154 lines
5.4 KiB
Python
154 lines
5.4 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import torch
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from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel
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from diffusers.utils.testing_utils import require_torch, slow, torch_device
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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@property
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def dummy_cond_unet(self):
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torch.manual_seed(0)
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model = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=4,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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)
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return model
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@property
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def dummy_vae(self):
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torch.manual_seed(0)
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model = AutoencoderKL(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=4,
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)
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return model
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@property
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def dummy_text_encoder(self):
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torch.manual_seed(0)
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config = CLIPTextConfig(
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bos_token_id=0,
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eos_token_id=2,
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hidden_size=32,
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intermediate_size=37,
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layer_norm_eps=1e-05,
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num_attention_heads=4,
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num_hidden_layers=5,
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pad_token_id=1,
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vocab_size=1000,
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)
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return CLIPTextModel(config)
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def test_inference_text2img(self):
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unet = self.dummy_cond_unet
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scheduler = DDIMScheduler()
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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ldm = LDMTextToImagePipeline(vqvae=vae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
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ldm.to(torch_device)
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ldm.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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# Warmup pass when using mps (see #372)
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if torch_device == "mps":
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generator = torch.manual_seed(0)
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_ = ldm(
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[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=1, output_type="numpy"
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).images
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generator = torch.manual_seed(0)
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image = ldm(
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[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy"
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).images
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generator = torch.manual_seed(0)
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image_from_tuple = ldm(
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[prompt],
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generator=generator,
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guidance_scale=6.0,
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num_inference_steps=2,
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output_type="numpy",
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return_dict=False,
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)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.5074, 0.5026, 0.4998, 0.4056, 0.3523, 0.4649, 0.5289, 0.5299, 0.4897])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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@slow
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@require_torch
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class LDMTextToImagePipelineIntegrationTests(unittest.TestCase):
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def test_inference_text2img(self):
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ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
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ldm.to(torch_device)
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ldm.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.manual_seed(0)
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image = ldm(
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[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy"
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).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 256, 256, 3)
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expected_slice = np.array([0.9256, 0.9340, 0.8933, 0.9361, 0.9113, 0.8727, 0.9122, 0.8745, 0.8099])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_inference_text2img_fast(self):
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ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
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ldm.to(torch_device)
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ldm.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.manual_seed(0)
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image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy").images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 256, 256, 3)
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expected_slice = np.array([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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