refactor: remove unused parameters

imaginairy/api.py

@@ -280,14 +280,12 @@ def imagine(
                     # encode (scaled latent)
                     seed_everything(prompt.seed)
                     noise = torch.randn_like(init_latent, device="cpu").to(get_device())
+                    # todo: this isn't the right scheduler for everything...
                     schedule = PLMSSchedule(
                         ddpm_num_timesteps=model.num_timesteps,
                         ddim_num_steps=prompt.steps,
                         alphas_cumprod=model.alphas_cumprod,
-                        alphas_cumprod_prev=model.alphas_cumprod_prev,
-                        betas=model.betas,
                         ddim_discretize="uniform",
-                        ddim_eta=0.0,
                     )
                     if generation_strength >= 1:
                         # prompt strength gets converted to time encodings,

imaginairy/modules/diffusion/ddpm.py

@@ -169,15 +169,6 @@ class DDPM(pl.LightningModule):
         self.register_buffer(
             "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
         )
-        self.register_buffer(
-            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
-        )
-        self.register_buffer(
-            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
-        )
-        self.register_buffer(
-            "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
-        )
 
         # calculations for posterior q(x_{t-1} | x_t, x_0)
         posterior_variance = (1 - self.v_posterior) * betas * (

imaginairy/samplers/ddim.py

@@ -23,8 +23,6 @@ class DDIMSchedule:
         self,
         model_num_timesteps,
         model_alphas_cumprod,
-        model_alphas_cumprod_prev,
-        model_betas,
         ddim_num_steps,
         ddim_discretize="uniform",
         ddim_eta=0.0,
@@ -49,19 +47,12 @@ class DDIMSchedule:
             eta=ddim_eta,
         )
         self.ddim_timesteps = ddim_timesteps
-        self.betas = to_torch(model_betas)
         self.alphas_cumprod = to_torch(alphas_cumprod)
-        self.alphas_cumprod_prev = to_torch(model_alphas_cumprod_prev)
         # calculations for diffusion q(x_t | x_{t-1}) and others
         self.sqrt_alphas_cumprod = to_torch(np.sqrt(alphas_cumprod.cpu()))
         self.sqrt_one_minus_alphas_cumprod = to_torch(
             np.sqrt(1.0 - alphas_cumprod.cpu())
         )
-        self.log_one_minus_alphas_cumprod = to_torch(np.log(1.0 - alphas_cumprod.cpu()))
-        self.sqrt_recip_alphas_cumprod = to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
-        self.sqrt_recipm1_alphas_cumprod = to_torch(
-            np.sqrt(1.0 / alphas_cumprod.cpu() - 1)
-        )
         self.ddim_sigmas = ddim_sigmas.to(torch.float32).to(device)
         self.ddim_alphas = ddim_alphas.to(torch.float32).to(device)
         self.ddim_alphas_prev = ddim_alphas_prev
@@ -109,8 +100,6 @@ class DDIMSampler:
         schedule = DDIMSchedule(
             model_num_timesteps=self.model.num_timesteps,
             model_alphas_cumprod=self.model.alphas_cumprod,
-            model_alphas_cumprod_prev=self.model.alphas_cumprod_prev,
-            model_betas=self.model.betas,
             ddim_num_steps=num_steps,
             ddim_discretize="uniform",
             ddim_eta=0.0,

imaginairy/samplers/plms.py

@@ -24,13 +24,8 @@ class PLMSSchedule:
         ddpm_num_timesteps,  # 1000?
         ddim_num_steps,  # prompt.steps?
         alphas_cumprod,
-        alphas_cumprod_prev,
-        betas,
         ddim_discretize="uniform",
-        ddim_eta=0.0,
     ):
-        if ddim_eta != 0:
-            raise ValueError("ddim_eta must be 0 for PLMS")
         device = get_device()
 
         assert (
@@ -40,19 +35,12 @@ class PLMSSchedule:
         def to_torch(x):
             return x.clone().detach().to(torch.float32).to(device)
 
-        self.betas = to_torch(betas)
         self.alphas_cumprod = to_torch(alphas_cumprod)
-        self.alphas_cumprod_prev = to_torch(alphas_cumprod_prev)
         # calculations for diffusion q(x_t | x_{t-1}) and others
         self.sqrt_alphas_cumprod = to_torch(np.sqrt(alphas_cumprod.cpu()))
         self.sqrt_one_minus_alphas_cumprod = to_torch(
             np.sqrt(1.0 - alphas_cumprod.cpu())
         )
-        self.log_one_minus_alphas_cumprod = to_torch(np.log(1.0 - alphas_cumprod.cpu()))
-        self.sqrt_recip_alphas_cumprod = to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
-        self.sqrt_recipm1_alphas_cumprod = to_torch(
-            np.sqrt(1.0 / alphas_cumprod.cpu() - 1)
-        )
 
         self.ddim_timesteps = make_ddim_timesteps(
             ddim_discr_method=ddim_discretize,
@@ -64,7 +52,7 @@ class PLMSSchedule:
         ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(
             alphacums=alphas_cumprod.cpu(),
             ddim_timesteps=self.ddim_timesteps,
-            eta=ddim_eta,
+            eta=0.0,
         )
         self.ddim_sigmas = ddim_sigmas.to(torch.float32).to(torch.device(device))
         self.ddim_alphas = ddim_alphas.to(torch.float32).to(torch.device(device))
@@ -113,10 +101,7 @@ class PLMSSampler:
             ddpm_num_timesteps=self.model.num_timesteps,
             ddim_num_steps=num_steps,
             alphas_cumprod=self.model.alphas_cumprod,
-            alphas_cumprod_prev=self.model.alphas_cumprod_prev,
-            betas=self.model.betas,
             ddim_discretize="uniform",
-            ddim_eta=0.0,
         )
         device = self.device
         # batch_size = shape[0]