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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 unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import AmusedImg2ImgPipeline, AmusedScheduler, UVit2DModel, VQModel
from diffusers.utils import load_image
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class AmusedImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = AmusedImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "latents"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params - {
"latents",
}
def get_dummy_components(self):
torch.manual_seed(0)
transformer = UVit2DModel(
hidden_size=8,
use_bias=False,
hidden_dropout=0.0,
cond_embed_dim=8,
micro_cond_encode_dim=2,
micro_cond_embed_dim=10,
encoder_hidden_size=8,
vocab_size=32,
codebook_size=8,
in_channels=8,
block_out_channels=8,
num_res_blocks=1,
downsample=True,
upsample=True,
block_num_heads=1,
num_hidden_layers=1,
num_attention_heads=1,
attention_dropout=0.0,
intermediate_size=8,
layer_norm_eps=1e-06,
ln_elementwise_affine=True,
)
scheduler = AmusedScheduler(mask_token_id=31)
torch.manual_seed(0)
vqvae = VQModel(
act_fn="silu",
block_out_channels=[8],
down_block_types=[
"DownEncoderBlock2D",
],
in_channels=3,
latent_channels=8,
layers_per_block=1,
norm_num_groups=8,
num_vq_embeddings=32, # reducing this to 16 or 8 -> RuntimeError: "cdist_cuda" not implemented for 'Half'
out_channels=3,
sample_size=8,
up_block_types=[
"UpDecoderBlock2D",
],
mid_block_add_attention=False,
lookup_from_codebook=True,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=8,
intermediate_size=8,
layer_norm_eps=1e-05,
num_attention_heads=1,
num_hidden_layers=1,
pad_token_id=1,
vocab_size=1000,
projection_dim=8,
)
text_encoder = CLIPTextModelWithProjection(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"transformer": transformer,
"scheduler": scheduler,
"vqvae": vqvae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
image = torch.full((1, 3, 4, 4), 1.0, dtype=torch.float32, device=device)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
"image": image,
}
return inputs
def test_inference_batch_consistent(self, batch_sizes=[2]):
self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False)
@unittest.skip("aMUSEd does not support lists of generators")
def test_inference_batch_single_identical(self):
...
@slow
@require_torch_gpu
class AmusedImg2ImgPipelineSlowTests(unittest.TestCase):
def test_amused_256(self):
pipe = AmusedImg2ImgPipeline.from_pretrained("amused/amused-256")
pipe.to(torch_device)
image = (
load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg")
.resize((256, 256))
.convert("RGB")
)
image = pipe(
"winter mountains",
image,
generator=torch.Generator().manual_seed(0),
num_inference_steps=2,
output_type="np",
).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.9993, 1.0, 0.9996, 1.0, 0.9995, 0.9925, 0.9990, 0.9954, 1.0])
assert np.abs(image_slice - expected_slice).max() < 1e-2
def test_amused_256_fp16(self):
pipe = AmusedImg2ImgPipeline.from_pretrained("amused/amused-256", torch_dtype=torch.float16, variant="fp16")
pipe.to(torch_device)
image = (
load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg")
.resize((256, 256))
.convert("RGB")
)
image = pipe(
"winter mountains",
image,
generator=torch.Generator().manual_seed(0),
num_inference_steps=2,
output_type="np",
).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.9980, 0.9980, 0.9940, 0.9944, 0.9960, 0.9908, 1.0, 1.0, 0.9986])
assert np.abs(image_slice - expected_slice).max() < 1e-2
def test_amused_512(self):
pipe = AmusedImg2ImgPipeline.from_pretrained("amused/amused-512")
pipe.to(torch_device)
image = (
load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg")
.resize((512, 512))
.convert("RGB")
)
image = pipe(
"winter mountains",
image,
generator=torch.Generator().manual_seed(0),
num_inference_steps=2,
output_type="np",
).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.1344, 0.0985, 0.0, 0.1194, 0.1809, 0.0765, 0.0854, 0.1371, 0.0933])
assert np.abs(image_slice - expected_slice).max() < 0.1
def test_amused_512_fp16(self):
pipe = AmusedImg2ImgPipeline.from_pretrained("amused/amused-512", variant="fp16", torch_dtype=torch.float16)
pipe.to(torch_device)
image = (
load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg")
.resize((512, 512))
.convert("RGB")
)
image = pipe(
"winter mountains",
image,
generator=torch.Generator().manual_seed(0),
num_inference_steps=2,
output_type="np",
).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.1536, 0.1767, 0.0227, 0.1079, 0.2400, 0.1427, 0.1511, 0.1564, 0.1542])
assert np.abs(image_slice - expected_slice).max() < 0.1
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