sayakpaul
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline.")
def test_inference_batch_single_identical(self):
pass
@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline.")
def test_num_images_per_prompt(self):
pass
def test_progress_bar(self):
return super().test_progress_bar()
@slow
@skip_mps
class TextToVideoSDPipelineSlowTests(unittest.TestCase):
def test_full_model(self):
expected_video = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"
)
pipe = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
prompt = "Spiderman is surfing"
generator = torch.Generator(device="cpu").manual_seed(0)
video_frames = pipe(prompt, generator=generator, num_inference_steps=25, output_type="pt").frames
video = video_frames.cpu().numpy()
assert np.abs(expected_video - video).mean() < 5e-2
def test_two_step_model(self):
expected_video = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"
)
pipe = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b")
pipe = pipe.to("cuda")
prompt = "Spiderman is surfing"
generator = torch.Generator(device="cpu").manual_seed(0)
video_frames = pipe(prompt, generator=generator, num_inference_steps=2, output_type="pt").frames
video = video_frames.cpu().numpy()
assert np.abs(expected_video - video).mean() < 5e-2
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