Spaces:
Sleeping
Sleeping
| # 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, CLIPTokenizer | |
| from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel | |
| from diffusers.utils.testing_utils import load_numpy, nightly, require_torch_gpu, slow, torch_device | |
| from ...pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS | |
| from ...test_pipelines_common import PipelineTesterMixin | |
| torch.backends.cuda.matmul.allow_tf32 = False | |
| 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 | |
| test_cpu_offload = False | |
| 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": "numpy", | |
| } | |
| 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([0.59450, 0.64078, 0.55509, 0.51229, 0.69640, 0.36960, 0.59296, 0.60801, 0.49332]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
| class LDMTextToImagePipelineSlowTests(unittest.TestCase): | |
| 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": "numpy", | |
| } | |
| 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 | |
| class LDMTextToImagePipelineNightlyTests(unittest.TestCase): | |
| 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": "numpy", | |
| } | |
| 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 | |