Finegrain/refiners
1
0
Fork 0
mirror of https://github.com/finegrain-ai/refiners.git synced 2024-11-09 23:12:02 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

use "solver" (not scheduler) wording in tests

Browse source
This commit is contained in:
Pierre Chapuis 2024-07-10 16:09:45 +02:00
parent 9e8c2a3753
commit b4db08de24
1 changed files with 23 additions and 23 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

46
tests/foundationals/latent_diffusion/test_solvers.py
View file

@ -25,8 +25,8 @@ def test_ddpm_diffusers():
diffusers_scheduler = DDPMScheduler(beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012)
diffusers_scheduler.set_timesteps(1000)
refiners_scheduler = DDPM(num_inference_steps=1000)
assert equal(diffusers_scheduler.timesteps, refiners_scheduler.timesteps)
solver = DDPM(num_inference_steps=1000)
assert equal(diffusers_scheduler.timesteps, solver.timesteps)
@pytest.mark.parametrize("n_steps, last_step_first_order", [(5, False), (5, True), (30, False), (30, True)])
@ -44,18 +44,18 @@ def test_dpm_solver_diffusers(n_steps: int, last_step_first_order: bool):
final_sigmas_type="sigma_min", # default before Diffusers 0.26.0
)
diffusers_scheduler.set_timesteps(n_steps)
refiners_scheduler = DPMSolver(
solver = DPMSolver(
num_inference_steps=n_steps,
last_step_first_order=last_step_first_order,
)
assert equal(refiners_scheduler.timesteps, diffusers_scheduler.timesteps)
assert equal(solver.timesteps, diffusers_scheduler.timesteps)
sample = randn(1, 3, 32, 32)
predicted_noise = randn(1, 3, 32, 32)
for step, timestep in enumerate(diffusers_scheduler.timesteps):
diffusers_output = cast(Tensor, diffusers_scheduler.step(predicted_noise, timestep, sample).prev_sample) # type: ignore
refiners_output = refiners_scheduler(x=sample, predicted_noise=predicted_noise, step=step)
refiners_output = solver(x=sample, predicted_noise=predicted_noise, step=step)
assert allclose(diffusers_output, refiners_output, rtol=0.01), f"outputs differ at step {step}"
@ -73,15 +73,15 @@ def test_ddim_diffusers():
clip_sample=False,
)
diffusers_scheduler.set_timesteps(30)
refiners_scheduler = DDIM(num_inference_steps=30)
assert equal(refiners_scheduler.timesteps, diffusers_scheduler.timesteps)
solver = DDIM(num_inference_steps=30)
assert equal(solver.timesteps, diffusers_scheduler.timesteps)
sample = randn(1, 4, 32, 32)
predicted_noise = randn(1, 4, 32, 32)
for step, timestep in enumerate(diffusers_scheduler.timesteps):
diffusers_output = cast(Tensor, diffusers_scheduler.step(predicted_noise, timestep, sample).prev_sample) # type: ignore
refiners_output = refiners_scheduler(x=sample, predicted_noise=predicted_noise, step=step)
refiners_output = solver(x=sample, predicted_noise=predicted_noise, step=step)
assert allclose(diffusers_output, refiners_output, rtol=0.01), f"outputs differ at step {step}"
@ -103,19 +103,19 @@ def test_euler_diffusers(model_prediction_type: ModelPredictionType):
prediction_type=diffusers_prediction_type,
)
diffusers_scheduler.set_timesteps(30)
refiners_scheduler = Euler(num_inference_steps=30, params=SolverParams(model_prediction_type=model_prediction_type))
assert equal(refiners_scheduler.timesteps, diffusers_scheduler.timesteps)
solver = Euler(num_inference_steps=30, params=SolverParams(model_prediction_type=model_prediction_type))
assert equal(solver.timesteps, diffusers_scheduler.timesteps)
sample = randn(1, 4, 32, 32)
predicted_noise = randn(1, 4, 32, 32)
ref_init_noise_sigma = diffusers_scheduler.init_noise_sigma # type: ignore
assert isinstance(ref_init_noise_sigma, Tensor)
assert isclose(ref_init_noise_sigma, refiners_scheduler.init_noise_sigma), "init_noise_sigma differ"
assert isclose(ref_init_noise_sigma, solver.init_noise_sigma), "init_noise_sigma differ"
for step, timestep in enumerate(diffusers_scheduler.timesteps):
diffusers_output = cast(Tensor, diffusers_scheduler.step(predicted_noise, timestep, sample).prev_sample) # type: ignore
refiners_output = refiners_scheduler(x=sample, predicted_noise=predicted_noise, step=step)
refiners_output = solver(x=sample, predicted_noise=predicted_noise, step=step)
assert allclose(diffusers_output, refiners_output, rtol=0.02), f"outputs differ at step {step}"
@ -138,20 +138,20 @@ def test_franken_diffusers():
diffusers_scheduler.set_timesteps(30)
diffusers_scheduler_2 = EulerDiscreteScheduler(**params) # type: ignore
refiners_scheduler = FrankenSolver(diffusers_scheduler_2, num_inference_steps=30)
assert equal(refiners_scheduler.timesteps, diffusers_scheduler.timesteps)
solver = FrankenSolver(diffusers_scheduler_2, num_inference_steps=30)
assert equal(solver.timesteps, diffusers_scheduler.timesteps)
sample = randn(1, 4, 32, 32)
predicted_noise = randn(1, 4, 32, 32)
ref_init_noise_sigma = diffusers_scheduler.init_noise_sigma # type: ignore
assert isinstance(ref_init_noise_sigma, Tensor)
init_noise_sigma = refiners_scheduler.scale_model_input(tensor(1), step=-1)
init_noise_sigma = solver.scale_model_input(tensor(1), step=-1)
assert equal(ref_init_noise_sigma, init_noise_sigma), "init_noise_sigma differ"
for step, timestep in enumerate(diffusers_scheduler.timesteps):
diffusers_output = cast(Tensor, diffusers_scheduler.step(predicted_noise, timestep, sample).prev_sample) # type: ignore
refiners_output = refiners_scheduler(x=sample, predicted_noise=predicted_noise, step=step)
refiners_output = solver(x=sample, predicted_noise=predicted_noise, step=step)
assert equal(diffusers_output, refiners_output), f"outputs differ at step {step}"
@ -167,8 +167,8 @@ def test_lcm_diffusers():
diffusers_scheduler = LCMScheduler()
diffusers_scheduler.set_timesteps(4)
refiners_scheduler = LCMSolver(num_inference_steps=4)
assert equal(refiners_scheduler.timesteps, diffusers_scheduler.timesteps)
solver = LCMSolver(num_inference_steps=4)
assert equal(solver.timesteps, diffusers_scheduler.timesteps)
sample = randn(1, 4, 32, 32)
predicted_noise = randn(1, 4, 32, 32)
@ -178,8 +178,8 @@ def test_lcm_diffusers():
diffusers_noise_ratio = (1 - alpha_prod_t).sqrt()
diffusers_scale_factor = alpha_prod_t.sqrt()
refiners_scale_factor = refiners_scheduler.cumulative_scale_factors[timestep]
refiners_noise_ratio = refiners_scheduler.noise_std[timestep]
refiners_scale_factor = solver.cumulative_scale_factors[timestep]
refiners_noise_ratio = solver.noise_std[timestep]
assert refiners_scale_factor == diffusers_scale_factor
assert refiners_noise_ratio == diffusers_noise_ratio
@ -187,7 +187,7 @@ def test_lcm_diffusers():
d_out = diffusers_scheduler.step(predicted_noise, timestep, sample, generator=diffusers_generator) # type: ignore
diffusers_output = cast(Tensor, d_out.prev_sample) # type: ignore
refiners_output = refiners_scheduler(
refiners_output = solver(
x=sample,
predicted_noise=predicted_noise,
step=step,
@ -211,14 +211,14 @@ def test_solver_remove_noise():
clip_sample=False,
)
diffusers_scheduler.set_timesteps(30)
refiners_scheduler = DDIM(num_inference_steps=30)
solver = 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)
refiners_output = solver.remove_noise(x=sample, noise=noise, step=step)
assert allclose(diffusers_output, refiners_output, rtol=0.01), f"outputs differ at step {step}"