initial experiments

ldm/models/diffusion/ksampler.py

@@ -1,4 +1,6 @@
 """wrapper around part of Katherine Crowson's k-diffusion library, making it call compatible with other Samplers"""
+from enum import Enum
+
 import k_diffusion as K
 import torch
 import torch.nn as nn
@@ -25,6 +27,9 @@ def cfg_apply_threshold(result, threshold = 0.0, scale = 0.7):
         minval = max(min(-1, scale*minval), -threshold)
     return torch.clamp(result, min=minval, max=maxval)
 
+class AttentionLayer(Enum):
+    SELF = 1
+    TOKENS = 2
 
 class CFGDenoiser(nn.Module):
     def __init__(self, model, threshold = 0, warmup = 0):
@@ -34,11 +39,22 @@ class CFGDenoiser(nn.Module):
         self.warmup_max = warmup
         self.warmup = max(warmup / 10, 1)
 
+
+    def get_attention_module(self, which: AttentionLayer):
+        which_attn = "attn1" if which is AttentionLayer.SELF else "attn2"
+        module = next(module for name,module in self.inner_model.named_modules() if
+                      type(module).__name__ == "CrossAttention" and which_attn in name)
+        return module
+
+
     def forward(self, x, sigma, uncond, cond, cond_scale):
         x_in = torch.cat([x] * 2)
         sigma_in = torch.cat([sigma] * 2)
         cond_in = torch.cat([uncond, cond])
         uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
+
+        module = self.get_attention_module(AttentionLayer.TOKENS)
+
         if self.warmup < self.warmup_max:
             thresh = max(1, 1 + (self.threshold - 1) * (self.warmup / self.warmup_max))
             self.warmup += 1

ldm/models/diffusion/sampler.py

@@ -4,6 +4,8 @@ ldm.models.diffusion.sampler
 Base class for ldm.models.diffusion.ddim, ldm.models.diffusion.ksampler, etc
 
 '''
+from enum import Enum
+
 import torch
 import numpy as np
 from tqdm import tqdm
@@ -411,3 +413,6 @@ class Sampler(object):
         return self.model.inner_model.q_sample(x0,ts)
         '''
         return self.model.q_sample(x0,ts)
+
+
+