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import os
from lama_cleaner.const import SD_CONTROLNET_CHOICES
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", "mps"])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.uni_pc])
@pytest.mark.parametrize("cpu_textencoder", [True])
@pytest.mark.parametrize("disable_nsfw", [True])
@pytest.mark.parametrize("sd_controlnet_method", SD_CONTROLNET_CHOICES)
def test_runway_sd_1_5(
sd_device, strategy, sampler, cpu_textencoder, disable_nsfw, sd_controlnet_method
):
if sd_device == "cuda" and not torch.cuda.is_available():
return
if device == "mps" and not torch.backends.mps.is_available():
return
sd_steps = 1 if sd_device == "cpu" else 30
model = ModelManager(
name="sd1.5",
sd_controlnet=True,
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=False,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=cpu_textencoder,
sd_controlnet_method=sd_controlnet_method,
)
controlnet_conditioning_scale = {
"control_v11p_sd15_canny": 0.4,
"control_v11p_sd15_openpose": 0.4,
"control_v11p_sd15_inpaint": 1.0,
"control_v11f1p_sd15_depth": 1.0,
}[sd_controlnet_method]
cfg = get_config(
strategy,
prompt="a fox sitting on a bench",
sd_steps=sd_steps,
controlnet_conditioning_scale=controlnet_conditioning_scale,
controlnet_method=sd_controlnet_method,
)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}_cpu_textencoder_disable_nsfw"
assert_equal(
model,
cfg,
f"sd_controlnet_{sd_controlnet_method}_{name}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1.2,
fy=1.2,
)
@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
if device == "mps" and not torch.backends.mps.is_available():
return
sd_steps = 1 if sd_device == "cpu" else 30
model = ModelManager(
name="sd1.5",
sd_controlnet=True,
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=False,
disable_nsfw=True,
sd_cpu_textencoder=False,
cpu_offload=True,
sd_local_model_path="/Users/cwq/data/models/sd-v1-5-inpainting.ckpt",
sd_controlnet_method="control_v11p_sd15_canny",
)
cfg = get_config(
HDStrategy.ORIGINAL,
prompt="a fox sitting on a bench",
sd_steps=sd_steps,
controlnet_method="control_v11p_sd15_canny",
)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}"
assert_equal(
model,
cfg,
f"sd_controlnet_canny_local_model_{name}.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_controlnet_native_inpainting(sd_device, sampler):
if sd_device == "cuda" and not torch.cuda.is_available():
return
if device == "mps" and not torch.backends.mps.is_available():
return
sd_steps = 1 if sd_device == "cpu" else 30
model = ModelManager(
name="sd1.5",
sd_controlnet=True,
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=False,
disable_nsfw=True,
sd_cpu_textencoder=False,
cpu_offload=True,
sd_local_model_path="/Users/cwq/data/models/v1-5-pruned-emaonly.safetensors",
sd_controlnet_method="control_v11p_sd15_inpaint",
)
cfg = get_config(
HDStrategy.ORIGINAL,
prompt="a fox sitting on a bench",
sd_steps=sd_steps,
controlnet_conditioning_scale=1.0,
sd_strength=1.0,
controlnet_method="control_v11p_sd15_inpaint",
)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}"
assert_equal(
model,
cfg,
f"sd_controlnet_local_native_{name}.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_controlnet_switch(sd_device, sampler):
if sd_device == "cuda" and not torch.cuda.is_available():
return
if device == "mps" and not torch.backends.mps.is_available():
return
sd_steps = 1 if sd_device == "cpu" else 30
model = ModelManager(
name="sd1.5",
sd_controlnet=True,
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=False,
disable_nsfw=True,
sd_cpu_textencoder=False,
cpu_offload=True,
sd_controlnet_method="control_v11p_sd15_canny",
)
cfg = get_config(
HDStrategy.ORIGINAL,
prompt="a fox sitting on a bench",
sd_steps=sd_steps,
controlnet_method="control_v11p_sd15_inpaint",
)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}"
assert_equal(
model,
cfg,
f"sd_controlnet_switch_to_inpaint_local_model_{name}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
)
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