# 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 gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNet2DConditionModel from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ( RobertaSeriesConfig, RobertaSeriesModelWithTransformation, ) from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() class AltDiffusionPipelineFastTests( PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase ): pipeline_class = AltDiffusionPipeline params = TEXT_TO_IMAGE_PARAMS batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = TEXT_TO_IMAGE_IMAGE_PARAMS image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS def get_dummy_components(self): torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, ) 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, ) # TODO: address the non-deterministic text encoder (fails for save-load tests) # torch.manual_seed(0) # text_encoder_config = RobertaSeriesConfig( # hidden_size=32, # project_dim=32, # intermediate_size=37, # layer_norm_eps=1e-05, # num_attention_heads=4, # num_hidden_layers=5, # vocab_size=5002, # ) # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config) torch.manual_seed(0) text_encoder_config = CLIPTextConfig( bos_token_id=0, eos_token_id=2, hidden_size=32, projection_dim=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=5002, ) text_encoder = CLIPTextModel(text_encoder_config) tokenizer = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta") tokenizer.model_max_length = 77 components = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, } 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": "numpy", } return inputs def test_attention_slicing_forward_pass(self): super().test_attention_slicing_forward_pass(expected_max_diff=3e-3) def test_inference_batch_single_identical(self): super().test_inference_batch_single_identical(expected_max_diff=3e-3) def test_alt_diffusion_ddim(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() torch.manual_seed(0) text_encoder_config = RobertaSeriesConfig( hidden_size=32, project_dim=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, vocab_size=5002, ) # TODO: remove after fixing the non-deterministic text encoder text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config) components["text_encoder"] = text_encoder alt_pipe = AltDiffusionPipeline(**components) alt_pipe = alt_pipe.to(device) alt_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) inputs["prompt"] = "A photo of an astronaut" output = alt_pipe(**inputs) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) expected_slice = np.array( [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093] ) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_alt_diffusion_pndm(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() components["scheduler"] = PNDMScheduler(skip_prk_steps=True) torch.manual_seed(0) text_encoder_config = RobertaSeriesConfig( hidden_size=32, project_dim=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, vocab_size=5002, ) # TODO: remove after fixing the non-deterministic text encoder text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config) components["text_encoder"] = text_encoder alt_pipe = AltDiffusionPipeline(**components) alt_pipe = alt_pipe.to(device) alt_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) output = alt_pipe(**inputs) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) expected_slice = np.array( [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237] ) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @nightly @require_torch_gpu class AltDiffusionPipelineIntegrationTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_alt_diffusion(self): # make sure here that pndm scheduler skips prk alt_pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion", safety_checker=None) alt_pipe = alt_pipe.to(torch_device) alt_pipe.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" generator = torch.manual_seed(0) output = alt_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np") image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_alt_diffusion_fast_ddim(self): scheduler = DDIMScheduler.from_pretrained("BAAI/AltDiffusion", subfolder="scheduler") alt_pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion", scheduler=scheduler, safety_checker=None) alt_pipe = alt_pipe.to(torch_device) alt_pipe.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" generator = torch.manual_seed(0) output = alt_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy") image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2