Spaces:
Running
on
Zero
Running
on
Zero
# 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", | |
] | |
) | |
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, | |
} | |
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) | |
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)) | |