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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 PIL import Image | |
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNet2DConditionModel | |
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device | |
from diffusers.utils.testing_utils import require_torch_gpu | |
torch.backends.cuda.matmul.allow_tf32 = False | |
class StableDiffusionUpscalePipelineFastTests(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_upscale(self): | |
torch.manual_seed(0) | |
model = UNet2DConditionModel( | |
block_out_channels=(32, 32, 64), | |
layers_per_block=2, | |
sample_size=32, | |
in_channels=7, | |
out_channels=4, | |
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"), | |
up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"), | |
cross_attention_dim=32, | |
# SD2-specific config below | |
attention_head_dim=8, | |
use_linear_projection=True, | |
only_cross_attention=(True, True, False), | |
num_class_embeds=100, | |
) | |
return model | |
def dummy_vae(self): | |
torch.manual_seed(0) | |
model = AutoencoderKL( | |
block_out_channels=[32, 32, 64], | |
in_channels=3, | |
out_channels=3, | |
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"], | |
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"], | |
latent_channels=4, | |
) | |
return model | |
def dummy_text_encoder(self): | |
torch.manual_seed(0) | |
config = CLIPTextConfig( | |
bos_token_id=0, | |
eos_token_id=2, | |
hidden_size=32, | |
intermediate_size=37, | |
layer_norm_eps=1e-05, | |
num_attention_heads=4, | |
num_hidden_layers=5, | |
pad_token_id=1, | |
vocab_size=1000, | |
# SD2-specific config below | |
hidden_act="gelu", | |
projection_dim=512, | |
) | |
return CLIPTextModel(config) | |
def test_stable_diffusion_upscale(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
unet = self.dummy_cond_unet_upscale | |
low_res_scheduler = DDPMScheduler() | |
scheduler = DDIMScheduler(prediction_type="v_prediction") | |
vae = self.dummy_vae | |
text_encoder = self.dummy_text_encoder | |
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] | |
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) | |
# make sure here that pndm scheduler skips prk | |
sd_pipe = StableDiffusionUpscalePipeline( | |
unet=unet, | |
low_res_scheduler=low_res_scheduler, | |
scheduler=scheduler, | |
vae=vae, | |
text_encoder=text_encoder, | |
tokenizer=tokenizer, | |
max_noise_level=350, | |
) | |
sd_pipe = sd_pipe.to(device) | |
sd_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 = sd_pipe( | |
[prompt], | |
image=low_res_image, | |
generator=generator, | |
guidance_scale=6.0, | |
noise_level=20, | |
num_inference_steps=2, | |
output_type="np", | |
) | |
image = output.images | |
generator = torch.Generator(device=device).manual_seed(0) | |
image_from_tuple = sd_pipe( | |
[prompt], | |
image=low_res_image, | |
generator=generator, | |
guidance_scale=6.0, | |
noise_level=20, | |
num_inference_steps=2, | |
output_type="np", | |
return_dict=False, | |
)[0] | |
image_slice = image[0, -3:, -3:, -1] | |
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] | |
expected_height_width = low_res_image.size[0] * 4 | |
assert image.shape == (1, expected_height_width, expected_height_width, 3) | |
expected_slice = np.array([0.2562, 0.3606, 0.4204, 0.4469, 0.4822, 0.4647, 0.5315, 0.5748, 0.5606]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_upscale_batch(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
unet = self.dummy_cond_unet_upscale | |
low_res_scheduler = DDPMScheduler() | |
scheduler = DDIMScheduler(prediction_type="v_prediction") | |
vae = self.dummy_vae | |
text_encoder = self.dummy_text_encoder | |
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] | |
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) | |
# make sure here that pndm scheduler skips prk | |
sd_pipe = StableDiffusionUpscalePipeline( | |
unet=unet, | |
low_res_scheduler=low_res_scheduler, | |
scheduler=scheduler, | |
vae=vae, | |
text_encoder=text_encoder, | |
tokenizer=tokenizer, | |
max_noise_level=350, | |
) | |
sd_pipe = sd_pipe.to(device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
prompt = "A painting of a squirrel eating a burger" | |
output = sd_pipe( | |
2 * [prompt], | |
image=2 * [low_res_image], | |
guidance_scale=6.0, | |
noise_level=20, | |
num_inference_steps=2, | |
output_type="np", | |
) | |
image = output.images | |
assert image.shape[0] == 2 | |
generator = torch.Generator(device=device).manual_seed(0) | |
output = sd_pipe( | |
[prompt], | |
image=low_res_image, | |
generator=generator, | |
num_images_per_prompt=2, | |
guidance_scale=6.0, | |
noise_level=20, | |
num_inference_steps=2, | |
output_type="np", | |
) | |
image = output.images | |
assert image.shape[0] == 2 | |
def test_stable_diffusion_upscale_fp16(self): | |
"""Test that stable diffusion upscale works with fp16""" | |
unet = self.dummy_cond_unet_upscale | |
low_res_scheduler = DDPMScheduler() | |
scheduler = DDIMScheduler(prediction_type="v_prediction") | |
vae = self.dummy_vae | |
text_encoder = self.dummy_text_encoder | |
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] | |
low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) | |
# put models in fp16, except vae as it overflows in fp16 | |
unet = unet.half() | |
text_encoder = text_encoder.half() | |
# make sure here that pndm scheduler skips prk | |
sd_pipe = StableDiffusionUpscalePipeline( | |
unet=unet, | |
low_res_scheduler=low_res_scheduler, | |
scheduler=scheduler, | |
vae=vae, | |
text_encoder=text_encoder, | |
tokenizer=tokenizer, | |
max_noise_level=350, | |
) | |
sd_pipe = sd_pipe.to(torch_device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
prompt = "A painting of a squirrel eating a burger" | |
generator = torch.manual_seed(0) | |
image = sd_pipe( | |
[prompt], | |
image=low_res_image, | |
generator=generator, | |
num_inference_steps=2, | |
output_type="np", | |
).images | |
expected_height_width = low_res_image.size[0] * 4 | |
assert image.shape == (1, expected_height_width, expected_height_width, 3) | |
class StableDiffusionUpscalePipelineIntegrationTests(unittest.TestCase): | |
def tearDown(self): | |
# clean up the VRAM after each test | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def test_stable_diffusion_upscale_pipeline(self): | |
image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/sd2-upscale/low_res_cat.png" | |
) | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale" | |
"/upsampled_cat.npy" | |
) | |
model_id = "stabilityai/stable-diffusion-x4-upscaler" | |
pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
prompt = "a cat sitting on a park bench" | |
generator = torch.manual_seed(0) | |
output = pipe( | |
prompt=prompt, | |
image=image, | |
generator=generator, | |
output_type="np", | |
) | |
image = output.images[0] | |
assert image.shape == (512, 512, 3) | |
assert np.abs(expected_image - image).max() < 1e-3 | |
def test_stable_diffusion_upscale_pipeline_fp16(self): | |
image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/sd2-upscale/low_res_cat.png" | |
) | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale" | |
"/upsampled_cat_fp16.npy" | |
) | |
model_id = "stabilityai/stable-diffusion-x4-upscaler" | |
pipe = StableDiffusionUpscalePipeline.from_pretrained( | |
model_id, | |
torch_dtype=torch.float16, | |
) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
prompt = "a cat sitting on a park bench" | |
generator = torch.manual_seed(0) | |
output = pipe( | |
prompt=prompt, | |
image=image, | |
generator=generator, | |
output_type="np", | |
) | |
image = output.images[0] | |
assert image.shape == (512, 512, 3) | |
assert np.abs(expected_image - image).max() < 5e-1 | |
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self): | |
torch.cuda.empty_cache() | |
torch.cuda.reset_max_memory_allocated() | |
torch.cuda.reset_peak_memory_stats() | |
image = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
"/sd2-upscale/low_res_cat.png" | |
) | |
model_id = "stabilityai/stable-diffusion-x4-upscaler" | |
pipe = StableDiffusionUpscalePipeline.from_pretrained( | |
model_id, | |
torch_dtype=torch.float16, | |
) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing(1) | |
pipe.enable_sequential_cpu_offload() | |
prompt = "a cat sitting on a park bench" | |
generator = torch.manual_seed(0) | |
_ = pipe( | |
prompt=prompt, | |
image=image, | |
generator=generator, | |
num_inference_steps=5, | |
output_type="np", | |
) | |
mem_bytes = torch.cuda.max_memory_allocated() | |
# make sure that less than 2.9 GB is allocated | |
assert mem_bytes < 2.9 * 10**9 | |