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https://github.com/invoke-ai/InvokeAI
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Merge remote-tracking branch 'origin/main' into dev/diffusers
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061c5369a2
@ -114,7 +114,8 @@ class Generator:
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results.append([image, seed])
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results.append([image, seed])
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if image_callback is not None:
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if image_callback is not None:
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image_callback(image, seed, first_seed=first_seed, attention_maps_image=attention_maps_images[-1])
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attention_maps_image = None if len(attention_maps_images)==0 else attention_maps_images[-1]
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image_callback(image, seed, first_seed=first_seed, attention_maps_image=attention_maps_image)
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seed = self.new_seed()
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seed = self.new_seed()
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@ -281,7 +281,7 @@ class InvokeAICrossAttentionMixin:
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return self.einsum_op_slice_dim1(q, k, v, slice_size)
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return self.einsum_op_slice_dim1(q, k, v, slice_size)
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# fallback for when there is no saved strategy, or saved strategy does not slice
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# fallback for when there is no saved strategy, or saved strategy does not slice
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mem_free_total = self.cached_mem_free_total or get_mem_free_total(q.device)
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mem_free_total = get_mem_free_total(q.device)
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# Divide factor of safety as there's copying and fragmentation
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# Divide factor of safety as there's copying and fragmentation
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return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
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return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
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@ -1 +0,0 @@
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banana sushi -Ak_lms -S42 -s10
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@ -1 +1,3 @@
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banana sushi -Ak_lms -S42 -s10
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banana sushi -Ak_lms -S42 -s5
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banana sushi -Ak_heun -S42 -s5
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banana sushi -Addim -S42 -s5
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