Will Berman
a72a057d62
* attend and excite batch test causing timeouts * move test num_images_per_prompt to pipeline mixin * style * prompt_key -> self.batch_params
307 lines
12 KiB
Python
307 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2023 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 PIL import Image
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from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection
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from diffusers import (
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AutoencoderKL,
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DPMSolverMultistepScheduler,
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PNDMScheduler,
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StableDiffusionImageVariationPipeline,
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UNet2DConditionModel,
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)
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from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
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from diffusers.utils.testing_utils import require_torch_gpu
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from ...pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
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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 StableDiffusionImageVariationPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = StableDiffusionImageVariationPipeline
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params = IMAGE_VARIATION_PARAMS
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batch_params = IMAGE_VARIATION_BATCH_PARAMS
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def get_dummy_components(self):
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torch.manual_seed(0)
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unet = 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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scheduler = PNDMScheduler(skip_prk_steps=True)
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torch.manual_seed(0)
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vae = 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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torch.manual_seed(0)
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image_encoder_config = CLIPVisionConfig(
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hidden_size=32,
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projection_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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image_size=32,
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patch_size=4,
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)
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image_encoder = CLIPVisionModelWithProjection(image_encoder_config)
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feature_extractor = CLIPImageProcessor(crop_size=32, size=32)
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components = {
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"unet": unet,
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"scheduler": scheduler,
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"vae": vae,
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"image_encoder": image_encoder,
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"feature_extractor": feature_extractor,
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"safety_checker": None,
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}
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return components
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def get_dummy_inputs(self, device, seed=0):
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image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed))
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image = image.cpu().permute(0, 2, 3, 1)[0]
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image = Image.fromarray(np.uint8(image)).convert("RGB").resize((32, 32))
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if str(device).startswith("mps"):
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generator = torch.manual_seed(seed)
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else:
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generator = torch.Generator(device=device).manual_seed(seed)
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inputs = {
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"image": image,
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"generator": generator,
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"num_inference_steps": 2,
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"guidance_scale": 6.0,
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"output_type": "numpy",
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}
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return inputs
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def test_stable_diffusion_img_variation_default_case(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionImageVariationPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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image = sd_pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.5167, 0.5746, 0.4835, 0.4914, 0.5605, 0.4691, 0.5201, 0.4898, 0.4958])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
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def test_stable_diffusion_img_variation_multiple_images(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionImageVariationPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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inputs["image"] = 2 * [inputs["image"]]
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output = sd_pipe(**inputs)
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image = output.images
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image_slice = image[-1, -3:, -3:, -1]
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assert image.shape == (2, 64, 64, 3)
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expected_slice = np.array([0.6568, 0.5470, 0.5684, 0.5444, 0.5945, 0.6221, 0.5508, 0.5531, 0.5263])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
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@slow
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@require_torch_gpu
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class StableDiffusionImageVariationPipelineSlowTests(unittest.TestCase):
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def tearDown(self):
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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 get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
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generator = torch.Generator(device=generator_device).manual_seed(seed)
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init_image = load_image(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_imgvar/input_image_vermeer.png"
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)
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latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
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latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
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inputs = {
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"image": init_image,
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"latents": latents,
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"generator": generator,
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"num_inference_steps": 3,
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"guidance_scale": 7.5,
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"output_type": "numpy",
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}
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return inputs
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def test_stable_diffusion_img_variation_pipeline_default(self):
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sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(
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"lambdalabs/sd-image-variations-diffusers", safety_checker=None
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)
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_inputs(torch_device)
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image = sd_pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1].flatten()
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379])
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assert np.abs(image_slice - expected_slice).max() < 1e-4
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def test_stable_diffusion_img_variation_intermediate_state(self):
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number_of_steps = 0
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def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
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callback_fn.has_been_called = True
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nonlocal number_of_steps
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number_of_steps += 1
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if step == 1:
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latents = latents.detach().cpu().numpy()
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assert latents.shape == (1, 4, 64, 64)
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latents_slice = latents[0, -3:, -3:, -1]
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expected_slice = np.array(
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[-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]
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)
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assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
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elif step == 2:
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latents = latents.detach().cpu().numpy()
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assert latents.shape == (1, 4, 64, 64)
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latents_slice = latents[0, -3:, -3:, -1]
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expected_slice = np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273])
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assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
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callback_fn.has_been_called = False
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pipe = StableDiffusionImageVariationPipeline.from_pretrained(
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"fusing/sd-image-variations-diffusers",
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safety_checker=None,
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torch_dtype=torch.float16,
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)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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inputs = self.get_inputs(torch_device, dtype=torch.float16)
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pipe(**inputs, callback=callback_fn, callback_steps=1)
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assert callback_fn.has_been_called
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assert number_of_steps == inputs["num_inference_steps"]
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def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
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torch.cuda.empty_cache()
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torch.cuda.reset_max_memory_allocated()
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torch.cuda.reset_peak_memory_stats()
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model_id = "fusing/sd-image-variations-diffusers"
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pipe = StableDiffusionImageVariationPipeline.from_pretrained(
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model_id, safety_checker=None, torch_dtype=torch.float16
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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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pipe.enable_attention_slicing(1)
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pipe.enable_sequential_cpu_offload()
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inputs = self.get_inputs(torch_device, dtype=torch.float16)
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_ = pipe(**inputs)
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mem_bytes = torch.cuda.max_memory_allocated()
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# make sure that less than 2.6 GB is allocated
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assert mem_bytes < 2.6 * 10**9
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@nightly
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@require_torch_gpu
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class StableDiffusionImageVariationPipelineNightlyTests(unittest.TestCase):
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def tearDown(self):
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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 get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
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generator = torch.Generator(device=generator_device).manual_seed(seed)
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init_image = load_image(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_imgvar/input_image_vermeer.png"
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)
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latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
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latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
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inputs = {
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"image": init_image,
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"latents": latents,
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"generator": generator,
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"num_inference_steps": 50,
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"guidance_scale": 7.5,
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"output_type": "numpy",
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}
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return inputs
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def test_img_variation_pndm(self):
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sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers")
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sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_inputs(torch_device)
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image = sd_pipe(**inputs).images[0]
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expected_image = load_numpy(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"
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)
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max_diff = np.abs(expected_image - image).max()
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assert max_diff < 1e-3
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def test_img_variation_dpm(self):
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sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers")
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sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
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sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_inputs(torch_device)
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inputs["num_inference_steps"] = 25
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image = sd_pipe(**inputs).images[0]
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expected_image = load_numpy(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"
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)
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max_diff = np.abs(expected_image - image).max()
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assert max_diff < 1e-3
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