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import gc
import tempfile
import unittest
import torch
from diffusers import (
StableDiffusionXLAdapterPipeline,
T2IAdapter,
)
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
)
from .single_file_testing_utils import (
SDXLSingleFileTesterMixin,
download_diffusers_config,
download_original_config,
download_single_file_checkpoint,
)
enable_full_determinism()
@slow
@require_torch_gpu
class StableDiffusionXLAdapterPipelineSingleFileSlowTests(unittest.TestCase, SDXLSingleFileTesterMixin):
pipeline_class = StableDiffusionXLAdapterPipeline
ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors"
repo_id = "stabilityai/stable-diffusion-xl-base-1.0"
original_config = (
"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
)
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self):
prompt = "toy"
generator = torch.Generator(device="cpu").manual_seed(0)
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png"
)
inputs = {
"prompt": prompt,
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_single_file_format_inference_is_same_as_pretrained(self):
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16)
pipe_single_file = StableDiffusionXLAdapterPipeline.from_single_file(
self.ckpt_path,
adapter=adapter,
torch_dtype=torch.float16,
safety_checker=None,
)
pipe_single_file.enable_model_cpu_offload()
pipe_single_file.set_progress_bar_config(disable=None)
inputs = self.get_inputs()
images_single_file = pipe_single_file(**inputs).images[0]
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
self.repo_id,
adapter=adapter,
torch_dtype=torch.float16,
safety_checker=None,
)
pipe.enable_model_cpu_offload()
inputs = self.get_inputs()
images = pipe(**inputs).images[0]
assert images_single_file.shape == (768, 512, 3)
assert images.shape == (768, 512, 3)
max_diff = numpy_cosine_similarity_distance(images.flatten(), images_single_file.flatten())
assert max_diff < 5e-3
def test_single_file_components(self):
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
variant="fp16",
adapter=adapter,
torch_dtype=torch.float16,
)
pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, safety_checker=None, adapter=adapter)
super().test_single_file_components(pipe, pipe_single_file)
def test_single_file_components_local_files_only(self):
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
variant="fp16",
adapter=adapter,
torch_dtype=torch.float16,
)
with tempfile.TemporaryDirectory() as tmpdir:
ckpt_filename = self.ckpt_path.split("/")[-1]
local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)
single_file_pipe = self.pipeline_class.from_single_file(
local_ckpt_path, adapter=adapter, safety_checker=None, local_files_only=True
)
self._compare_component_configs(pipe, single_file_pipe)
def test_single_file_components_with_diffusers_config(self):
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
variant="fp16",
adapter=adapter,
torch_dtype=torch.float16,
safety_checker=None,
)
pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, config=self.repo_id, adapter=adapter)
self._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_diffusers_config_local_files_only(self):
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
variant="fp16",
adapter=adapter,
torch_dtype=torch.float16,
)
with tempfile.TemporaryDirectory() as tmpdir:
ckpt_filename = self.ckpt_path.split("/")[-1]
local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)
local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir)
pipe_single_file = self.pipeline_class.from_single_file(
local_ckpt_path,
config=local_diffusers_config,
adapter=adapter,
safety_checker=None,
local_files_only=True,
)
self._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_original_config(self):
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
variant="fp16",
adapter=adapter,
torch_dtype=torch.float16,
safety_checker=None,
)
pipe_single_file = self.pipeline_class.from_single_file(
self.ckpt_path, original_config=self.original_config, adapter=adapter
)
self._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_original_config_local_files_only(self):
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
variant="fp16",
adapter=adapter,
torch_dtype=torch.float16,
)
with tempfile.TemporaryDirectory() as tmpdir:
ckpt_filename = self.ckpt_path.split("/")[-1]
local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)
local_original_config = download_original_config(self.original_config, tmpdir)
pipe_single_file = self.pipeline_class.from_single_file(
local_ckpt_path,
original_config=local_original_config,
adapter=adapter,
safety_checker=None,
local_files_only=True,
)
self._compare_component_configs(pipe, pipe_single_file)
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