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import gc
import tempfile
import unittest
import torch
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
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 (
SDSingleFileTesterMixin,
download_diffusers_config,
download_original_config,
download_single_file_checkpoint,
)
enable_full_determinism()
@slow
@require_torch_accelerator
class StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
pipeline_class = StableDiffusionControlNetPipeline
ckpt_path = (
"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
)
original_config = (
"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
)
repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
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):
control_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
).resize((512, 512))
inputs = {
"prompt": "bird",
"image": control_image,
"generator": torch.Generator(device="cpu").manual_seed(0),
"num_inference_steps": 3,
"output_type": "np",
}
return inputs
def test_single_file_format_inference_is_same_as_pretrained(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)
pipe.unet.set_default_attn_processor()
pipe.enable_model_cpu_offload(device=torch_device)
pipe_sf = self.pipeline_class.from_single_file(
self.ckpt_path,
controlnet=controlnet,
)
pipe_sf.unet.set_default_attn_processor()
pipe_sf.enable_model_cpu_offload(device=torch_device)
inputs = self.get_inputs()
output = pipe(**inputs).images[0]
inputs = self.get_inputs()
output_sf = pipe_sf(**inputs).images[0]
max_diff = numpy_cosine_similarity_distance(output_sf.flatten(), output.flatten())
assert max_diff < 1e-3
def test_single_file_components(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe = self.pipeline_class.from_pretrained(
self.repo_id, variant="fp16", safety_checker=None, controlnet=controlnet
)
pipe_single_file = self.pipeline_class.from_single_file(
self.ckpt_path,
safety_checker=None,
controlnet=controlnet,
)
super()._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_local_files_only(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
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)
pipe_single_file = self.pipeline_class.from_single_file(
local_ckpt_path, controlnet=controlnet, local_files_only=True
)
super()._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_original_config(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", 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, original_config=self.original_config
)
super()._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_original_config_local_files_only(self):
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_canny", 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_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, controlnet=controlnet, local_files_only=True
)
pipe_single_file.scheduler = pipe.scheduler
super()._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_diffusers_config(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", 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, safety_checker=None, 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(
"lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16"
)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
controlnet=controlnet,
safety_checker=None,
)
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,
controlnet=controlnet,
safety_checker=None,
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(
"lllyasviel/control_v11p_sd15_canny", 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)
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