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# 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 random | |
import unittest | |
import numpy as np | |
import torch | |
from transformers import XLMRobertaTokenizer | |
from diffusers import ( | |
AltDiffusionImg2ImgPipeline, | |
AutoencoderKL, | |
PNDMScheduler, | |
UNet2DConditionModel, | |
) | |
from diffusers.image_processor import VaeImageProcessor | |
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ( | |
RobertaSeriesConfig, | |
RobertaSeriesModelWithTransformation, | |
) | |
from diffusers.utils import load_image | |
from diffusers.utils.testing_utils import ( | |
enable_full_determinism, | |
floats_tensor, | |
load_numpy, | |
nightly, | |
require_torch_gpu, | |
torch_device, | |
) | |
enable_full_determinism() | |
class AltDiffusionImg2ImgPipelineFastTests(unittest.TestCase): | |
def tearDown(self): | |
# clean up the VRAM after each test | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def dummy_image(self): | |
batch_size = 1 | |
num_channels = 3 | |
sizes = (32, 32) | |
image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device) | |
return image | |
def dummy_cond_unet(self): | |
torch.manual_seed(0) | |
model = 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, | |
) | |
return model | |
def dummy_vae(self): | |
torch.manual_seed(0) | |
model = 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, | |
) | |
return model | |
def dummy_text_encoder(self): | |
torch.manual_seed(0) | |
config = RobertaSeriesConfig( | |
hidden_size=32, | |
project_dim=32, | |
intermediate_size=37, | |
layer_norm_eps=1e-05, | |
num_attention_heads=4, | |
num_hidden_layers=5, | |
pad_token_id=1, | |
vocab_size=5006, | |
) | |
return RobertaSeriesModelWithTransformation(config) | |
def dummy_extractor(self): | |
def extract(*args, **kwargs): | |
class Out: | |
def __init__(self): | |
self.pixel_values = torch.ones([0]) | |
def to(self, device): | |
self.pixel_values.to(device) | |
return self | |
return Out() | |
return extract | |
def test_stable_diffusion_img2img_default_case(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
unet = self.dummy_cond_unet | |
scheduler = PNDMScheduler(skip_prk_steps=True) | |
vae = self.dummy_vae | |
bert = self.dummy_text_encoder | |
tokenizer = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta") | |
tokenizer.model_max_length = 77 | |
init_image = self.dummy_image.to(device) | |
init_image = init_image / 2 + 0.5 | |
# make sure here that pndm scheduler skips prk | |
alt_pipe = AltDiffusionImg2ImgPipeline( | |
unet=unet, | |
scheduler=scheduler, | |
vae=vae, | |
text_encoder=bert, | |
tokenizer=tokenizer, | |
safety_checker=None, | |
feature_extractor=self.dummy_extractor, | |
) | |
alt_pipe.image_processor = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor, do_normalize=True) | |
alt_pipe = alt_pipe.to(device) | |
alt_pipe.set_progress_bar_config(disable=None) | |
prompt = "A painting of a squirrel eating a burger" | |
generator = torch.Generator(device=device).manual_seed(0) | |
output = alt_pipe( | |
[prompt], | |
generator=generator, | |
guidance_scale=6.0, | |
num_inference_steps=2, | |
output_type="np", | |
image=init_image, | |
) | |
image = output.images | |
generator = torch.Generator(device=device).manual_seed(0) | |
image_from_tuple = alt_pipe( | |
[prompt], | |
generator=generator, | |
guidance_scale=6.0, | |
num_inference_steps=2, | |
output_type="np", | |
image=init_image, | |
return_dict=False, | |
)[0] | |
image_slice = image[0, -3:, -3:, -1] | |
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] | |
assert image.shape == (1, 32, 32, 3) | |
expected_slice = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3 | |
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-3 | |
def test_stable_diffusion_img2img_fp16(self): | |
"""Test that stable diffusion img2img works with fp16""" | |
unet = self.dummy_cond_unet | |
scheduler = PNDMScheduler(skip_prk_steps=True) | |
vae = self.dummy_vae | |
bert = self.dummy_text_encoder | |
tokenizer = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta") | |
tokenizer.model_max_length = 77 | |
init_image = self.dummy_image.to(torch_device) | |
# put models in fp16 | |
unet = unet.half() | |
vae = vae.half() | |
bert = bert.half() | |
# make sure here that pndm scheduler skips prk | |
alt_pipe = AltDiffusionImg2ImgPipeline( | |
unet=unet, | |
scheduler=scheduler, | |
vae=vae, | |
text_encoder=bert, | |
tokenizer=tokenizer, | |
safety_checker=None, | |
feature_extractor=self.dummy_extractor, | |
) | |
alt_pipe.image_processor = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor, do_normalize=False) | |
alt_pipe = alt_pipe.to(torch_device) | |
alt_pipe.set_progress_bar_config(disable=None) | |
prompt = "A painting of a squirrel eating a burger" | |
generator = torch.manual_seed(0) | |
image = alt_pipe( | |
[prompt], | |
generator=generator, | |
num_inference_steps=2, | |
output_type="np", | |
image=init_image, | |
).images | |
assert image.shape == (1, 32, 32, 3) | |
def test_stable_diffusion_img2img_pipeline_multiple_of_8(self): | |
init_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/img2img/sketch-mountains-input.jpg" | |
) | |
# resize to resolution that is divisible by 8 but not 16 or 32 | |
init_image = init_image.resize((760, 504)) | |
model_id = "BAAI/AltDiffusion" | |
pipe = AltDiffusionImg2ImgPipeline.from_pretrained( | |
model_id, | |
safety_checker=None, | |
) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
prompt = "A fantasy landscape, trending on artstation" | |
generator = torch.manual_seed(0) | |
output = pipe( | |
prompt=prompt, | |
image=init_image, | |
strength=0.75, | |
guidance_scale=7.5, | |
generator=generator, | |
output_type="np", | |
) | |
image = output.images[0] | |
image_slice = image[255:258, 383:386, -1] | |
assert image.shape == (504, 760, 3) | |
expected_slice = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
class AltDiffusionImg2ImgPipelineIntegrationTests(unittest.TestCase): | |
def tearDown(self): | |
# clean up the VRAM after each test | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def test_stable_diffusion_img2img_pipeline_default(self): | |
init_image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/img2img/sketch-mountains-input.jpg" | |
) | |
init_image = init_image.resize((768, 512)) | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy" | |
) | |
model_id = "BAAI/AltDiffusion" | |
pipe = AltDiffusionImg2ImgPipeline.from_pretrained( | |
model_id, | |
safety_checker=None, | |
) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
prompt = "A fantasy landscape, trending on artstation" | |
generator = torch.manual_seed(0) | |
output = pipe( | |
prompt=prompt, | |
image=init_image, | |
strength=0.75, | |
guidance_scale=7.5, | |
generator=generator, | |
output_type="np", | |
) | |
image = output.images[0] | |
assert image.shape == (512, 768, 3) | |
# img2img is flaky across GPUs even in fp32, so using MAE here | |
assert np.abs(expected_image - image).max() < 1e-2 | |