diffuse the cascaded prior for continuous sr model

codes/models/audio/music/transformer_diffusion13.py

@@ -1,10 +1,10 @@
 import itertools
+import random
 from random import randrange
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import torchvision.utils
 
 from models.arch_util import ResBlock, TimestepEmbedSequential, AttentionBlock, build_local_attention_mask
 from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
@@ -88,7 +88,7 @@ class ConditioningEncoder(nn.Module):
     def forward(self, x, resolution):
         h = self.init(x) + self.resolution_embedding(resolution).unsqueeze(-1)
         h = self.attn(h)
-        return h[:, :, :6]
+        return h[:, :, :5]
 
 
 class TransformerDiffusion(nn.Module):
@@ -130,10 +130,14 @@ class TransformerDiffusion(nn.Module):
             nn.SiLU(),
             linear(time_embed_dim, model_channels),
         )
+        self.prior_time_embed = nn.Sequential(
+            linear(time_embed_dim, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, model_channels),
+        )
         self.resolution_embed = nn.Embedding(resolution_steps, model_channels)
         self.conditioning_encoder = ConditioningEncoder(in_channels, model_channels, resolution_steps, num_attn_heads=model_channels//64)
-        self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,6))
+        self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,5))
-        self.unconditioned_prior = nn.Parameter(torch.zeros(1,in_channels,1))
 
         self.inp_block = conv_nd(1, in_channels+input_vec_dim, model_channels, 3, 1, 1)
         self.layers = TimestepEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, num_heads, dropout) for _ in range(num_layers)])
@@ -169,7 +173,7 @@ class TransformerDiffusion(nn.Module):
         }
         return groups
 
-    def input_to_random_resolution_and_window(self, x):
+    def input_to_random_resolution_and_window(self, x, ts, diffuser):
         """
         This function MUST be applied to the target *before* noising. It returns the reduced, re-scoped target as well
         as caches an internal prior for the rescoped target which will be used in training.
@@ -185,26 +189,47 @@ class TransformerDiffusion(nn.Module):
             s = s[:,:,start:start+self.max_window]
         s_prior = F.interpolate(s, scale_factor=.25, mode='nearest')
         s_prior = F.interpolate(s_prior, size=(s.shape[-1],), mode='linear', align_corners=True)
-        self.preprocessed = (s_prior, torch.tensor([resolution] * x.shape[0], dtype=torch.long, device=x.device))
+
+        # Now diffuse the prior randomly between the x timestep and 0.
+        adv = torch.rand_like(ts.float())
+        t_prior = (adv * ts).long()
+        s_prior_diffused = diffuser.q_sample(s_prior, t_prior, torch.randn_like(s_prior))
+
+        self.preprocessed = (s_prior_diffused, t_prior, torch.tensor([resolution] * x.shape[0], dtype=torch.long, device=x.device))
         return s
 
-    def forward(self, x, timesteps, x_prior=None, resolution=None, conditioning_input=None, conditioning_free=False):
+    def forward(self, x, timesteps, prior_timesteps=None, x_prior=None, resolution=None, conditioning_input=None, conditioning_free=False):
+        """
+        Predicts the previous diffusion timestep of x, given a partially diffused low-resolution prior and a conditioning
+        input.
+
+        All parameters are optional because during training, input_to_random_resolution_and_window is used by a training
+        harness to preformat the inputs and fill in the parameters as state variables.
+
+        Args:
+            x: Prediction prior.
+            timesteps: Number of timesteps x has been diffused for.
+            prior_timesteps: Number of timesteps x_prior has been diffused for. Must be <= timesteps for each batch element.
+            x_prior: A low-resolution prior that guides the model.
+            resolution: Integer indicating the operating resolution level. '0' is the highest resolution.
+            conditioning_input: A semi-related (un-aligned) conditioning input which is used to guide diffusion. Similar to a class input, but hooked to a learned conditioning encoder.
+            conditioning_free: Whether or not to ignore the conditioning input.
+        """
         conditioning_input = x_prior if conditioning_input is None else conditioning_input
 
-        h = x
         if resolution is None:
             # This is assumed to be training.
             assert self.preprocessed is not None, 'Preprocessing function not called.'
             assert x_prior is None, 'Provided prior will not be used, instead preprocessing output will be used.'
-            h_sub, resolution = self.preprocessed
+            x_prior, prior_timesteps, resolution = self.preprocessed
             self.preprocessed = None
         else:
-            assert h.shape[-1] > x_prior.shape[-1] * 3.9, f'{h.shape} {x_prior.shape}'
+            assert x.shape[-1] > x_prior.shape[-1] * 3.9, f'{x.shape} {x_prior.shape}'
-            h_sub = F.interpolate(x_prior, size=(x.shape[-1],), mode='linear', align_corners=True)
+            x_prior = F.interpolate(x_prior, size=(x.shape[-1],), mode='linear', align_corners=True)
+        assert torch.all(timesteps - prior_timesteps > 0), f'Prior timesteps should always be lower (more resolved) than input timesteps. {timesteps}, {prior_timesteps}'
 
         if conditioning_free:
-            h_sub = self.unconditioned_prior.repeat(x.shape[0], 1, x.shape[-1])
+            code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
-            code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
         else:
             MIN_COND_LEN = 200
             MAX_COND_LEN = 1200
@@ -217,17 +242,17 @@ class TransformerDiffusion(nn.Module):
 
         # Mask out the conditioning input and x_prior inputs for whole batch elements, implementing something similar to classifier-free guidance.
         if self.training and self.unconditioned_percentage > 0:
-            unconditioned_batches = torch.rand((h.shape[0], 1, 1),
+            unconditioned_batches = torch.rand((x.shape[0], 1, 1),
-                                               device=h.device) < self.unconditioned_percentage
+                                               device=x.device) < self.unconditioned_percentage
-            h_sub = torch.where(unconditioned_batches, self.unconditioned_prior.repeat(h_sub.shape[0], 1, h_sub.shape[-1]), h_sub)
             code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(code_emb.shape[0], 1, 1), code_emb)
 
         with torch.autocast(x.device.type, enabled=self.enable_fp16):
             time_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
+            prior_time_emb = self.prior_time_embed(timestep_embedding(prior_timesteps, self.time_embed_dim))
             res_emb = self.resolution_embed(resolution)
-            blk_emb = torch.cat([time_emb.unsqueeze(-1), res_emb.unsqueeze(-1), code_emb], dim=-1)
+            blk_emb = torch.cat([time_emb.unsqueeze(-1), prior_time_emb.unsqueeze(-1), res_emb.unsqueeze(-1), code_emb], dim=-1)
 
-            h = torch.cat([h, h_sub], dim=1)
+            h = torch.cat([x, x_prior], dim=1)
             h = self.inp_block(h)
             for layer in self.layers:
                 h = checkpoint(layer, h, blk_emb)
@@ -236,7 +261,7 @@ class TransformerDiffusion(nn.Module):
         out = self.out(h)
 
         # Defensively involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
-        unused_params = [self.unconditioned_prior, self.unconditioned_embedding]
+        unused_params = [self.unconditioned_embedding]
         extraneous_addition = 0
         for p in unused_params:
             extraneous_addition = extraneous_addition + p.mean()
@@ -251,6 +276,12 @@ def register_transformer_diffusion13(opt_net, opt):
 
 
 def test_tfd():
+    from models.diffusion.respace import SpacedDiffusion
+    from models.diffusion.respace import space_timesteps
+    from models.diffusion.gaussian_diffusion import get_named_beta_schedule
+    diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [4000]), model_mean_type='epsilon',
+                    model_var_type='learned_range', loss_type='mse',
+                    betas=get_named_beta_schedule('linear', 4000))
     clip = torch.randn(2,256,10336)
     cond = torch.randn(2,256,10336)
     ts = torch.LongTensor([600, 600])
@@ -258,8 +289,8 @@ def test_tfd():
                                  num_heads=512//64, input_vec_dim=256, num_layers=12, dropout=.1,
                                  unconditioned_percentage=.6)
     for k in range(100):
-        x = model.input_to_random_resolution_and_window(clip, x_prior=clip)
+        x = model.input_to_random_resolution_and_window(clip, ts, diffuser)
-        model(x, ts, clip)
+        model(x, ts, conditioning_input=cond)
 
 
 def remove_conditioning(sd_path):

codes/trainer/injectors/gaussian_diffusion_injector.py

@@ -89,10 +89,10 @@ class GaussianDiffusionInjector(Injector):
                 sampler = self.schedule_sampler
                 self.deterministic_sampler.reset()  # Keep this reset whenever it is not being used, so it is ready to use automatically.
             model_inputs = {k: state[v] if isinstance(v, str) else v for k, v in self.model_input_keys.items()}
-            if self.preprocess_fn is not None:
-                hq = getattr(gen.module, self.preprocess_fn)(hq)
-
             t, weights = sampler.sample(hq.shape[0], hq.device)
+
+            if self.preprocess_fn is not None:
+                hq = getattr(gen.module, self.preprocess_fn)(hq, t, self.diffusion)
             if self.causal_mode:
                 cs, ce = self.causal_slope_range
                 slope = random.random() * (ce-cs) + cs