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bring in prompt parser from fix-prompts branch

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attention is parsed but ignored, blends old syntax doesn't work,
	  conjunctions are parsed but ignored, the only part that's used
	  here is the new .blend() syntax and cross-attention control
	  using .swap()
This commit is contained in:
Damian at mba
2022-10-20 12:01:48 +02:00
parent 42883545f9
commit c9d27634b4
10 changed files with 169 additions and 61 deletions
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6
ldm/models/diffusion/ddim.py
View File

@ -16,10 +16,10 @@ class DDIMSampler(Sampler):
def prepare_to_sample(self, t_enc, **kwargs):
super().prepare_to_sample(t_enc, **kwargs)
structured_conditioning = kwargs.get('structured_conditioning', None)
extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
if structured_conditioning is not None and structured_conditioning.wants_cross_attention_control:
self.invokeai_diffuser.setup_cross_attention_control(structured_conditioning)
if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info)
else:
self.invokeai_diffuser.remove_cross_attention_control()

14
ldm/models/diffusion/ksampler.py
View File

@ -34,10 +34,10 @@ class CFGDenoiser(nn.Module):
def prepare_to_sample(self, t_enc, **kwargs):
structured_conditioning = kwargs.get('structured_conditioning', None)
extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
if structured_conditioning is not None and structured_conditioning.wants_cross_attention_control:
self.invokeai_diffuser.setup_cross_attention_control(structured_conditioning)
if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info)
else:
self.invokeai_diffuser.remove_cross_attention_control()
@ -164,7 +164,7 @@ class KSampler(Sampler):
log_every_t=100,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
structured_conditioning=None,
extra_conditioning_info=None,
threshold = 0,
perlin = 0,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
@ -197,7 +197,7 @@ class KSampler(Sampler):
x = torch.randn([batch_size, *shape], device=self.device) * sigmas[0]
model_wrap_cfg = CFGDenoiser(self.model, threshold=threshold, warmup=max(0.8*S,S-10))
model_wrap_cfg.prepare_to_sample(S, structured_conditioning=structured_conditioning)
model_wrap_cfg.prepare_to_sample(S, extra_conditioning_info=extra_conditioning_info)
extra_args = {
'cond': conditioning,
'uncond': unconditional_conditioning,
@ -224,7 +224,7 @@ class KSampler(Sampler):
index,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
structured_conditioning=None,
extra_conditioning_info=None,
**kwargs,
):
if self.model_wrap is None:
@ -250,7 +250,7 @@ class KSampler(Sampler):
# so the actual formula for indexing into sigmas:
# sigma_index = (steps-index)
s_index = t_enc - index - 1
self.model_wrap.prepare_to_sample(s_index, structured_conditioning=structured_conditioning)
self.model_wrap.prepare_to_sample(s_index, extra_conditioning_info=extra_conditioning_info)
img = K.sampling.__dict__[f'_{self.schedule}'](
self.model_wrap,
img,

6
ldm/models/diffusion/plms.py
View File

@ -20,10 +20,10 @@ class PLMSSampler(Sampler):
def prepare_to_sample(self, t_enc, **kwargs):
super().prepare_to_sample(t_enc, **kwargs)
structured_conditioning = kwargs.get('structured_conditioning', None)
extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
if structured_conditioning is not None and structured_conditioning.wants_cross_attention_control:
self.invokeai_diffuser.setup_cross_attention_control(structured_conditioning)
if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info)
else:
self.invokeai_diffuser.remove_cross_attention_control()