Spaces:
Runtime error
Runtime error
tango2-full
/
diffusers
/tests
/pipelines
/stable_diffusion
/test_stable_diffusion_inpaint_legacy.py
# 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, | |
DPMSolverMultistepScheduler, | |
LMSDiscreteScheduler, | |
PNDMScheduler, | |
StableDiffusionInpaintPipelineLegacy, | |
UNet2DConditionModel, | |
UNet2DModel, | |
VQModel, | |
) | |
from diffusers.utils import floats_tensor, load_image, nightly, slow, torch_device | |
from diffusers.utils.testing_utils import load_numpy, require_torch_gpu | |
torch.backends.cuda.matmul.allow_tf32 = False | |
class StableDiffusionInpaintLegacyPipelineFastTests(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_uncond_unet(self): | |
torch.manual_seed(0) | |
model = UNet2DModel( | |
block_out_channels=(32, 64), | |
layers_per_block=2, | |
sample_size=32, | |
in_channels=3, | |
out_channels=3, | |
down_block_types=("DownBlock2D", "AttnDownBlock2D"), | |
up_block_types=("AttnUpBlock2D", "UpBlock2D"), | |
) | |
return model | |
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_cond_unet_inpaint(self): | |
torch.manual_seed(0) | |
model = 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, | |
) | |
return model | |
def dummy_vq_model(self): | |
torch.manual_seed(0) | |
model = VQModel( | |
block_out_channels=[32, 64], | |
in_channels=3, | |
out_channels=3, | |
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
latent_channels=3, | |
) | |
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 = 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, | |
) | |
return CLIPTextModel(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_inpaint_legacy(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 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] | |
init_image = Image.fromarray(np.uint8(image)).convert("RGB") | |
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((32, 32)) | |
# make sure here that pndm scheduler skips prk | |
sd_pipe = StableDiffusionInpaintPipelineLegacy( | |
unet=unet, | |
scheduler=scheduler, | |
vae=vae, | |
text_encoder=bert, | |
tokenizer=tokenizer, | |
safety_checker=None, | |
feature_extractor=self.dummy_extractor, | |
) | |
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], | |
generator=generator, | |
guidance_scale=6.0, | |
num_inference_steps=2, | |
output_type="np", | |
image=init_image, | |
mask_image=mask_image, | |
) | |
image = output.images | |
generator = torch.Generator(device=device).manual_seed(0) | |
image_from_tuple = sd_pipe( | |
[prompt], | |
generator=generator, | |
guidance_scale=6.0, | |
num_inference_steps=2, | |
output_type="np", | |
image=init_image, | |
mask_image=mask_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([0.4941, 0.5396, 0.4689, 0.6338, 0.5392, 0.4094, 0.5477, 0.5904, 0.5165]) | |
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_inpaint_legacy_negative_prompt(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 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] | |
init_image = Image.fromarray(np.uint8(image)).convert("RGB") | |
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((32, 32)) | |
# make sure here that pndm scheduler skips prk | |
sd_pipe = StableDiffusionInpaintPipelineLegacy( | |
unet=unet, | |
scheduler=scheduler, | |
vae=vae, | |
text_encoder=bert, | |
tokenizer=tokenizer, | |
safety_checker=None, | |
feature_extractor=self.dummy_extractor, | |
) | |
sd_pipe = sd_pipe.to(device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
prompt = "A painting of a squirrel eating a burger" | |
negative_prompt = "french fries" | |
generator = torch.Generator(device=device).manual_seed(0) | |
output = sd_pipe( | |
prompt, | |
negative_prompt=negative_prompt, | |
generator=generator, | |
guidance_scale=6.0, | |
num_inference_steps=2, | |
output_type="np", | |
image=init_image, | |
mask_image=mask_image, | |
) | |
image = output.images | |
image_slice = image[0, -3:, -3:, -1] | |
assert image.shape == (1, 32, 32, 3) | |
expected_slice = np.array([0.4941, 0.5396, 0.4689, 0.6338, 0.5392, 0.4094, 0.5477, 0.5904, 0.5165]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_inpaint_legacy_num_images_per_prompt(self): | |
device = "cpu" | |
unet = self.dummy_cond_unet | |
scheduler = PNDMScheduler(skip_prk_steps=True) | |
vae = self.dummy_vae | |
bert = 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] | |
init_image = Image.fromarray(np.uint8(image)).convert("RGB") | |
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((32, 32)) | |
# make sure here that pndm scheduler skips prk | |
sd_pipe = StableDiffusionInpaintPipelineLegacy( | |
unet=unet, | |
scheduler=scheduler, | |
vae=vae, | |
text_encoder=bert, | |
tokenizer=tokenizer, | |
safety_checker=None, | |
feature_extractor=self.dummy_extractor, | |
) | |
sd_pipe = sd_pipe.to(device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
prompt = "A painting of a squirrel eating a burger" | |
# test num_images_per_prompt=1 (default) | |
images = sd_pipe( | |
prompt, | |
num_inference_steps=2, | |
output_type="np", | |
image=init_image, | |
mask_image=mask_image, | |
).images | |
assert images.shape == (1, 32, 32, 3) | |
# test num_images_per_prompt=1 (default) for batch of prompts | |
batch_size = 2 | |
images = sd_pipe( | |
[prompt] * batch_size, | |
num_inference_steps=2, | |
output_type="np", | |
image=init_image, | |
mask_image=mask_image, | |
).images | |
assert images.shape == (batch_size, 32, 32, 3) | |
# test num_images_per_prompt for single prompt | |
num_images_per_prompt = 2 | |
images = sd_pipe( | |
prompt, | |
num_inference_steps=2, | |
output_type="np", | |
image=init_image, | |
mask_image=mask_image, | |
num_images_per_prompt=num_images_per_prompt, | |
).images | |
assert images.shape == (num_images_per_prompt, 32, 32, 3) | |
# test num_images_per_prompt for batch of prompts | |
batch_size = 2 | |
images = sd_pipe( | |
[prompt] * batch_size, | |
num_inference_steps=2, | |
output_type="np", | |
image=init_image, | |
mask_image=mask_image, | |
num_images_per_prompt=num_images_per_prompt, | |
).images | |
assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3) | |
class StableDiffusionInpaintLegacyPipelineSlowTests(unittest.TestCase): | |
def tearDown(self): | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): | |
generator = torch.Generator(device=generator_device).manual_seed(seed) | |
init_image = load_image( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_inpaint/input_bench_image.png" | |
) | |
mask_image = load_image( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_inpaint/input_bench_mask.png" | |
) | |
inputs = { | |
"prompt": "A red cat sitting on a park bench", | |
"image": init_image, | |
"mask_image": mask_image, | |
"generator": generator, | |
"num_inference_steps": 3, | |
"strength": 0.75, | |
"guidance_scale": 7.5, | |
"output_type": "numpy", | |
} | |
return inputs | |
def test_stable_diffusion_inpaint_legacy_pndm(self): | |
pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained( | |
"CompVis/stable-diffusion-v1-4", safety_checker=None | |
) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
inputs = self.get_inputs(torch_device) | |
image = pipe(**inputs).images | |
image_slice = image[0, 253:256, 253:256, -1].flatten() | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.5665, 0.6117, 0.6430, 0.4057, 0.4594, 0.5658, 0.1596, 0.3106, 0.4305]) | |
assert np.abs(expected_slice - image_slice).max() < 1e-4 | |
def test_stable_diffusion_inpaint_legacy_k_lms(self): | |
pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained( | |
"CompVis/stable-diffusion-v1-4", safety_checker=None | |
) | |
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
inputs = self.get_inputs(torch_device) | |
image = pipe(**inputs).images | |
image_slice = image[0, 253:256, 253:256, -1].flatten() | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.4534, 0.4467, 0.4329, 0.4329, 0.4339, 0.4220, 0.4244, 0.4332, 0.4426]) | |
assert np.abs(expected_slice - image_slice).max() < 1e-4 | |
def test_stable_diffusion_inpaint_legacy_intermediate_state(self): | |
number_of_steps = 0 | |
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None: | |
callback_fn.has_been_called = True | |
nonlocal number_of_steps | |
number_of_steps += 1 | |
if step == 1: | |
latents = latents.detach().cpu().numpy() | |
assert latents.shape == (1, 4, 64, 64) | |
latents_slice = latents[0, -3:, -3:, -1] | |
expected_slice = np.array([0.5977, 1.5449, 1.0586, -0.3250, 0.7383, -0.0862, 0.4631, -0.2571, -1.1289]) | |
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3 | |
elif step == 2: | |
latents = latents.detach().cpu().numpy() | |
assert latents.shape == (1, 4, 64, 64) | |
latents_slice = latents[0, -3:, -3:, -1] | |
expected_slice = np.array([0.5190, 1.1621, 0.6885, 0.2424, 0.3337, -0.1617, 0.6914, -0.1957, -0.5474]) | |
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3 | |
callback_fn.has_been_called = False | |
pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained( | |
"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16 | |
) | |
pipe = pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
inputs = self.get_inputs(torch_device, dtype=torch.float16) | |
pipe(**inputs, callback=callback_fn, callback_steps=1) | |
assert callback_fn.has_been_called | |
assert number_of_steps == 2 | |
class StableDiffusionInpaintLegacyPipelineNightlyTests(unittest.TestCase): | |
def tearDown(self): | |
super().tearDown() | |
gc.collect() | |
torch.cuda.empty_cache() | |
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): | |
generator = torch.Generator(device=generator_device).manual_seed(seed) | |
init_image = load_image( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_inpaint/input_bench_image.png" | |
) | |
mask_image = load_image( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_inpaint/input_bench_mask.png" | |
) | |
inputs = { | |
"prompt": "A red cat sitting on a park bench", | |
"image": init_image, | |
"mask_image": mask_image, | |
"generator": generator, | |
"num_inference_steps": 50, | |
"strength": 0.75, | |
"guidance_scale": 7.5, | |
"output_type": "numpy", | |
} | |
return inputs | |
def test_inpaint_pndm(self): | |
sd_pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained("runwayml/stable-diffusion-v1-5") | |
sd_pipe.to(torch_device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
image = sd_pipe(**inputs).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_inpaint_legacy/stable_diffusion_1_5_pndm.npy" | |
) | |
max_diff = np.abs(expected_image - image).max() | |
assert max_diff < 1e-3 | |
def test_inpaint_ddim(self): | |
sd_pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained("runwayml/stable-diffusion-v1-5") | |
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config) | |
sd_pipe.to(torch_device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
image = sd_pipe(**inputs).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_inpaint_legacy/stable_diffusion_1_5_ddim.npy" | |
) | |
max_diff = np.abs(expected_image - image).max() | |
assert max_diff < 1e-3 | |
def test_inpaint_lms(self): | |
sd_pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained("runwayml/stable-diffusion-v1-5") | |
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config) | |
sd_pipe.to(torch_device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
image = sd_pipe(**inputs).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_inpaint_legacy/stable_diffusion_1_5_lms.npy" | |
) | |
max_diff = np.abs(expected_image - image).max() | |
assert max_diff < 1e-3 | |
def test_inpaint_dpm(self): | |
sd_pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained("runwayml/stable-diffusion-v1-5") | |
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config) | |
sd_pipe.to(torch_device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
inputs["num_inference_steps"] = 30 | |
image = sd_pipe(**inputs).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_inpaint_legacy/stable_diffusion_1_5_dpm_multi.npy" | |
) | |
max_diff = np.abs(expected_image - image).max() | |
assert max_diff < 1e-3 | |