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Update Cascade Tests (#7324)

* update

* update

* update
This commit is contained in:
Dhruv Nair
2024-03-14 20:51:22 +05:30
committed by GitHub
parent 83062fb872
commit 4974b84564
3 changed files with 25 additions and 33 deletions

View File

@@ -50,9 +50,7 @@ class StableCascadeUNetModelSlowTests(unittest.TestCase):
gc.collect()
torch.cuda.empty_cache()
unet = StableCascadeUNet.from_pretrained(
"stabilityai/stable-cascade-prior", subfolder="prior", revision="refs/pr/2", variant="bf16"
)
unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade-prior", subfolder="prior", variant="bf16")
unet_config = unet.config
del unet
gc.collect()
@@ -74,9 +72,7 @@ class StableCascadeUNetModelSlowTests(unittest.TestCase):
gc.collect()
torch.cuda.empty_cache()
unet = StableCascadeUNet.from_pretrained(
"stabilityai/stable-cascade", subfolder="decoder", revision="refs/pr/44", variant="bf16"
)
unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade", subfolder="decoder", variant="bf16")
unet_config = unet.config
del unet
gc.collect()

View File

@@ -21,13 +21,13 @@ import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import DDPMWuerstchenScheduler, StableCascadeDecoderPipeline
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import StableCascadeUNet
from diffusers.pipelines.wuerstchen import PaellaVQModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_image,
load_numpy,
load_pt,
numpy_cosine_similarity_distance,
require_torch_gpu,
skip_mps,
slow,
@@ -258,7 +258,7 @@ class StableCascadeDecoderPipelineIntegrationTests(unittest.TestCase):
def test_stable_cascade_decoder(self):
pipe = StableCascadeDecoderPipeline.from_pretrained(
"diffusers/StableCascade-decoder", torch_dtype=torch.bfloat16
"stabilityai/stable-cascade", variant="bf16", torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
@@ -271,18 +271,16 @@ class StableCascadeDecoderPipelineIntegrationTests(unittest.TestCase):
)
image = pipe(
prompt=prompt, image_embeddings=image_embedding, num_inference_steps=10, generator=generator
prompt=prompt,
image_embeddings=image_embedding,
output_type="np",
num_inference_steps=2,
generator=generator,
).images[0]
assert image.size == (1024, 1024)
expected_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/t2i.png"
assert image.shape == (1024, 1024, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/stable_cascade_decoder_image.npy"
)
image_processor = VaeImageProcessor()
image_np = image_processor.pil_to_numpy(image)
expected_image_np = image_processor.pil_to_numpy(expected_image)
self.assertTrue(np.allclose(image_np, expected_image_np, atol=53e-2))
max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
assert max_diff < 1e-4

View File

@@ -29,7 +29,8 @@ from diffusers.models.attention_processor import LoRAAttnProcessor, LoRAAttnProc
from diffusers.utils.import_utils import is_peft_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_pt,
load_numpy,
numpy_cosine_similarity_distance,
require_peft_backend,
require_torch_gpu,
skip_mps,
@@ -319,7 +320,9 @@ class StableCascadePriorPipelineIntegrationTests(unittest.TestCase):
torch.cuda.empty_cache()
def test_stable_cascade_prior(self):
pipe = StableCascadePriorPipeline.from_pretrained("diffusers/StableCascade-prior", torch_dtype=torch.bfloat16)
pipe = StableCascadePriorPipeline.from_pretrained(
"stabilityai/stable-cascade-prior", variant="bf16", torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
@@ -327,17 +330,12 @@ class StableCascadePriorPipelineIntegrationTests(unittest.TestCase):
generator = torch.Generator(device="cpu").manual_seed(0)
output = pipe(prompt, num_inference_steps=10, generator=generator)
output = pipe(prompt, num_inference_steps=2, output_type="np", generator=generator)
image_embedding = output.image_embeddings
expected_image_embedding = load_pt(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/image_embedding.pt"
expected_image_embedding = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/stable_cascade_prior_image_embeddings.npy"
)
assert image_embedding.shape == (1, 16, 24, 24)
self.assertTrue(
np.allclose(
image_embedding.cpu().float().numpy(), expected_image_embedding.cpu().float().numpy(), atol=5e-2
)
)
max_diff = numpy_cosine_similarity_distance(image_embedding.flatten(), expected_image_embedding.flatten())
assert max_diff < 1e-4