209 lines
7.8 KiB
Python
209 lines
7.8 KiB
Python
from pathlib import Path
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import pytest
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import torch
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from lama_cleaner.model_manager import ModelManager
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from lama_cleaner.schema import HDStrategy, SDSampler
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from lama_cleaner.tests.test_model import get_config, assert_equal
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current_dir = Path(__file__).parent.absolute().resolve()
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save_dir = current_dir / 'result'
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save_dir.mkdir(exist_ok=True, parents=True)
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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device = torch.device(device)
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@pytest.mark.parametrize("sd_device", ['cuda'])
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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@pytest.mark.parametrize("sampler", [SDSampler.ddim])
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@pytest.mark.parametrize("cpu_textencoder", [True, False])
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@pytest.mark.parametrize("disable_nsfw", [True, False])
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def test_runway_sd_1_5_ddim(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw):
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def callback(i, t, latents):
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print(f"sd_step_{i}")
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if sd_device == 'cuda' and not torch.cuda.is_available():
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return
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sd_steps = 50 if sd_device == 'cuda' else 1
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model = ModelManager(name="sd1.5",
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device=torch.device(sd_device),
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hf_access_token="",
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sd_run_local=True,
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disable_nsfw=disable_nsfw,
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sd_cpu_textencoder=cpu_textencoder,
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callback=callback)
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cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps)
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cfg.sd_sampler = sampler
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name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
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assert_equal(
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model,
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cfg,
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f"runway_sd_{strategy.capitalize()}_{name}.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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fx=1.3
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)
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@pytest.mark.parametrize("sd_device", ['cuda'])
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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@pytest.mark.parametrize("sampler", [SDSampler.pndm, SDSampler.k_lms, SDSampler.k_euler, SDSampler.k_euler_a])
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@pytest.mark.parametrize("cpu_textencoder", [False])
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@pytest.mark.parametrize("disable_nsfw", [True])
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def test_runway_sd_1_5(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw):
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def callback(i, t, latents):
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print(f"sd_step_{i}")
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if sd_device == 'cuda' and not torch.cuda.is_available():
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return
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sd_steps = 50 if sd_device == 'cuda' else 1
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model = ModelManager(name="sd1.5",
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device=torch.device(sd_device),
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hf_access_token="",
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sd_run_local=True,
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disable_nsfw=disable_nsfw,
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sd_cpu_textencoder=cpu_textencoder,
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callback=callback)
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cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps)
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cfg.sd_sampler = sampler
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name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
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assert_equal(
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model,
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cfg,
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f"runway_sd_{strategy.capitalize()}_{name}.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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fx=1.3
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)
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@pytest.mark.parametrize("sd_device", ['cuda'])
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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@pytest.mark.parametrize("sampler", [SDSampler.ddim])
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def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler):
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def callback(i, t, latents):
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pass
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if sd_device == 'cuda' and not torch.cuda.is_available():
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return
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sd_steps = 50 if sd_device == 'cuda' else 1
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model = ModelManager(name="sd1.5",
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device=torch.device(sd_device),
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hf_access_token="",
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sd_run_local=True,
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disable_nsfw=False,
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sd_cpu_textencoder=False,
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callback=callback)
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cfg = get_config(
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strategy,
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sd_steps=sd_steps,
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prompt='Face of a fox, high resolution, sitting on a park bench',
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negative_prompt='orange, yellow, small',
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sd_sampler=sampler,
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sd_match_histograms=True
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)
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name = f"{sampler}_negative_prompt"
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assert_equal(
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model,
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cfg,
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f"runway_sd_{strategy.capitalize()}_{name}.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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fx=1
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)
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@pytest.mark.parametrize("sd_device", ['cuda'])
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
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@pytest.mark.parametrize("cpu_textencoder", [False])
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@pytest.mark.parametrize("disable_nsfw", [False])
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def test_runway_sd_1_5_sd_scale(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw):
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if sd_device == 'cuda' and not torch.cuda.is_available():
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return
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sd_steps = 50 if sd_device == 'cuda' else 1
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model = ModelManager(name="sd1.5",
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device=torch.device(sd_device),
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hf_access_token="",
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sd_run_local=True,
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disable_nsfw=disable_nsfw,
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sd_cpu_textencoder=cpu_textencoder)
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cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps, sd_scale=0.85)
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cfg.sd_sampler = sampler
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name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
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assert_equal(
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model,
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cfg,
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f"runway_sd_{strategy.capitalize()}_{name}_sdscale.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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fx=1.3
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)
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@pytest.mark.parametrize("sd_device", ['cuda'])
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
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def test_runway_sd_1_5_cpu_offload(sd_device, strategy, sampler):
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if sd_device == 'cuda' and not torch.cuda.is_available():
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return
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sd_steps = 50 if sd_device == 'cuda' else 1
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model = ModelManager(name="sd1.5",
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device=torch.device(sd_device),
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hf_access_token="",
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sd_run_local=True,
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disable_nsfw=True,
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sd_cpu_textencoder=False,
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cpu_offload=True)
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cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps, sd_scale=0.85)
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cfg.sd_sampler = sampler
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name = f"device_{sd_device}_{sampler}"
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assert_equal(
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model,
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cfg,
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f"runway_sd_{strategy.capitalize()}_{name}_cpu_offload.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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)
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@pytest.mark.parametrize("sd_device", ['cpu'])
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@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
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@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
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def test_runway_sd_1_5_cpu_offload_cpu_device(sd_device, strategy, sampler):
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model = ModelManager(name="sd1.5",
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device=torch.device(sd_device),
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hf_access_token="",
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sd_run_local=True,
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disable_nsfw=False,
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sd_cpu_textencoder=False,
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cpu_offload=True)
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cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=1, sd_scale=0.85)
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cfg.sd_sampler = sampler
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name = f"device_{sd_device}_{sampler}"
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assert_equal(
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model,
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cfg,
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f"runway_sd_{strategy.capitalize()}_{name}_cpu_offload_cpu_device.png",
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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)
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