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test_stable_diffusion_xl_controlnet_single_file.py
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import gc
import tempfile
import unittest
import torch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_accelerator,
slow,
torch_device,
)
from .single_file_testing_utils import (
SDXLSingleFileTesterMixin,
download_diffusers_config,
download_single_file_checkpoint,
)
enable_full_determinism()
@slow
@require_torch_accelerator
class StableDiffusionXLControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDXLSingleFileTesterMixin):
pipeline_class = StableDiffusionXLControlNetPipeline
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()
backend_empty_cache(torch_device)
def tearDown(self):
super().tearDown()
gc.collect()
backend_empty_cache(torch_device)
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png"
)
inputs = {
"prompt": "Stormtrooper's lecture",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_single_file_format_inference_is_same_as_pretrained(self):
controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16)
pipe_single_file = self.pipeline_class.from_single_file(
self.ckpt_path, controlnet=controlnet, torch_dtype=torch.float16
)
pipe_single_file.unet.set_default_attn_processor()
pipe_single_file.enable_model_cpu_offload(device=torch_device)
pipe_single_file.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
single_file_images = pipe_single_file(**inputs).images[0]
pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet, torch_dtype=torch.float16)
pipe.unet.set_default_attn_processor()
pipe.enable_model_cpu_offload(device=torch_device)
inputs = self.get_inputs(torch_device)
images = pipe(**inputs).images[0]
assert images.shape == (512, 512, 3)
assert single_file_images.shape == (512, 512, 3)
max_diff = numpy_cosine_similarity_distance(images[0].flatten(), single_file_images[0].flatten())
assert max_diff < 5e-2
def test_single_file_components(self):
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
variant="fp16",
controlnet=controlnet,
torch_dtype=torch.float16,
)
pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, controlnet=controlnet)
super().test_single_file_components(pipe, pipe_single_file)
def test_single_file_components_local_files_only(self):
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
variant="fp16",
controlnet=controlnet,
torch_dtype=torch.float16,
)
with tempfile.TemporaryDirectory() as tmpdir:
repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path)
local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir)
single_file_pipe = self.pipeline_class.from_single_file(
local_ckpt_path, controlnet=controlnet, safety_checker=None, local_files_only=True
)
self._compare_component_configs(pipe, single_file_pipe)
def test_single_file_components_with_original_config(self):
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
variant="fp16",
controlnet=controlnet,
torch_dtype=torch.float16,
)
pipe_single_file = self.pipeline_class.from_single_file(
self.ckpt_path,
original_config=self.original_config,
controlnet=controlnet,
)
self._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_original_config_local_files_only(self):
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
variant="fp16",
controlnet=controlnet,
torch_dtype=torch.float16,
)
with tempfile.TemporaryDirectory() as tmpdir:
repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path)
local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir)
pipe_single_file = self.pipeline_class.from_single_file(
local_ckpt_path,
safety_checker=None,
controlnet=controlnet,
local_files_only=True,
)
self._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_diffusers_config(self):
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
)
pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)
pipe_single_file = self.pipeline_class.from_single_file(
self.ckpt_path, controlnet=controlnet, config=self.repo_id
)
super()._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_diffusers_config_local_files_only(self):
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
controlnet=controlnet,
)
with tempfile.TemporaryDirectory() as tmpdir:
repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path)
local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, 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,
safety_checker=None,
controlnet=controlnet,
local_files_only=True,
)
super()._compare_component_configs(pipe, pipe_single_file)
def test_single_file_setting_pipeline_dtype_to_fp16(self):
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
)
single_file_pipe = self.pipeline_class.from_single_file(
self.ckpt_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
)
super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe)