# coding=utf-8 # Copyright 2024 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, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionConfig, CLIPVisionModelWithProjection, ) from diffusers import ( DiffusionPipeline, UnCLIPImageVariationPipeline, UnCLIPScheduler, UNet2DConditionModel, UNet2DModel, ) from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel from diffusers.utils.testing_utils import ( enable_full_determinism, floats_tensor, load_image, load_numpy, nightly, require_torch_gpu, skip_mps, torch_device, ) from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference enable_full_determinism() class UnCLIPImageVariationPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = UnCLIPImageVariationPipeline params = IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"} batch_params = IMAGE_VARIATION_BATCH_PARAMS required_optional_params = [ "generator", "return_dict", "decoder_num_inference_steps", "super_res_num_inference_steps", ] test_xformers_attention = False @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 cross_attention_dim(self): return 100 @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, hidden_size=self.text_embedder_hidden_size, projection_dim=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 CLIPTextModelWithProjection(config) @property def dummy_image_encoder(self): torch.manual_seed(0) config = CLIPVisionConfig( hidden_size=self.text_embedder_hidden_size, projection_dim=self.text_embedder_hidden_size, num_hidden_layers=5, num_attention_heads=4, image_size=32, intermediate_size=37, patch_size=1, ) return CLIPVisionModelWithProjection(config) @property def dummy_text_proj(self): torch.manual_seed(0) model_kwargs = { "clip_embeddings_dim": self.text_embedder_hidden_size, "time_embed_dim": self.time_embed_dim, "cross_attention_dim": self.cross_attention_dim, } model = UnCLIPTextProjModel(**model_kwargs) return model @property def dummy_decoder(self): torch.manual_seed(0) model_kwargs = { "sample_size": 32, # RGB in channels "in_channels": 3, # Out channels is double in channels because predicts mean and variance "out_channels": 6, "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"), "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"), "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", "block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2), "layers_per_block": 1, "cross_attention_dim": self.cross_attention_dim, "attention_head_dim": 4, "resnet_time_scale_shift": "scale_shift", "class_embed_type": "identity", } model = UNet2DConditionModel(**model_kwargs) return model @property def dummy_super_res_kwargs(self): return { "sample_size": 64, "layers_per_block": 1, "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"), "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"), "block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2), "in_channels": 6, "out_channels": 3, } @property def dummy_super_res_first(self): torch.manual_seed(0) model = UNet2DModel(**self.dummy_super_res_kwargs) return model @property def dummy_super_res_last(self): # seeded differently to get different unet than `self.dummy_super_res_first` torch.manual_seed(1) model = UNet2DModel(**self.dummy_super_res_kwargs) return model def get_dummy_components(self): decoder = self.dummy_decoder text_proj = self.dummy_text_proj text_encoder = self.dummy_text_encoder tokenizer = self.dummy_tokenizer super_res_first = self.dummy_super_res_first super_res_last = self.dummy_super_res_last decoder_scheduler = UnCLIPScheduler( variance_type="learned_range", prediction_type="epsilon", num_train_timesteps=1000, ) super_res_scheduler = UnCLIPScheduler( variance_type="fixed_small_log", prediction_type="epsilon", num_train_timesteps=1000, ) feature_extractor = CLIPImageProcessor(crop_size=32, size=32) image_encoder = self.dummy_image_encoder return { "decoder": decoder, "text_encoder": text_encoder, "tokenizer": tokenizer, "text_proj": text_proj, "feature_extractor": feature_extractor, "image_encoder": image_encoder, "super_res_first": super_res_first, "super_res_last": super_res_last, "decoder_scheduler": decoder_scheduler, "super_res_scheduler": super_res_scheduler, } def get_dummy_inputs(self, device, seed=0, pil_image=True): 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) if pil_image: input_image = input_image * 0.5 + 0.5 input_image = input_image.clamp(0, 1) input_image = input_image.cpu().permute(0, 2, 3, 1).float().numpy() input_image = DiffusionPipeline.numpy_to_pil(input_image)[0] return { "image": input_image, "generator": generator, "decoder_num_inference_steps": 2, "super_res_num_inference_steps": 2, "output_type": "np", } def test_unclip_image_variation_input_tensor(self): device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) pipeline_inputs = self.get_dummy_inputs(device, pil_image=False) output = pipe(**pipeline_inputs) image = output.images tuple_pipeline_inputs = self.get_dummy_inputs(device, pil_image=False) image_from_tuple = pipe( **tuple_pipeline_inputs, return_dict=False, )[0] 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( [ 0.9997, 0.0002, 0.9997, 0.9997, 0.9969, 0.0023, 0.9997, 0.9969, 0.9970, ] ) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 def test_unclip_image_variation_input_image(self): device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) pipeline_inputs = self.get_dummy_inputs(device, pil_image=True) output = pipe(**pipeline_inputs) image = output.images tuple_pipeline_inputs = self.get_dummy_inputs(device, pil_image=True) image_from_tuple = pipe( **tuple_pipeline_inputs, return_dict=False, )[0] 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([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 def test_unclip_image_variation_input_list_images(self): device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) pipeline_inputs = self.get_dummy_inputs(device, pil_image=True) pipeline_inputs["image"] = [ pipeline_inputs["image"], pipeline_inputs["image"], ] output = pipe(**pipeline_inputs) image = output.images tuple_pipeline_inputs = self.get_dummy_inputs(device, pil_image=True) tuple_pipeline_inputs["image"] = [ tuple_pipeline_inputs["image"], tuple_pipeline_inputs["image"], ] image_from_tuple = pipe( **tuple_pipeline_inputs, return_dict=False, )[0] image_slice = image[0, -3:, -3:, -1] image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (2, 64, 64, 3) expected_slice = np.array( [ 0.9997, 0.9989, 0.0008, 0.0021, 0.9960, 0.0018, 0.0014, 0.0002, 0.9933, ] ) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 def test_unclip_passed_image_embed(self): device = torch.device("cpu") class DummyScheduler: init_noise_sigma = 1 components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device=device).manual_seed(0) dtype = pipe.decoder.dtype batch_size = 1 shape = ( batch_size, pipe.decoder.config.in_channels, pipe.decoder.config.sample_size, pipe.decoder.config.sample_size, ) decoder_latents = pipe.prepare_latents( shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler() ) shape = ( batch_size, pipe.super_res_first.config.in_channels // 2, pipe.super_res_first.config.sample_size, pipe.super_res_first.config.sample_size, ) super_res_latents = pipe.prepare_latents( shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler() ) pipeline_inputs = self.get_dummy_inputs(device, pil_image=False) img_out_1 = pipe( **pipeline_inputs, decoder_latents=decoder_latents, super_res_latents=super_res_latents ).images pipeline_inputs = self.get_dummy_inputs(device, pil_image=False) # Don't pass image, instead pass embedding image = pipeline_inputs.pop("image") image_embeddings = pipe.image_encoder(image).image_embeds img_out_2 = pipe( **pipeline_inputs, decoder_latents=decoder_latents, super_res_latents=super_res_latents, image_embeddings=image_embeddings, ).images # make sure passing text embeddings manually is identical assert np.abs(img_out_1 - img_out_2).max() < 1e-4 # Overriding PipelineTesterMixin::test_attention_slicing_forward_pass # because UnCLIP GPU undeterminism requires a looser check. @skip_mps def test_attention_slicing_forward_pass(self): test_max_difference = torch_device == "cpu" # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor expected_max_diff = 1e-2 self._test_attention_slicing_forward_pass( test_max_difference=test_max_difference, expected_max_diff=expected_max_diff ) # Overriding PipelineTesterMixin::test_inference_batch_single_identical # because UnCLIP undeterminism requires a looser check. @unittest.skip("UnCLIP produces very large differences. Test is not useful.") @skip_mps def test_inference_batch_single_identical(self): additional_params_copy_to_batched_inputs = [ "decoder_num_inference_steps", "super_res_num_inference_steps", ] self._test_inference_batch_single_identical( additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs, expected_max_diff=5e-3 ) def test_inference_batch_consistent(self): additional_params_copy_to_batched_inputs = [ "decoder_num_inference_steps", "super_res_num_inference_steps", ] if torch_device == "mps": # TODO: MPS errors with larger batch sizes batch_sizes = [2, 3] self._test_inference_batch_consistent( batch_sizes=batch_sizes, additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs, ) else: self._test_inference_batch_consistent( additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs ) @skip_mps def test_dict_tuple_outputs_equivalent(self): return super().test_dict_tuple_outputs_equivalent() @unittest.skip("UnCLIP produces very large difference. Test is not useful.") @skip_mps def test_save_load_local(self): return super().test_save_load_local(expected_max_difference=4e-3) @skip_mps def test_save_load_optional_components(self): return super().test_save_load_optional_components() @unittest.skip("UnCLIP produces very large difference in fp16 vs fp32. Test is not useful.") def test_float16_inference(self): super().test_float16_inference(expected_max_diff=1.0) @nightly @require_torch_gpu class UnCLIPImageVariationPipelineIntegrationTests(unittest.TestCase): def setUp(self): # clean up the VRAM before each test super().setUp() gc.collect() torch.cuda.empty_cache() def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_unclip_image_variation_karlo(self): input_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png" ) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/unclip/karlo_v1_alpha_cat_variation_fp16.npy" ) pipeline = UnCLIPImageVariationPipeline.from_pretrained( "kakaobrain/karlo-v1-alpha-image-variations", torch_dtype=torch.float16 ) pipeline = pipeline.to(torch_device) pipeline.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) output = pipeline( input_image, generator=generator, output_type="np", ) image = output.images[0] assert image.shape == (256, 256, 3) assert_mean_pixel_difference(image, expected_image, 15)