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tests/diffusers_tests/pipelines/animatediff/test_animatediff_video2video_controlnet.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,198 @@ | ||
| import unittest | ||
|
|
||
| import numpy as np | ||
| import torch | ||
| from ddt import data, ddt, unpack | ||
| from PIL import Image | ||
| from transformers import CLIPTextConfig | ||
|
|
||
| import mindspore as ms | ||
|
|
||
| from ..pipeline_test_utils import ( | ||
| THRESHOLD_FP16, | ||
| THRESHOLD_FP32, | ||
| PipelineTesterMixin, | ||
| get_module, | ||
| get_pipeline_components, | ||
| ) | ||
|
|
||
| test_cases = [ | ||
| {"mode": ms.PYNATIVE_MODE, "dtype": "float32"}, | ||
| {"mode": ms.PYNATIVE_MODE, "dtype": "float16"}, | ||
| {"mode": ms.GRAPH_MODE, "dtype": "float32"}, | ||
| {"mode": ms.GRAPH_MODE, "dtype": "float16"}, | ||
| ] | ||
|
|
||
|
|
||
| @ddt | ||
| class AnimateDiffVideoToVideoControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | ||
| cross_attention_dim = 8 | ||
| block_out_channels = (8, 8) | ||
|
|
||
| pipeline_config = [ | ||
| [ | ||
| "unet", | ||
| "diffusers.models.unets.unet_2d_condition.UNet2DConditionModel", | ||
| "mindone.diffusers.models.unets.unet_2d_condition.UNet2DConditionModel", | ||
| dict( | ||
| block_out_channels=block_out_channels, | ||
| layers_per_block=2, | ||
| sample_size=8, | ||
| in_channels=4, | ||
| out_channels=4, | ||
| down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"), | ||
| up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | ||
| cross_attention_dim=cross_attention_dim, | ||
| norm_num_groups=2, | ||
| ), | ||
| ], | ||
| [ | ||
| "scheduler", | ||
| "diffusers.schedulers.scheduling_ddim.DDIMScheduler", | ||
| "mindone.diffusers.schedulers.scheduling_ddim.DDIMScheduler", | ||
| dict( | ||
| beta_start=0.00085, | ||
| beta_end=0.012, | ||
| beta_schedule="linear", | ||
| clip_sample=False, | ||
| ), | ||
| ], | ||
| [ | ||
| "controlnet", | ||
| "diffusers.models.controlnet.ControlNetModel", | ||
| "mindone.diffusers.models.controlnet.ControlNetModel", | ||
| dict( | ||
| block_out_channels=block_out_channels, | ||
| layers_per_block=2, | ||
| in_channels=4, | ||
| down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"), | ||
| cross_attention_dim=cross_attention_dim, | ||
| conditioning_embedding_out_channels=(8, 8), | ||
| norm_num_groups=1, | ||
| ), | ||
| ], | ||
| [ | ||
| "vae", | ||
| "diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL", | ||
| "mindone.diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL", | ||
| dict( | ||
| block_out_channels=block_out_channels, | ||
| in_channels=3, | ||
| out_channels=3, | ||
| down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | ||
| up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | ||
| latent_channels=4, | ||
| norm_num_groups=2, | ||
| ), | ||
| ], | ||
| [ | ||
| "text_encoder", | ||
| "transformers.models.clip.modeling_clip.CLIPTextModel", | ||
| "mindone.transformers.models.clip.modeling_clip.CLIPTextModel", | ||
| dict( | ||
| config=CLIPTextConfig( | ||
| bos_token_id=0, | ||
| eos_token_id=2, | ||
| hidden_size=cross_attention_dim, | ||
| intermediate_size=37, | ||
| layer_norm_eps=1e-05, | ||
| num_attention_heads=4, | ||
| num_hidden_layers=5, | ||
| pad_token_id=1, | ||
| vocab_size=1000, | ||
| ), | ||
| ), | ||
| ], | ||
| [ | ||
| "tokenizer", | ||
| "transformers.models.clip.tokenization_clip.CLIPTokenizer", | ||
| "transformers.models.clip.tokenization_clip.CLIPTokenizer", | ||
| dict( | ||
| pretrained_model_name_or_path="hf-internal-testing/tiny-random-clip", | ||
| ), | ||
| ], | ||
| [ | ||
| "motion_adapter", | ||
| "diffusers.models.unets.unet_motion_model.MotionAdapter", | ||
| "mindone.diffusers.models.unets.unet_motion_model.MotionAdapter", | ||
| dict( | ||
| block_out_channels=block_out_channels, | ||
| motion_layers_per_block=2, | ||
| motion_norm_num_groups=2, | ||
| motion_num_attention_heads=4, | ||
| ), | ||
| ], | ||
| ] | ||
|
|
||
| def get_dummy_components(self): | ||
| components = { | ||
| key: None | ||
| for key in [ | ||
| "unet", | ||
| "controlnet", | ||
| "scheduler", | ||
| "vae", | ||
| "motion_adapter", | ||
| "text_encoder", | ||
| "tokenizer", | ||
| "feature_extractor", | ||
| "image_encoder", | ||
| ] | ||
| } | ||
|
|
||
| return get_pipeline_components(components, self.pipeline_config) | ||
|
|
||
| def get_dummy_inputs(self, num_frames: int = 2): | ||
| video_height = 32 | ||
| video_width = 32 | ||
| video = [Image.new("RGB", (video_width, video_height))] * num_frames | ||
|
|
||
| video_height = 32 | ||
| video_width = 32 | ||
| conditioning_frames = [Image.new("RGB", (video_width, video_height))] * num_frames | ||
|
|
||
| inputs = { | ||
| "video": video, | ||
| "conditioning_frames": conditioning_frames, | ||
| "prompt": "A painting of a squirrel eating a burger", | ||
| "num_inference_steps": 2, | ||
| "guidance_scale": 7.5, | ||
| "output_type": "np", | ||
| } | ||
| return inputs | ||
|
|
||
| @data(*test_cases) | ||
| @unpack | ||
| def test_inference(self, mode, dtype): | ||
| ms.set_context(mode=mode) | ||
|
|
||
| pt_components, ms_components = self.get_dummy_components() | ||
| pt_pipe_cls = get_module( | ||
| "diffusers.pipelines.animatediff.pipeline_animatediff_video2video_controlnet.AnimateDiffVideoToVideoControlNetPipeline" | ||
| ) | ||
| ms_pipe_cls = get_module( | ||
| "mindone.diffusers.pipelines.animatediff.pipeline_animatediff_video2video_controlnet.AnimateDiffVideoToVideoControlNetPipeline" | ||
| ) | ||
|
|
||
| pt_pipe = pt_pipe_cls(**pt_components) | ||
| ms_pipe = ms_pipe_cls(**ms_components) | ||
|
|
||
| pt_pipe.set_progress_bar_config(disable=None) | ||
| ms_pipe.set_progress_bar_config(disable=None) | ||
|
|
||
| ms_dtype, pt_dtype = getattr(ms, dtype), getattr(torch, dtype) | ||
| pt_pipe = pt_pipe.to(pt_dtype) | ||
| ms_pipe = ms_pipe.to(ms_dtype) | ||
|
|
||
| inputs = self.get_dummy_inputs() | ||
|
|
||
| torch.manual_seed(0) | ||
| pt_frame = pt_pipe(**inputs) | ||
| torch.manual_seed(0) | ||
| ms_frame = ms_pipe(**inputs) | ||
|
|
||
| pt_image_slice = pt_frame.frames[0][0, -3:, -3:, -1] | ||
| ms_image_slice = ms_frame[0][0][0, -3:, -3:, -1] | ||
|
|
||
| threshold = THRESHOLD_FP32 if dtype == "float32" else THRESHOLD_FP16 | ||
| assert np.max(np.linalg.norm(pt_image_slice - ms_image_slice) / np.linalg.norm(pt_image_slice)) < threshold |
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