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Dhruv Nair
2024-01-02 10:44:24 +00:00
parent e945e18637
commit fa3a0d6634
3 changed files with 97 additions and 9 deletions
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44
tests/pipelines/controlnet/test_controlnet_sdxl.py
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@@ -31,7 +31,14 @@ from diffusers import (
from diffusers.models.unet_2d_blocks import UNetMidBlock2D
from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, slow, torch_device
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_image,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from diffusers.utils.torch_utils import randn_tensor
from ..pipeline_params import (
@@ -819,6 +826,41 @@ class ControlNetSDXLPipelineSlowTests(unittest.TestCase):
expected_image = np.array([0.4399, 0.5112, 0.5478, 0.4314, 0.472, 0.4823, 0.4647, 0.4957, 0.4853])
assert np.allclose(original_image, expected_image, atol=1e-04)
def test_download_ckpt_diff_format_is_same(self):
controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0")
single_file_url = (
"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors"
)
pipe_single_file = StableDiffusionXLControlNetPipeline.from_single_file(
single_file_url, controlnet=controlnet, torch_dtype=torch.float16
)
pipe_single_file.unet.set_default_attn_processor()
pipe_single_file.enable_model_cpu_offload()
pipe_single_file.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "Stormtrooper's lecture"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png"
)
single_file_images = pipe_single_file(
prompt, image=image, generator=generator, output_type="np", num_inference_steps=3
).images
generator = torch.Generator(device="cpu").manual_seed(0)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.unet.set_default_attn_processor()
pipe.enable_model_cpu_offload()
images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images
assert images[0].shape == (512, 512, 3)
assert single_file_images[0].shape == (512, 512, 3)
max_diff = numpy_cosine_similarity_distance(images[0].flatten(), single_file_images[0].flatten())
assert max_diff < 1e-4
class StableDiffusionSSD1BControlNetPipelineFastTests(StableDiffusionXLControlNetPipelineFastTests):
def test_controlnet_sdxl_guess(self):

20
tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py
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@@ -1051,24 +1051,28 @@ class StableDiffusionXLPipelineIntegrationTests(unittest.TestCase):
assert max_diff < 1e-2
def test_download_ckpt_diff_format_is_same(self):
ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt"
ckpt_path = (
"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors"
)
pipe = StableDiffusionXLPipeline.from_single_file(ckpt_path)
pipe = StableDiffusionXLPipeline.from_single_file(ckpt_path, torch_dtype=torch.float16)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_attn_processor(AttnProcessor())
pipe.to("cuda")
pipe.unet.set_default_attn_processor()
pipe.enable_model_cpu_offload()
generator = torch.Generator(device="cpu").manual_seed(0)
image_ckpt = pipe("a turtle", num_inference_steps=2, generator=generator, output_type="np").images[0]
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_attn_processor(AttnProcessor())
pipe.to("cuda")
pipe.unet.set_default_attn_processor()
pipe.enable_model_cpu_offload()
generator = torch.Generator(device="cpu").manual_seed(0)
image = pipe("a turtle", num_inference_steps=2, generator=generator, output_type="np").images[0]
max_diff = numpy_cosine_similarity_distance(image.flatten(), image_ckpt.flatten())
assert max_diff < 1e-3
assert max_diff < 6e-3

42
tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py
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@@ -13,6 +13,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import unittest
@@ -31,15 +32,19 @@ from transformers import (
from diffusers import (
AutoencoderKL,
AutoencoderTiny,
DDIMScheduler,
EulerDiscreteScheduler,
LCMScheduler,
StableDiffusionXLImg2ImgPipeline,
UNet2DConditionModel,
)
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
@@ -763,3 +768,40 @@ class StableDiffusionXLImg2ImgRefinerOnlyPipelineFastTests(
def test_save_load_optional_components(self):
self._test_save_load_optional_components()
@slow
class StableDiffusionXLImg2ImgIntegrationTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_download_ckpt_diff_format_is_same(self):
ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/sd_xl_refiner_1.0.safetensors"
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/sketch-mountains-input.png"
)
pipe = StableDiffusionXLImg2ImgPipeline.from_single_file(ckpt_path)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
generator = torch.Generator(device="cpu").manual_seed(0)
image_ckpt = pipe(
"mountains", image=init_image, num_inference_steps=2, generator=generator, output_type="np"
).images[0]
pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
generator = torch.Generator(device="cpu").manual_seed(0)
image = pipe(
"mountains", image=init_image, num_inference_steps=2, generator=generator, output_type="np"
).images[0]
max_diff = numpy_cosine_similarity_distance(image.flatten(), image_ckpt.flatten())
assert max_diff < 1e-3