Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

Pad conditionings using zeros and encoder_attention_mask

Browse Source
This commit is contained in:
Sergey Borisov 2023-07-15 00:52:54 +03:00
parent 565299c7a1
commit 7093e5d033
2 changed files with 36 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
invokeai/app/invocations/compel.py
View File

@ -100,7 +100,7 @@ class CompelInvocation(BaseInvocation):
text_encoder=text_encoder,
textual_inversion_manager=ti_manager,
dtype_for_device_getter=torch_dtype,
truncate_long_prompts=True, # TODO:
truncate_long_prompts=False,
)
conjunction = Compel.parse_prompt_string(self.prompt)
@ -112,9 +112,6 @@ class CompelInvocation(BaseInvocation):
c, options = compel.build_conditioning_tensor_for_prompt_object(
prompt)
# TODO: long prompt support
# if not self.truncate_long_prompts:
# [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
tokens_count_including_eos_bos=get_max_token_count(
tokenizer, conjunction),

36
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -241,11 +241,45 @@ class InvokeAIDiffuserComponent:
def _apply_standard_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs):
# fast batched path
def _pad_conditioning(cond, target_len, encoder_attention_mask):
conditioning_attention_mask = torch.ones((cond.shape[0], cond.shape[1]), device=cond.device, dtype=cond.dtype)
if cond.shape[1] < max_len:
conditioning_attention_mask = torch.cat([
conditioning_attention_mask,
torch.zeros((cond.shape[0], max_len - cond.shape[1]), device=cond.device, dtype=cond.dtype),
], dim=1)
cond = torch.cat([
cond,
torch.zeros((cond.shape[0], max_len - cond.shape[1], cond.shape[2]), device=cond.device, dtype=cond.dtype),
], dim=1)
if encoder_attention_mask is None:
encoder_attention_mask = conditioning_attention_mask
else:
encoder_attention_mask = torch.cat([
encoder_attention_mask,
conditioning_attention_mask,
])
return cond, encoder_attention_mask
x_twice = torch.cat([x] * 2)
sigma_twice = torch.cat([sigma] * 2)
encoder_attention_mask = None
if unconditioning.shape[1] != conditioning.shape[1]:
max_len = max(unconditioning.shape[1], conditioning.shape[1])
unconditioning, encoder_attention_mask = _pad_conditioning(unconditioning, max_len, encoder_attention_mask)
conditioning, encoder_attention_mask = _pad_conditioning(conditioning, max_len, encoder_attention_mask)
both_conditionings = torch.cat([unconditioning, conditioning])
both_results = self.model_forward_callback(
x_twice, sigma_twice, both_conditionings, **kwargs,
x_twice, sigma_twice, both_conditionings,
encoder_attention_mask=encoder_attention_mask,
**kwargs,
)
unconditioned_next_x, conditioned_next_x = both_results.chunk(2)
return unconditioned_next_x, conditioned_next_x