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wip: update ldm_patched

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currently issues with calculate_sigmas call
This commit is contained in:
Manuel Schmid
2024-06-02 20:52:52 +02:00
parent ab01104d42
commit 3881fa0806
84 changed files with 5380 additions and 1923 deletions
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ldm_patched/k_diffusion/sampling.py
+19 -28
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@@ -527,6 +527,9 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
@torch.no_grad()
def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
"""DPM-Solver++ (stochastic)."""
if len(sigmas) <= 1:
return x
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
@@ -595,6 +598,8 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
@torch.no_grad()
def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
"""DPM-Solver++(2M) SDE."""
if len(sigmas) <= 1:
return x
if solver_type not in {'heun', 'midpoint'}:
raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
@@ -642,6 +647,9 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
"""DPM-Solver++(3M) SDE."""
if len(sigmas) <= 1:
return x
seed = extra_args.get("seed", None)
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
@@ -690,18 +698,27 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
@torch.no_grad()
def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
if len(sigmas) <= 1:
return x
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
@torch.no_grad()
def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
if len(sigmas) <= 1:
return x
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
@torch.no_grad()
def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
if len(sigmas) <= 1:
return x
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)
@@ -748,10 +765,11 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n
x = denoised
if sigmas[i + 1] > 0:
x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1])
x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)
return x
@torch.no_grad()
def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
# From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
@@ -807,30 +825,3 @@ def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=Non
d_prime = w1 * d + w2 * d_2 + w3 * d_3
x = x + d_prime * dt
return x
@torch.no_grad()
def sample_tcd(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, eta=0.3):
extra_args = {} if extra_args is None else extra_args
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
model_sampling = model.inner_model.inner_model.model_sampling
timesteps_s = torch.floor((1 - eta) * model_sampling.timestep(sigmas)).to(dtype=torch.long).detach().cpu()
timesteps_s[-1] = 0
alpha_prod_s = model_sampling.alphas_cumprod[timesteps_s]
beta_prod_s = 1 - alpha_prod_s
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args) # predicted_original_sample
eps = (x - denoised) / sigmas[i]
denoised = alpha_prod_s[i + 1].sqrt() * denoised + beta_prod_s[i + 1].sqrt() * eps
if callback is not None:
callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
x = denoised
if eta > 0 and sigmas[i + 1] > 0:
noise = noise_sampler(sigmas[i], sigmas[i + 1])
x = x / alpha_prod_s[i+1].sqrt() + noise * (sigmas[i+1]**2 + 1 - 1/alpha_prod_s[i+1]).sqrt()
return x