Spaces:
Sleeping
Sleeping
File size: 7,647 Bytes
89ea09f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 |
import os
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
from pathlib import Path
import pytest
import torch
from lama_cleaner.model_manager import ModelManager
from lama_cleaner.schema import HDStrategy, SDSampler
from lama_cleaner.tests.test_model import get_config, assert_equal
current_dir = Path(__file__).parent.absolute().resolve()
save_dir = current_dir / "result"
save_dir.mkdir(exist_ok=True, parents=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(device)
@pytest.mark.parametrize("sd_device", ["cuda"])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.ddim])
@pytest.mark.parametrize("cpu_textencoder", [True, False])
@pytest.mark.parametrize("disable_nsfw", [True, False])
def test_runway_sd_1_5_ddim(
sd_device, strategy, sampler, cpu_textencoder, disable_nsfw
):
def callback(i, t, latents):
pass
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == "cuda" else 1
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=cpu_textencoder,
callback=callback,
)
cfg = get_config(strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
assert_equal(
model,
cfg,
f"runway_sd_{strategy.capitalize()}_{name}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1.3,
)
@pytest.mark.parametrize("sd_device", ["cuda"])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize(
"sampler", [SDSampler.pndm, SDSampler.k_lms, SDSampler.k_euler, SDSampler.k_euler_a]
)
@pytest.mark.parametrize("cpu_textencoder", [False])
@pytest.mark.parametrize("disable_nsfw", [True])
def test_runway_sd_1_5(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw):
def callback(i, t, latents):
print(f"sd_step_{i}")
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == "cuda" else 1
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=cpu_textencoder,
callback=callback,
)
cfg = get_config(strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
assert_equal(
model,
cfg,
f"runway_sd_{strategy.capitalize()}_{name}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1.3,
)
@pytest.mark.parametrize("sd_device", ["cuda"])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.ddim])
def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler):
def callback(i, t, latents):
pass
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == "cuda" else 1
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=False,
sd_cpu_textencoder=False,
callback=callback,
)
cfg = get_config(
strategy,
sd_steps=sd_steps,
prompt="Face of a fox, high resolution, sitting on a park bench",
negative_prompt="orange, yellow, small",
sd_sampler=sampler,
sd_match_histograms=True,
)
name = f"{sampler}_negative_prompt"
assert_equal(
model,
cfg,
f"runway_sd_{strategy.capitalize()}_{name}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1,
)
@pytest.mark.parametrize("sd_device", ["cuda"])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
@pytest.mark.parametrize("cpu_textencoder", [False])
@pytest.mark.parametrize("disable_nsfw", [False])
def test_runway_sd_1_5_sd_scale(
sd_device, strategy, sampler, cpu_textencoder, disable_nsfw
):
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == "cuda" else 1
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=cpu_textencoder,
)
cfg = get_config(
strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps, sd_scale=0.85
)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
assert_equal(
model,
cfg,
f"runway_sd_{strategy.capitalize()}_{name}_sdscale.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1.3,
)
@pytest.mark.parametrize("sd_device", ["cuda"])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
def test_runway_sd_1_5_cpu_offload(sd_device, strategy, sampler):
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == "cuda" else 1
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=True,
sd_cpu_textencoder=False,
cpu_offload=True,
)
cfg = get_config(
strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps, sd_scale=0.85
)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}"
assert_equal(
model,
cfg,
f"runway_sd_{strategy.capitalize()}_{name}_cpu_offload.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
)
@pytest.mark.parametrize("sd_device", ["cuda", "mps"])
@pytest.mark.parametrize("sampler", [SDSampler.uni_pc])
def test_local_file_path(sd_device, sampler):
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 1 if sd_device == "cpu" else 50
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=True,
sd_cpu_textencoder=False,
cpu_offload=True,
sd_local_model_path="/Users/cwq/data/models/sd-v1-5-inpainting.ckpt",
)
cfg = get_config(
HDStrategy.ORIGINAL,
prompt="a fox sitting on a bench",
sd_steps=sd_steps,
)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}"
assert_equal(
model,
cfg,
f"sd_local_model_{name}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
)
|