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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 random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import AutoTokenizer, T5EncoderModel
from diffusers import (
AutoPipelineForImage2Image,
Kandinsky3Img2ImgPipeline,
Kandinsky3UNet,
VQModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class Kandinsky3Img2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = Kandinsky3Img2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS
test_xformers_attention = False
required_optional_params = frozenset(
[
"num_inference_steps",
"num_images_per_prompt",
"generator",
"output_type",
"return_dict",
]
)
@property
def dummy_movq_kwargs(self):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def dummy_movq(self):
torch.manual_seed(0)
model = VQModel(**self.dummy_movq_kwargs)
return model
def get_dummy_components(self, time_cond_proj_dim=None):
torch.manual_seed(0)
unet = Kandinsky3UNet(
in_channels=4,
time_embedding_dim=4,
groups=2,
attention_head_dim=4,
layers_per_block=3,
block_out_channels=(32, 64),
cross_attention_dim=4,
encoder_hid_dim=32,
)
scheduler = DDPMScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="squaredcos_cap_v2",
clip_sample=True,
thresholding=False,
)
torch.manual_seed(0)
movq = self.dummy_movq
torch.manual_seed(0)
text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
torch.manual_seed(0)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
components = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def get_dummy_inputs(self, device, seed=0):
# create init_image
image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)
image = image.cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": init_image,
"generator": generator,
"strength": 0.75,
"num_inference_steps": 10,
"guidance_scale": 6.0,
"output_type": "np",
}
return inputs
def test_dict_tuple_outputs_equivalent(self):
expected_slice = None
if torch_device == "cpu":
expected_slice = np.array([0.5762, 0.6112, 0.4150, 0.6018, 0.6167, 0.4626, 0.5426, 0.5641, 0.6536])
super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)
def test_kandinsky3_img2img(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(device))
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array(
[0.576259, 0.6132097, 0.41703486, 0.603196, 0.62062526, 0.4655338, 0.5434324, 0.5660727, 0.65433365]
)
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
def test_float16_inference(self):
super().test_float16_inference(expected_max_diff=1e-1)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=1e-2)
@slow
@require_torch_gpu
class Kandinsky3Img2ImgPipelineIntegrationTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_kandinskyV3_img2img(self):
pipe = AutoPipelineForImage2Image.from_pretrained(
"kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png"
)
w, h = 512, 512
image = image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
prompt = "A painting of the inside of a subway train with tiny raccoons."
image = pipe(prompt, image=image, strength=0.75, num_inference_steps=5, generator=generator).images[0]
assert image.size == (512, 512)
expected_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/i2i.png"
)
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=5e-2))