# 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 unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel from diffusers.utils.testing_utils import ( enable_full_determinism, load_numpy, nightly, require_torch_gpu, torch_device, ) from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineTesterMixin enable_full_determinism() class LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = LDMTextToImagePipeline params = TEXT_TO_IMAGE_PARAMS - { "negative_prompt", "negative_prompt_embeds", "cross_attention_kwargs", "prompt_embeds", } required_optional_params = PipelineTesterMixin.required_optional_params - { "num_images_per_prompt", "callback", "callback_steps", } batch_params = TEXT_TO_IMAGE_BATCH_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, ) torch.manual_seed(0) text_encoder_config = CLIPTextConfig( bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1000, ) text_encoder = CLIPTextModel(text_encoder_config) tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") components = { "unet": unet, "scheduler": scheduler, "vqvae": vae, "bert": 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": "np", } return inputs def test_inference_text2img(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() pipe = LDMTextToImagePipeline(**components) pipe.to(device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) image = pipe(**inputs).images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 16, 16, 3) expected_slice = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 @nightly @require_torch_gpu class LDMTextToImagePipelineSlowTests(unittest.TestCase): def setUp(self): super().setUp() gc.collect() torch.cuda.empty_cache() def tearDown(self): super().tearDown() gc.collect() torch.cuda.empty_cache() def get_inputs(self, device, dtype=torch.float32, seed=0): generator = torch.manual_seed(seed) latents = np.random.RandomState(seed).standard_normal((1, 4, 32, 32)) latents = torch.from_numpy(latents).to(device=device, dtype=dtype) inputs = { "prompt": "A painting of a squirrel eating a burger", "latents": latents, "generator": generator, "num_inference_steps": 3, "guidance_scale": 6.0, "output_type": "np", } return inputs def test_ldm_default_ddim(self): pipe = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_inputs(torch_device) image = pipe(**inputs).images image_slice = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 256, 256, 3) expected_slice = np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878]) max_diff = np.abs(expected_slice - image_slice).max() assert max_diff < 1e-3 @nightly @require_torch_gpu class LDMTextToImagePipelineNightlyTests(unittest.TestCase): def setUp(self): super().setUp() gc.collect() torch.cuda.empty_cache() def tearDown(self): super().tearDown() gc.collect() torch.cuda.empty_cache() def get_inputs(self, device, dtype=torch.float32, seed=0): generator = torch.manual_seed(seed) latents = np.random.RandomState(seed).standard_normal((1, 4, 32, 32)) latents = torch.from_numpy(latents).to(device=device, dtype=dtype) inputs = { "prompt": "A painting of a squirrel eating a burger", "latents": latents, "generator": generator, "num_inference_steps": 50, "guidance_scale": 6.0, "output_type": "np", } return inputs def test_ldm_default_ddim(self): pipe = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_inputs(torch_device) image = pipe(**inputs).images[0] expected_image = load_numpy( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy" ) max_diff = np.abs(expected_image - image).max() assert max_diff < 1e-3