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sgm/modules/diffusionmodules/guiders.py

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+from functools import partial
+
+import torch
+
+from ...util import default, instantiate_from_config
+
+
+class VanillaCFG:
+    """
+    implements parallelized CFG
+    """
+
+    def __init__(self, scale, dyn_thresh_config=None):
+        scale_schedule = lambda scale, sigma: scale  # independent of step
+        self.scale_schedule = partial(scale_schedule, scale)
+        self.dyn_thresh = instantiate_from_config(
+            default(
+                dyn_thresh_config,
+                {
+                    "target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
+                },
+            )
+        )
+
+    def __call__(self, x, sigma):
+        x_u, x_c = x.chunk(2)
+        scale_value = self.scale_schedule(sigma)
+        x_pred = self.dyn_thresh(x_u, x_c, scale_value)
+        return x_pred
+
+    def prepare_inputs(self, x, s, c, uc):
+        c_out = dict()
+
+        for k in c:
+            if k in ["vector", "crossattn", "concat"]:
+                c_out[k] = torch.cat((uc[k], c[k]), 0)
+            else:
+                assert c[k] == uc[k]
+                c_out[k] = c[k]
+        return torch.cat([x] * 2), torch.cat([s] * 2), c_out
+
+
+class IdentityGuider:
+    def __call__(self, x, sigma):
+        return x
+
+    def prepare_inputs(self, x, s, c, uc):
+        c_out = dict()
+
+        for k in c:
+            c_out[k] = c[k]
+
+        return x, s, c_out