refiners/tests/foundationals/latent_diffusion/test_schedulers.py
2023-12-04 15:27:06 +01:00

109 lines
3.8 KiB
Python

import pytest
from typing import cast
from warnings import warn
from refiners.foundationals.latent_diffusion.schedulers import Scheduler, DPMSolver, DDIM
from refiners.fluxion import norm, manual_seed
from torch import linspace, float32, randn, Tensor, allclose, device as Device
def test_scheduler_utils():
class DummyScheduler(Scheduler):
def __call__(self, x: Tensor, noise: Tensor, step: int) -> Tensor:
return Tensor()
def _generate_timesteps(self) -> Tensor:
return Tensor()
scheduler = DummyScheduler(num_inference_steps=10, num_train_timesteps=20, initial_diffusion_rate=0.1, final_diffusion_rate=0.2, device="cpu")
scale_factors = (
1.0
- linspace(
start=0.1**0.5,
end=0.2**0.5,
steps=20,
dtype=float32,
)
** 2
)
assert norm(scheduler.scale_factors - scale_factors) == 0
def test_dpm_solver_diffusers():
from diffusers import DPMSolverMultistepScheduler as DiffuserScheduler # type: ignore
manual_seed(0)
diffusers_scheduler = DiffuserScheduler(beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012)
diffusers_scheduler.set_timesteps(30)
refiners_scheduler = DPMSolver(num_inference_steps=30)
sample = randn(1, 3, 32, 32)
noise = randn(1, 3, 32, 32)
for step, timestep in enumerate(diffusers_scheduler.timesteps):
diffusers_output = cast(Tensor, diffusers_scheduler.step(noise, timestep, sample).prev_sample) # type: ignore
refiners_output = refiners_scheduler(x=sample, noise=noise, step=step)
assert allclose(diffusers_output, refiners_output), f"outputs differ at step {step}"
def test_ddim_solver_diffusers():
from diffusers import DDIMScheduler # type: ignore
diffusers_scheduler = DDIMScheduler(
beta_end=0.012,
beta_schedule="scaled_linear",
beta_start=0.00085,
num_train_timesteps=1000,
set_alpha_to_one=False,
steps_offset=1,
clip_sample=False,
)
diffusers_scheduler.set_timesteps(30)
refiners_scheduler = DDIM(num_inference_steps=30)
sample = randn(1, 4, 32, 32)
noise = randn(1, 4, 32, 32)
for step, timestep in enumerate(diffusers_scheduler.timesteps):
diffusers_output = cast(Tensor, diffusers_scheduler.step(noise, timestep, sample).prev_sample) # type: ignore
refiners_output = refiners_scheduler(x=sample, noise=noise, step=step)
assert allclose(diffusers_output, refiners_output, rtol=0.01), f"outputs differ at step {step}"
def test_scheduler_remove_noise():
from diffusers import DDIMScheduler # type: ignore
diffusers_scheduler = DDIMScheduler(
beta_end=0.012,
beta_schedule="scaled_linear",
beta_start=0.00085,
num_train_timesteps=1000,
set_alpha_to_one=False,
steps_offset=1,
clip_sample=False,
)
diffusers_scheduler.set_timesteps(30)
refiners_scheduler = DDIM(num_inference_steps=30)
sample = randn(1, 4, 32, 32)
noise = randn(1, 4, 32, 32)
for step, timestep in enumerate(diffusers_scheduler.timesteps):
diffusers_output = cast(Tensor, diffusers_scheduler.step(noise, timestep, sample).pred_original_sample) # type: ignore
refiners_output = refiners_scheduler.remove_noise(x=sample, noise=noise, step=step)
assert allclose(diffusers_output, refiners_output, rtol=0.01), f"outputs differ at step {step}"
def test_scheduler_device(test_device: Device):
if test_device.type == "cpu":
warn("not running on CPU, skipping")
pytest.skip()
scheduler = DDIM(num_inference_steps=30, device=test_device)
x = randn(1, 4, 32, 32, device=test_device)
noise = randn(1, 4, 32, 32, device=test_device)
noised = scheduler.add_noise(x, noise, scheduler.steps[0])
assert noised.device == test_device