348 lines
12 KiB
Python
348 lines
12 KiB
Python
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# 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 gc
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import random
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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 AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNet2DConditionModel
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from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
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RobertaSeriesConfig,
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RobertaSeriesModelWithTransformation,
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)
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from diffusers.utils import floats_tensor, slow, torch_device
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from diffusers.utils.testing_utils import require_torch_gpu
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from transformers import XLMRobertaTokenizer
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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 AltDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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@property
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def dummy_image(self):
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batch_size = 1
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num_channels = 3
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sizes = (32, 32)
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image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
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return image
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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_cond_unet_inpaint(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=9,
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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 = RobertaSeriesConfig(
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hidden_size=32,
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project_dim=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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vocab_size=5002,
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)
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return RobertaSeriesModelWithTransformation(config)
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@property
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def dummy_extractor(self):
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def extract(*args, **kwargs):
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class Out:
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def __init__(self):
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self.pixel_values = torch.ones([0])
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def to(self, device):
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self.pixel_values.to(device)
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return self
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return Out()
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return extract
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def test_alt_diffusion_ddim(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False,
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)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
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tokenizer.model_max_length = 77
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# make sure here that pndm scheduler skips prk
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alt_pipe = AltDiffusionPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=None,
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feature_extractor=self.dummy_extractor,
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)
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alt_pipe = alt_pipe.to(device)
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alt_pipe.set_progress_bar_config(disable=None)
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prompt = "A photo of an astronaut"
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generator = torch.Generator(device=device).manual_seed(0)
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output = alt_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
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image = output.images
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generator = torch.Generator(device=device).manual_seed(0)
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image_from_tuple = alt_pipe(
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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="np",
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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, 128, 128, 3)
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expected_slice = np.array(
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[0.49249017, 0.46064827, 0.4790093, 0.50883967, 0.4811985, 0.51540506, 0.5084924, 0.4860553, 0.47318557]
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)
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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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def test_alt_diffusion_pndm(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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unet = self.dummy_cond_unet
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scheduler = PNDMScheduler(skip_prk_steps=True)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
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tokenizer.model_max_length = 77
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# make sure here that pndm scheduler skips prk
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alt_pipe = AltDiffusionPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=None,
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feature_extractor=self.dummy_extractor,
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)
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alt_pipe = alt_pipe.to(device)
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alt_pipe.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.Generator(device=device).manual_seed(0)
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output = alt_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
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image = output.images
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generator = torch.Generator(device=device).manual_seed(0)
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image_from_tuple = alt_pipe(
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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="np",
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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, 128, 128, 3)
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expected_slice = np.array(
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[0.4786532, 0.45791715, 0.47507674, 0.50763345, 0.48375353, 0.515062, 0.51244247, 0.48673993, 0.47105807]
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)
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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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@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
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def test_alt_diffusion_fp16(self):
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"""Test that stable diffusion works with fp16"""
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unet = self.dummy_cond_unet
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scheduler = PNDMScheduler(skip_prk_steps=True)
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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tokenizer = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
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tokenizer.model_max_length = 77
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# put models in fp16
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unet = unet.half()
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vae = vae.half()
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bert = bert.half()
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# make sure here that pndm scheduler skips prk
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alt_pipe = AltDiffusionPipeline(
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unet=unet,
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scheduler=scheduler,
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vae=vae,
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text_encoder=bert,
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tokenizer=tokenizer,
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safety_checker=None,
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feature_extractor=self.dummy_extractor,
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)
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alt_pipe = alt_pipe.to(torch_device)
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alt_pipe.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.Generator(device=torch_device).manual_seed(0)
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image = alt_pipe([prompt], generator=generator, num_inference_steps=2, output_type="np").images
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assert image.shape == (1, 128, 128, 3)
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@slow
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@require_torch_gpu
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class AltDiffusionPipelineIntegrationTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def test_alt_diffusion(self):
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# make sure here that pndm scheduler skips prk
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alt_pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion", safety_checker=None)
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alt_pipe = alt_pipe.to(torch_device)
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alt_pipe.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.Generator(device=torch_device).manual_seed(0)
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with torch.autocast("cuda"):
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output = alt_pipe(
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[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np"
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)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array(
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[0.8720703, 0.87109375, 0.87402344, 0.87109375, 0.8779297, 0.8925781, 0.8823242, 0.8808594, 0.8613281]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_alt_diffusion_fast_ddim(self):
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scheduler = DDIMScheduler.from_pretrained("BAAI/AltDiffusion", subfolder="scheduler")
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alt_pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion", scheduler=scheduler, safety_checker=None)
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alt_pipe = alt_pipe.to(torch_device)
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alt_pipe.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.Generator(device=torch_device).manual_seed(0)
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with torch.autocast("cuda"):
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output = alt_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy")
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array(
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[0.9267578, 0.9301758, 0.9013672, 0.9345703, 0.92578125, 0.94433594, 0.9423828, 0.9423828, 0.9160156]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_alt_diffusion_text2img_pipeline_fp16(self):
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torch.cuda.reset_peak_memory_stats()
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model_id = "BAAI/AltDiffusion"
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pipe = AltDiffusionPipeline.from_pretrained(
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model_id, revision="fp16", torch_dtype=torch.float16, safety_checker=None
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)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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prompt = "a photograph of an astronaut riding a horse"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output_chunked = pipe(
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[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
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)
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image_chunked = output_chunked.images
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generator = torch.Generator(device=torch_device).manual_seed(0)
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with torch.autocast(torch_device):
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output = pipe(
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[prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
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)
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image = output.images
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# Make sure results are close enough
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diff = np.abs(image_chunked.flatten() - image.flatten())
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# They ARE different since ops are not run always at the same precision
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# however, they should be extremely close.
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assert diff.mean() < 2e-2
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