mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-29 07:22:12 +03:00
2d380895e5
* enable 7 cases on XPU Signed-off-by: Yao Matrix <matrix.yao@intel.com> * calibrate A100 expectations Signed-off-by: YAO Matrix <matrix.yao@intel.com> --------- Signed-off-by: Yao Matrix <matrix.yao@intel.com> Signed-off-by: YAO Matrix <matrix.yao@intel.com>
230 lines
8.0 KiB
Python
230 lines
8.0 KiB
Python
# 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 random
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import torch
|
|
from PIL import Image
|
|
from transformers import CLIPImageProcessor, CLIPVisionConfig
|
|
|
|
from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNet2DConditionModel
|
|
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
|
|
from diffusers.utils.testing_utils import (
|
|
backend_empty_cache,
|
|
enable_full_determinism,
|
|
floats_tensor,
|
|
load_image,
|
|
nightly,
|
|
require_torch_accelerator,
|
|
torch_device,
|
|
)
|
|
|
|
from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
|
|
from ..test_pipelines_common import PipelineTesterMixin
|
|
|
|
|
|
enable_full_determinism()
|
|
|
|
|
|
class PaintByExamplePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
|
|
pipeline_class = PaintByExamplePipeline
|
|
params = IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
|
|
batch_params = IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
|
|
image_params = frozenset([]) # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
|
|
|
|
supports_dduf = 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=9,
|
|
out_channels=4,
|
|
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
|
|
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
|
|
cross_attention_dim=32,
|
|
)
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
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)
|
|
config = CLIPVisionConfig(
|
|
hidden_size=32,
|
|
projection_dim=32,
|
|
intermediate_size=37,
|
|
layer_norm_eps=1e-05,
|
|
num_attention_heads=4,
|
|
num_hidden_layers=5,
|
|
image_size=32,
|
|
patch_size=4,
|
|
)
|
|
image_encoder = PaintByExampleImageEncoder(config, proj_size=32)
|
|
feature_extractor = CLIPImageProcessor(crop_size=32, size=32)
|
|
|
|
components = {
|
|
"unet": unet,
|
|
"scheduler": scheduler,
|
|
"vae": vae,
|
|
"image_encoder": image_encoder,
|
|
"safety_checker": None,
|
|
"feature_extractor": feature_extractor,
|
|
}
|
|
return components
|
|
|
|
def convert_to_pt(self, image):
|
|
image = np.array(image.convert("RGB"))
|
|
image = image[None].transpose(0, 3, 1, 2)
|
|
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
|
|
return image
|
|
|
|
def get_dummy_inputs(self, device="cpu", seed=0):
|
|
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
|
|
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
|
|
image = image.cpu().permute(0, 2, 3, 1)[0]
|
|
init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
|
|
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((64, 64))
|
|
example_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((32, 32))
|
|
|
|
if str(device).startswith("mps"):
|
|
generator = torch.manual_seed(seed)
|
|
else:
|
|
generator = torch.Generator(device=device).manual_seed(seed)
|
|
inputs = {
|
|
"example_image": example_image,
|
|
"image": init_image,
|
|
"mask_image": mask_image,
|
|
"generator": generator,
|
|
"num_inference_steps": 2,
|
|
"guidance_scale": 6.0,
|
|
"output_type": "np",
|
|
}
|
|
return inputs
|
|
|
|
def test_paint_by_example_inpaint(self):
|
|
components = self.get_dummy_components()
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
pipe = PaintByExamplePipeline(**components)
|
|
pipe = pipe.to("cpu")
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs()
|
|
output = pipe(**inputs)
|
|
image = output.images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 64, 64, 3)
|
|
expected_slice = np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
def test_paint_by_example_image_tensor(self):
|
|
device = "cpu"
|
|
inputs = self.get_dummy_inputs()
|
|
inputs.pop("mask_image")
|
|
image = self.convert_to_pt(inputs.pop("image"))
|
|
mask_image = image.clamp(0, 1) / 2
|
|
|
|
# make sure here that pndm scheduler skips prk
|
|
pipe = PaintByExamplePipeline(**self.get_dummy_components())
|
|
pipe = pipe.to(device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
output = pipe(image=image, mask_image=mask_image[:, 0], **inputs)
|
|
out_1 = output.images
|
|
|
|
image = image.cpu().permute(0, 2, 3, 1)[0]
|
|
mask_image = mask_image.cpu().permute(0, 2, 3, 1)[0]
|
|
|
|
image = Image.fromarray(np.uint8(image)).convert("RGB")
|
|
mask_image = Image.fromarray(np.uint8(mask_image)).convert("RGB")
|
|
|
|
output = pipe(**self.get_dummy_inputs())
|
|
out_2 = output.images
|
|
|
|
assert out_1.shape == (1, 64, 64, 3)
|
|
assert np.abs(out_1.flatten() - out_2.flatten()).max() < 5e-2
|
|
|
|
def test_inference_batch_single_identical(self):
|
|
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
|
|
|
|
|
|
@nightly
|
|
@require_torch_accelerator
|
|
class PaintByExamplePipelineIntegrationTests(unittest.TestCase):
|
|
def setUp(self):
|
|
# clean up the VRAM before each test
|
|
super().setUp()
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
|
|
|
|
def tearDown(self):
|
|
# clean up the VRAM after each test
|
|
super().tearDown()
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
|
|
|
|
def test_paint_by_example(self):
|
|
# make sure here that pndm scheduler skips prk
|
|
init_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/paint_by_example/dog_in_bucket.png"
|
|
)
|
|
mask_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/paint_by_example/mask.png"
|
|
)
|
|
example_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
|
|
"/paint_by_example/panda.jpg"
|
|
)
|
|
|
|
pipe = PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example")
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
generator = torch.manual_seed(321)
|
|
output = pipe(
|
|
image=init_image,
|
|
mask_image=mask_image,
|
|
example_image=example_image,
|
|
generator=generator,
|
|
guidance_scale=5.0,
|
|
num_inference_steps=50,
|
|
output_type="np",
|
|
)
|
|
|
|
image = output.images
|
|
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|