Various updates

1 files changed, 92 insertions, 118 deletions

diff --git a/schedulers/scheduling_euler_a.py b/schedulers/scheduling_euler_a.py
index 13ea6b3..6abe971 100644
--- a/schedulers/scheduling_euler_a.py
+++ b/schedulers/scheduling_euler_a.py
@@ -7,113 +7,6 @@ from diffusers.configuration_utils import ConfigMixin, register_to_config
 from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
 
 
-'''
-helper functions:   append_zero(),
-                    t_to_sigma(),
-                    get_sigmas(),
-                    append_dims(),
-                    CFGDenoiserForward(),
-                    get_scalings(),
-                    DSsigma_to_t(),
-                    DiscreteEpsDDPMDenoiserForward(),
-                    to_d(),
-                    get_ancestral_step()
-need cleaning 
-'''
-
-
-def append_zero(x):
-    return torch.cat([x, x.new_zeros([1])])
-
-
-def t_to_sigma(t, sigmas):
-    t = t.float()
-    low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
-    return (1 - w) * sigmas[low_idx] + w * sigmas[high_idx]
-
-
-def get_sigmas(sigmas, n=None):
-    if n is None:
-        return append_zero(sigmas.flip(0))
-    t_max = len(sigmas) - 1  # = 999
-    t = torch.linspace(t_max, 0, n, device=sigmas.device, dtype=sigmas.dtype)
-    return append_zero(t_to_sigma(t, sigmas))
-
-# from k_samplers utils.py
-
-
-def append_dims(x, target_dims):
-    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
-    dims_to_append = target_dims - x.ndim
-    if dims_to_append < 0:
-        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
-    return x[(...,) + (None,) * dims_to_append]
-
-
-def CFGDenoiserForward(Unet, x_in, sigma_in, cond_in, cond_scale, quantize=False, DSsigmas=None):
-    # x_in = torch.cat([x] * 2)#A# concat the latent
-    # sigma_in = torch.cat([sigma] * 2) #A# concat sigma
-    # cond_in = torch.cat([uncond, cond])
-    # uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
-    # uncond, cond = DiscreteEpsDDPMDenoiserForward(Unet,x_in, sigma_in,DSsigmas=DSsigmas, cond=cond_in).chunk(2)
-    # return uncond + (cond - uncond) * cond_scale
-    noise_pred = DiscreteEpsDDPMDenoiserForward(
-        Unet, x_in, sigma_in, quantize=quantize, DSsigmas=DSsigmas, cond=cond_in)
-    return noise_pred
-
-# from k_samplers sampling.py
-
-
-def to_d(x, sigma, denoised):
-    """Converts a denoiser output to a Karras ODE derivative."""
-    return (x - denoised) / append_dims(sigma.to(denoised.device), x.ndim)
-
-
-def get_scalings(sigma):
-    sigma_data = 1.
-    c_out = -sigma
-    c_in = 1 / (sigma ** 2 + sigma_data ** 2) ** 0.5
-    return c_out, c_in
-
-# DiscreteSchedule DS
-
-
-def DSsigma_to_t(sigma, quantize=False, DSsigmas=None):
-    dists = torch.abs(sigma - DSsigmas[:, None])
-    if quantize:
-        return torch.argmin(dists, dim=0).view(sigma.shape)
-    low_idx, high_idx = torch.sort(torch.topk(dists, dim=0, k=2, largest=False).indices, dim=0)[0]
-    low, high = DSsigmas[low_idx], DSsigmas[high_idx]
-    w = (low - sigma) / (low - high)
-    w = w.clamp(0, 1)
-    t = (1 - w) * low_idx + w * high_idx
-    return t.view(sigma.shape)
-
-
-def DiscreteEpsDDPMDenoiserForward(Unet, input, sigma, DSsigmas=None, quantize=False, **kwargs):
-    sigma = sigma.to(dtype=input.dtype, device=Unet.device)
-    DSsigmas = DSsigmas.to(dtype=input.dtype, device=Unet.device)
-    c_out, c_in = [append_dims(x, input.ndim) for x in get_scalings(sigma)]
-    # print(f">>>>>>>>>>> {input.dtype} {c_in.dtype} {sigma.dtype} {DSsigmas.dtype}")
-    eps = Unet(input * c_in, DSsigma_to_t(sigma, quantize=quantize, DSsigmas=DSsigmas),
-               encoder_hidden_states=kwargs['cond']).sample
-    return input + eps * c_out
-
-
-# from k_samplers sampling.py
-def get_ancestral_step(sigma_from, sigma_to):
-    """Calculates the noise level (sigma_down) to step down to and the amount
-    of noise to add (sigma_up) when doing an ancestral sampling step."""
-    sigma_up = (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5
-    sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
-    return sigma_down, sigma_up
-
-
-'''
-Euler Ancestral Scheduler
-'''
-
-
 class EulerAScheduler(SchedulerMixin, ConfigMixin):
     """
     Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
@@ -154,20 +47,24 @@ class EulerAScheduler(SchedulerMixin, ConfigMixin):
         beta_end: float = 0.02,
         beta_schedule: str = "linear",
         trained_betas: Optional[np.ndarray] = None,
+        tensor_format: str = "pt",
+        num_inference_steps=None,
+        device='cuda'
     ):
         if trained_betas is not None:
-            self.betas = torch.from_numpy(trained_betas)
+            self.betas = torch.from_numpy(trained_betas).to(device)
         if beta_schedule == "linear":
-            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
+            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32, device=device)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
-            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
+            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps,
+                                        dtype=torch.float32, device=device) ** 2
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
 
+        self.device = device
+        self.tensor_format = tensor_format
+
         self.alphas = 1.0 - self.betas
         self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
 
@@ -175,8 +72,12 @@ class EulerAScheduler(SchedulerMixin, ConfigMixin):
         self.init_noise_sigma = 1.0
 
         # setable values
-        self.num_inference_steps = None
-        self.timesteps = np.arange(0, num_train_timesteps)[::-1]
+        self.num_inference_steps = num_inference_steps
+        self.timesteps = np.arange(0, num_train_timesteps)[::-1].copy()
+        # get sigmas
+        self.DSsigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
+        self.sigmas = self.get_sigmas(self.DSsigmas, self.num_inference_steps)
+        self.set_format(tensor_format=tensor_format)
 
     # A# take number of steps as input
     # A# store 1) number of steps 2) timesteps 3) schedule
@@ -192,7 +93,7 @@ class EulerAScheduler(SchedulerMixin, ConfigMixin):
 
         self.num_inference_steps = num_inference_steps
         self.DSsigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
-        self.sigmas = get_sigmas(self.DSsigmas, self.num_inference_steps).to(device=device)
+        self.sigmas = self.get_sigmas(self.DSsigmas, self.num_inference_steps)
         self.timesteps = self.sigmas[:-1]
         self.is_scale_input_called = False
 
@@ -251,8 +152,8 @@ class EulerAScheduler(SchedulerMixin, ConfigMixin):
         s_prev = self.sigmas[step_prev_index]
         latents = sample
 
-        sigma_down, sigma_up = get_ancestral_step(s, s_prev)
-        d = to_d(latents, s, model_output)
+        sigma_down, sigma_up = self.get_ancestral_step(s, s_prev)
+        d = self.to_d(latents, s, model_output)
         dt = sigma_down - s
         latents = latents + d * dt
         latents = latents + torch.randn(latents.shape, layout=latents.layout, device=latents.device, dtype=latents.dtype,
@@ -313,3 +214,76 @@ class EulerAScheduler(SchedulerMixin, ConfigMixin):
         noisy_samples = original_samples + noise * sigma
         self.is_scale_input_called = True
         return noisy_samples
+
+    # from k_samplers sampling.py
+
+    def get_ancestral_step(self, sigma_from, sigma_to):
+        """Calculates the noise level (sigma_down) to step down to and the amount
+        of noise to add (sigma_up) when doing an ancestral sampling step."""
+        sigma_up = (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5
+        sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
+        return sigma_down, sigma_up
+
+    def t_to_sigma(self, t, sigmas):
+        t = t.float()
+        low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
+        return (1 - w) * sigmas[low_idx] + w * sigmas[high_idx]
+
+    def append_zero(self, x):
+        return torch.cat([x, x.new_zeros([1])])
+
+    def get_sigmas(self, sigmas, n=None):
+        if n is None:
+            return self.append_zero(sigmas.flip(0))
+        t_max = len(sigmas) - 1  # = 999
+        device = self.device
+        t = torch.linspace(t_max, 0, n, device=device)
+        # t = torch.linspace(t_max, 0, n, device=sigmas.device)
+        return self.append_zero(self.t_to_sigma(t, sigmas))
+
+    # from k_samplers utils.py
+    def append_dims(self, x, target_dims):
+        """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+        dims_to_append = target_dims - x.ndim
+        if dims_to_append < 0:
+            raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+        return x[(...,) + (None,) * dims_to_append]
+
+    # from k_samplers sampling.py
+    def to_d(self, x, sigma, denoised):
+        """Converts a denoiser output to a Karras ODE derivative."""
+        return (x - denoised) / self.append_dims(sigma, x.ndim)
+
+    def get_scalings(self, sigma):
+        sigma_data = 1.
+        c_out = -sigma
+        c_in = 1 / (sigma ** 2 + sigma_data ** 2) ** 0.5
+        return c_out, c_in
+
+    # DiscreteSchedule DS
+    def DSsigma_to_t(self, sigma, quantize=None):
+        # quantize = self.quantize if quantize is None else quantize
+        quantize = False
+        dists = torch.abs(sigma - self.DSsigmas[:, None])
+        if quantize:
+            return torch.argmin(dists, dim=0).view(sigma.shape)
+        low_idx, high_idx = torch.sort(torch.topk(dists, dim=0, k=2, largest=False).indices, dim=0)[0]
+        low, high = self.DSsigmas[low_idx], self.DSsigmas[high_idx]
+        w = (low - sigma) / (low - high)
+        w = w.clamp(0, 1)
+        t = (1 - w) * low_idx + w * high_idx
+        return t.view(sigma.shape)
+
+    def prepare_input(self, latent_in, t, batch_size):
+        sigma = t.reshape(1)  # A# potential bug: doesn't work on samples > 1
+
+        sigma_in = torch.cat([sigma] * 2 * batch_size)
+        # noise_pred = CFGDenoiserForward(self.unet, latent_model_input, sigma_in, text_embeddings , guidance_scale,DSsigmas=self.scheduler.DSsigmas)
+        # noise_pred = DiscreteEpsDDPMDenoiserForward(self.unet,latent_model_input, sigma_in,DSsigmas=self.scheduler.DSsigmas, cond=cond_in)
+        c_out, c_in = [self.append_dims(x, latent_in.ndim) for x in self.get_scalings(sigma_in)]
+
+        sigma_in = self.DSsigma_to_t(sigma_in)
+        # s_in = latent_in.new_ones([latent_in.shape[0]])
+        # sigma_in = sigma_in * s_in
+
+        return c_out, c_in, sigma_in