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# coding=utf-8
# Copyright 2024 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 unittest
import torch
from diffusers import StableCascadeUNet
from diffusers.utils import logging
from diffusers.utils.testing_utils import (
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
)
from diffusers.utils.torch_utils import randn_tensor
logger = logging.get_logger(__name__)
enable_full_determinism()
@slow
class StableCascadeUNetModelSlowTests(unittest.TestCase):
def tearDown(self) -> None:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_cascade_unet_prior_single_file_components(self):
single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors"
single_file_unet = StableCascadeUNet.from_single_file(single_file_url)
single_file_unet_config = single_file_unet.config
del single_file_unet
gc.collect()
torch.cuda.empty_cache()
unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade-prior", subfolder="prior", variant="bf16")
unet_config = unet.config
del unet
gc.collect()
torch.cuda.empty_cache()
PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
for param_name, param_value in single_file_unet_config.items():
if param_name in PARAMS_TO_IGNORE:
continue
assert unet_config[param_name] == param_value
def test_stable_cascade_unet_decoder_single_file_components(self):
single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_bf16.safetensors"
single_file_unet = StableCascadeUNet.from_single_file(single_file_url)
single_file_unet_config = single_file_unet.config
del single_file_unet
gc.collect()
torch.cuda.empty_cache()
unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade", subfolder="decoder", variant="bf16")
unet_config = unet.config
del unet
gc.collect()
torch.cuda.empty_cache()
PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
for param_name, param_value in single_file_unet_config.items():
if param_name in PARAMS_TO_IGNORE:
continue
assert unet_config[param_name] == param_value
def test_stable_cascade_unet_config_loading(self):
config = StableCascadeUNet.load_config(
pretrained_model_name_or_path="diffusers/stable-cascade-configs", subfolder="prior"
)
single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors"
single_file_unet = StableCascadeUNet.from_single_file(single_file_url, config=config)
single_file_unet_config = single_file_unet.config
del single_file_unet
gc.collect()
torch.cuda.empty_cache()
PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
for param_name, param_value in config.items():
if param_name in PARAMS_TO_IGNORE:
continue
assert single_file_unet_config[param_name] == param_value
@require_torch_gpu
def test_stable_cascade_unet_single_file_prior_forward_pass(self):
dtype = torch.bfloat16
generator = torch.Generator("cpu")
model_inputs = {
"sample": randn_tensor((1, 16, 24, 24), generator=generator.manual_seed(0)).to("cuda", dtype),
"timestep_ratio": torch.tensor([1]).to("cuda", dtype),
"clip_text_pooled": randn_tensor((1, 1, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
"clip_text": randn_tensor((1, 77, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
"clip_img": randn_tensor((1, 1, 768), generator=generator.manual_seed(0)).to("cuda", dtype),
"pixels": randn_tensor((1, 3, 8, 8), generator=generator.manual_seed(0)).to("cuda", dtype),
}
unet = StableCascadeUNet.from_pretrained(
"stabilityai/stable-cascade-prior",
subfolder="prior",
revision="refs/pr/2",
variant="bf16",
torch_dtype=dtype,
)
unet.to("cuda")
with torch.no_grad():
prior_output = unet(**model_inputs).sample.float().cpu().numpy()
# Remove UNet from GPU memory before loading the single file UNet model
del unet
gc.collect()
torch.cuda.empty_cache()
single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors"
single_file_unet = StableCascadeUNet.from_single_file(single_file_url, torch_dtype=dtype)
single_file_unet.to("cuda")
with torch.no_grad():
prior_single_file_output = single_file_unet(**model_inputs).sample.float().cpu().numpy()
# Remove UNet from GPU memory before loading the single file UNet model
del single_file_unet
gc.collect()
torch.cuda.empty_cache()
max_diff = numpy_cosine_similarity_distance(prior_output.flatten(), prior_single_file_output.flatten())
assert max_diff < 8e-3
@require_torch_gpu
def test_stable_cascade_unet_single_file_decoder_forward_pass(self):
dtype = torch.float32
generator = torch.Generator("cpu")
model_inputs = {
"sample": randn_tensor((1, 4, 256, 256), generator=generator.manual_seed(0)).to("cuda", dtype),
"timestep_ratio": torch.tensor([1]).to("cuda", dtype),
"clip_text": randn_tensor((1, 77, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
"clip_text_pooled": randn_tensor((1, 1, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
"pixels": randn_tensor((1, 3, 8, 8), generator=generator.manual_seed(0)).to("cuda", dtype),
}
unet = StableCascadeUNet.from_pretrained(
"stabilityai/stable-cascade",
subfolder="decoder",
revision="refs/pr/44",
torch_dtype=dtype,
)
unet.to("cuda")
with torch.no_grad():
prior_output = unet(**model_inputs).sample.float().cpu().numpy()
# Remove UNet from GPU memory before loading the single file UNet model
del unet
gc.collect()
torch.cuda.empty_cache()
single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b.safetensors"
single_file_unet = StableCascadeUNet.from_single_file(single_file_url, torch_dtype=dtype)
single_file_unet.to("cuda")
with torch.no_grad():
prior_single_file_output = single_file_unet(**model_inputs).sample.float().cpu().numpy()
# Remove UNet from GPU memory before loading the single file UNet model
del single_file_unet
gc.collect()
torch.cuda.empty_cache()
max_diff = numpy_cosine_similarity_distance(prior_output.flatten(), prior_single_file_output.flatten())
assert max_diff < 1e-4
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