diff options
Diffstat (limited to 'modules/sd_hijack.py')
| -rw-r--r-- | modules/sd_hijack.py | 23 |
1 files changed, 21 insertions, 2 deletions
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index f07ec041..c81722a0 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -10,6 +10,7 @@ from torch.nn.functional import silu import modules.textual_inversion.textual_inversion
from modules import prompt_parser, devices, sd_hijack_optimizations, shared
from modules.shared import opts, device, cmd_opts
+from modules.sd_hijack_optimizations import invokeAI_mps_available
import ldm.modules.attention
import ldm.modules.diffusionmodules.model
@@ -30,8 +31,16 @@ def apply_optimizations(): elif cmd_opts.opt_split_attention_v1:
print("Applying v1 cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+ elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
+ if not invokeAI_mps_available and shared.device.type == 'mps':
+ print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
+ print("Applying v1 cross attention optimization.")
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+ else:
+ print("Applying cross attention optimization (InvokeAI).")
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
- print("Applying cross attention optimization.")
+ print("Applying cross attention optimization (Doggettx).")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
@@ -312,7 +321,17 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): fixes.append(fix[1])
self.hijack.fixes.append(fixes)
- z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
+ tokens = []
+ multipliers = []
+ for j in range(len(remade_batch_tokens)):
+ if len(remade_batch_tokens[j]) > 0:
+ tokens.append(remade_batch_tokens[j][:75])
+ multipliers.append(batch_multipliers[j][:75])
+ else:
+ tokens.append([self.wrapped.tokenizer.eos_token_id] * 75)
+ multipliers.append([1.0] * 75)
+
+ z1 = self.process_tokens(tokens, multipliers)
z = z1 if z is None else torch.cat((z, z1), axis=-2)
remade_batch_tokens = rem_tokens
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