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https://github.com/invoke-ai/InvokeAI
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save VRAM by not recombining tensors that have been sliced to save VRAM
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@ -151,72 +151,33 @@ class CrossAttentionControl:
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#else:
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# print(f"in wrangler, whole, use_last_attn_slice is {self.use_last_attn_slice}, save_last_attn_slice is {self.save_last_attn_slice}")
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if self.use_last_attn_slice:
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this_attn_slice = attn_slice
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if self.last_attn_slice_mask is not None:
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# indices and mask operate on dim=2, no need to slice
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base_attn_slice_full = torch.index_select(self.last_attn_slice, -1, self.last_attn_slice_indices)
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base_attn_slice_mask = self.last_attn_slice_mask
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if dim is None:
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base_attn_slice = base_attn_slice_full
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#print("using whole base slice of shape", base_attn_slice.shape, "from complete shape", base_attn_slice_full.shape)
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elif dim == 0:
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base_attn_slice = base_attn_slice_full[start:end]
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#print("using base dim 0 slice of shape", base_attn_slice.shape, "from complete shape", base_attn_slice_full.shape)
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elif dim == 1:
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base_attn_slice = base_attn_slice_full[:, start:end]
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#print("using base dim 1 slice of shape", base_attn_slice.shape, "from complete shape", base_attn_slice_full.shape)
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attn_slice = this_attn_slice * (1 - base_attn_slice_mask) + \
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base_attn_slice * base_attn_slice_mask
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else:
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if dim is None:
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attn_slice = self.last_attn_slice
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last_attn_slice = self.last_attn_slice
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# print("took whole slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
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elif dim == 0:
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attn_slice = self.last_attn_slice[start:end]
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#print("took dim 0 slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
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elif dim == 1:
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attn_slice = self.last_attn_slice[:, start:end]
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#print("took dim 1 slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
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else:
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last_attn_slice = self.last_attn_slice[offset]
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if self.last_attn_slice_mask is None:
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# just use everything
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attn_slice = last_attn_slice
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else:
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last_attn_slice_mask = self.last_attn_slice_mask
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remapped_last_attn_slice = torch.index_select(last_attn_slice, -1, self.last_attn_slice_indices)
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this_attn_slice = attn_slice
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this_attn_slice_mask = 1 - last_attn_slice_mask
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attn_slice = this_attn_slice * this_attn_slice_mask + \
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remapped_last_attn_slice * last_attn_slice_mask
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if self.save_last_attn_slice:
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if dim is None:
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self.last_attn_slice = attn_slice
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elif dim == 0:
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# dynamically grow last_attn_slice if needed
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if self.last_attn_slice is None:
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self.last_attn_slice = attn_slice
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#print("no last_attn_slice: shape now", self.last_attn_slice.shape)
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elif self.last_attn_slice.shape[0] == start:
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self.last_attn_slice = torch.cat([self.last_attn_slice, attn_slice], dim=0)
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assert(self.last_attn_slice.shape[0] == end)
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#print("last_attn_slice too small, appended dim 0 shape", attn_slice.shape, ", shape now", self.last_attn_slice.shape)
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else:
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# no need to grow
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self.last_attn_slice[start:end] = attn_slice
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#print("last_attn_slice shape is fine, setting dim 0 shape", attn_slice.shape, ", shape now", self.last_attn_slice.shape)
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elif dim == 1:
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# dynamically grow last_attn_slice if needed
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if self.last_attn_slice is None:
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self.last_attn_slice = attn_slice
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elif self.last_attn_slice.shape[1] == start:
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self.last_attn_slice = torch.cat([self.last_attn_slice, attn_slice], dim=1)
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assert(self.last_attn_slice.shape[1] == end)
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self.last_attn_slice = { offset: attn_slice }
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else:
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# no need to grow
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self.last_attn_slice[:, start:end] = attn_slice
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if self.use_last_attn_weights and self.last_attn_slice_weights is not None:
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if dim is None:
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weights = self.last_attn_slice_weights
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elif dim == 0:
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weights = self.last_attn_slice_weights[start:end]
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elif dim == 1:
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weights = self.last_attn_slice_weights[:, start:end]
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attn_slice = attn_slice * weights
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self.last_attn_slice[offset] = attn_slice
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return attn_slice
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