# 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 random import unittest import numpy as np import torch from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImg2ImgPipeline from diffusers.pipelines.shap_e import ShapERenderer from diffusers.utils.testing_utils import ( floats_tensor, load_image, load_numpy, nightly, require_torch_gpu, torch_device, ) from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference class ShapEImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = ShapEImg2ImgPipeline params = ["image"] batch_params = ["image"] required_optional_params = [ "num_images_per_prompt", "num_inference_steps", "generator", "latents", "guidance_scale", "frame_size", "output_type", "return_dict", ] test_xformers_attention = False @property def text_embedder_hidden_size(self): return 16 @property def time_input_dim(self): return 16 @property def time_embed_dim(self): return self.time_input_dim * 4 @property def renderer_dim(self): return 8 @property def dummy_image_encoder(self): torch.manual_seed(0) config = CLIPVisionConfig( hidden_size=self.text_embedder_hidden_size, image_size=32, projection_dim=self.text_embedder_hidden_size, intermediate_size=24, num_attention_heads=2, num_channels=3, num_hidden_layers=5, patch_size=1, ) model = CLIPVisionModel(config) return model @property def dummy_image_processor(self): image_processor = CLIPImageProcessor( crop_size=224, do_center_crop=True, do_normalize=True, do_resize=True, image_mean=[0.48145466, 0.4578275, 0.40821073], image_std=[0.26862954, 0.26130258, 0.27577711], resample=3, size=224, ) return image_processor @property def dummy_prior(self): torch.manual_seed(0) model_kwargs = { "num_attention_heads": 2, "attention_head_dim": 16, "embedding_dim": self.time_input_dim, "num_embeddings": 32, "embedding_proj_dim": self.text_embedder_hidden_size, "time_embed_dim": self.time_embed_dim, "num_layers": 1, "clip_embed_dim": self.time_input_dim * 2, "additional_embeddings": 0, "time_embed_act_fn": "gelu", "norm_in_type": "layer", "embedding_proj_norm_type": "layer", "encoder_hid_proj_type": None, "added_emb_type": None, } model = PriorTransformer(**model_kwargs) return model @property def dummy_renderer(self): torch.manual_seed(0) model_kwargs = { "param_shapes": ( (self.renderer_dim, 93), (self.renderer_dim, 8), (self.renderer_dim, 8), (self.renderer_dim, 8), ), "d_latent": self.time_input_dim, "d_hidden": self.renderer_dim, "n_output": 12, "background": ( 0.1, 0.1, 0.1, ), } model = ShapERenderer(**model_kwargs) return model def get_dummy_components(self): prior = self.dummy_prior image_encoder = self.dummy_image_encoder image_processor = self.dummy_image_processor shap_e_renderer = self.dummy_renderer scheduler = HeunDiscreteScheduler( beta_schedule="exp", num_train_timesteps=1024, prediction_type="sample", use_karras_sigmas=True, clip_sample=True, clip_sample_range=1.0, ) components = { "prior": prior, "image_encoder": image_encoder, "image_processor": image_processor, "shap_e_renderer": shap_e_renderer, "scheduler": scheduler, } return components def get_dummy_inputs(self, device, seed=0): input_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "image": input_image, "generator": generator, "num_inference_steps": 1, "frame_size": 32, "output_type": "np", } return inputs def test_shap_e(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[0] image_slice = image[0, -3:, -3:, -1] assert image.shape == (20, 32, 32, 3) expected_slice = np.array( [ 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, ] ) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_inference_batch_consistent(self): # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches self._test_inference_batch_consistent(batch_sizes=[2]) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical( batch_size=2, expected_max_diff=5e-3, ) def test_num_images_per_prompt(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) batch_size = 1 num_images_per_prompt = 2 inputs = self.get_dummy_inputs(torch_device) for key in inputs.keys(): if key in self.batch_params: inputs[key] = batch_size * [inputs[key]] images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0] assert images.shape[0] == batch_size * num_images_per_prompt @nightly @require_torch_gpu class ShapEImg2ImgPipelineIntegrationTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_shap_e_img2img(self): input_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png" ) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/test_shap_e_img2img_out.npy" ) pipe = ShapEImg2ImgPipeline.from_pretrained("openai/shap-e-img2img") pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device=torch_device).manual_seed(0) images = pipe( input_image, generator=generator, guidance_scale=3.0, num_inference_steps=64, frame_size=64, output_type="np", ).images[0] assert images.shape == (20, 64, 64, 3) assert_mean_pixel_difference(images, expected_image)