# 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, TextToVideoSDPipeline, UNet3DConditionModel, ) from diffusers.utils import is_xformers_available from diffusers.utils.testing_utils import ( enable_full_determinism, load_numpy, require_torch_gpu, skip_mps, slow, torch_device, ) from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineTesterMixin enable_full_determinism() @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, 32), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=("CrossAttnDownBlock3D", "DownBlock3D"), up_block_types=("UpBlock3D", "CrossAttnUpBlock3D"), cross_attention_dim=4, 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,), in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D"], latent_channels=4, sample_size=32, ) torch.manual_seed(0) text_encoder_config = CLIPTextConfig( bos_token_id=0, eos_token_id=2, hidden_size=4, intermediate_size=16, layer_norm_eps=1e-05, num_attention_heads=2, num_hidden_layers=2, pad_token_id=1, vocab_size=1000, hidden_act="gelu", projection_dim=32, ) 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 == (32, 32, 3) expected_slice = np.array([91.0, 152.0, 66.0, 192.0, 94.0, 126.0, 101.0, 123.0, 152.0]) 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, expected_max_diff=3e-3) @unittest.skipIf( torch_device != "cuda" or not is_xformers_available(), reason="XFormers attention is only available with CUDA and `xformers` installed", ) def test_xformers_attention_forwardGenerator_pass(self): self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False, expected_max_diff=1e-2) # (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 @require_torch_gpu class TextToVideoSDPipelineSlowTests(unittest.TestCase): 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(torch_device) 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 def test_two_step_model_with_freeu(self): expected_video = [] pipe = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b") pipe = pipe.to(torch_device) prompt = "Spiderman is surfing" generator = torch.Generator(device="cpu").manual_seed(0) pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4) video_frames = pipe(prompt, generator=generator, num_inference_steps=2, output_type="pt").frames video = video_frames.cpu().numpy() video = video[0, 0, -3:, -3:, -1].flatten() expected_video = [-0.3102, -0.2477, -0.1772, -0.648, -0.6176, -0.5484, -0.0217, -0.056, -0.0177] assert np.abs(expected_video - video).mean() < 5e-2