File size: 4,836 Bytes
44e6efe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
import gc
import unittest
import numpy as np
import torch
from diffusers import FluxPipeline, FluxPriorReduxPipeline
from diffusers.utils import load_image
from ...testing_utils import (
Expectations,
backend_empty_cache,
numpy_cosine_similarity_distance,
require_big_accelerator,
slow,
torch_device,
)
@slow
@require_big_accelerator
class FluxReduxSlowTests(unittest.TestCase):
pipeline_class = FluxPriorReduxPipeline
repo_id = "black-forest-labs/FLUX.1-Redux-dev"
base_pipeline_class = FluxPipeline
base_repo_id = "black-forest-labs/FLUX.1-schnell"
def setUp(self):
super().setUp()
gc.collect()
backend_empty_cache(torch_device)
def tearDown(self):
super().tearDown()
gc.collect()
backend_empty_cache(torch_device)
def get_inputs(self, device, seed=0):
init_image = load_image(
"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy/img5.png"
)
return {"image": init_image}
def get_base_pipeline_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device="cpu").manual_seed(seed)
return {
"num_inference_steps": 2,
"guidance_scale": 2.0,
"output_type": "np",
"generator": generator,
}
def test_flux_redux_inference(self):
pipe_redux = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.bfloat16)
pipe_base = self.base_pipeline_class.from_pretrained(
self.base_repo_id, torch_dtype=torch.bfloat16, text_encoder=None, text_encoder_2=None
)
pipe_redux.to(torch_device)
pipe_base.enable_model_cpu_offload(device=torch_device)
inputs = self.get_inputs(torch_device)
base_pipeline_inputs = self.get_base_pipeline_inputs(torch_device)
redux_pipeline_output = pipe_redux(**inputs)
image = pipe_base(**base_pipeline_inputs, **redux_pipeline_output).images[0]
image_slice = image[0, :10, :10]
expected_slices = Expectations(
{
("cuda", 7): np.array(
[
0.30078125,
0.37890625,
0.46875,
0.28125,
0.36914062,
0.47851562,
0.28515625,
0.375,
0.4765625,
0.28125,
0.375,
0.48046875,
0.27929688,
0.37695312,
0.47851562,
0.27734375,
0.38085938,
0.4765625,
0.2734375,
0.38085938,
0.47265625,
0.27539062,
0.37890625,
0.47265625,
0.27734375,
0.37695312,
0.47070312,
0.27929688,
0.37890625,
0.47460938,
],
dtype=np.float32,
),
("xpu", 3): np.array(
[
0.20507812,
0.30859375,
0.3984375,
0.18554688,
0.30078125,
0.41015625,
0.19921875,
0.3125,
0.40625,
0.19726562,
0.3125,
0.41601562,
0.19335938,
0.31445312,
0.4140625,
0.1953125,
0.3203125,
0.41796875,
0.19726562,
0.32421875,
0.41992188,
0.19726562,
0.32421875,
0.41992188,
0.20117188,
0.32421875,
0.41796875,
0.203125,
0.32617188,
0.41796875,
],
dtype=np.float32,
),
}
)
expected_slice = expected_slices.get_expectation()
max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())
assert max_diff < 1e-4
|