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go back to using InvokeAI attention

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Damian at mba
2022-10-18 01:54:30 +02:00
parent 1fc1f8bf05
commit 37a204324b
3 changed files with 172 additions and 372 deletions
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6
ldm/models/diffusion/ksampler.py
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@ -48,21 +48,21 @@ class CFGDenoiser(nn.Module):
def forward(self, x, sigma, uncond, cond, cond_scale):
print('generating unconditioned latents')
#rint('generating unconditioned latents')
unconditioned_latents = self.inner_model(x, sigma, cond=uncond)
# process x using the original prompt, saving the attention maps if required
if self.edited_conditioning is not None:
# this is automatically toggled off after the model forward()
CrossAttentionControl.request_save_attention_maps(self.inner_model)
print('generating conditioned latents')
#print('generating conditioned latents')
conditioned_latents = self.inner_model(x, sigma, cond=cond)
if self.edited_conditioning is not None:
# process x again, using the saved attention maps but the new conditioning
# this is automatically toggled off after the model forward()
CrossAttentionControl.request_apply_saved_attention_maps(self.inner_model)
print('generating edited conditioned latents')
#print('generating edited conditioned latents')
conditioned_latents = self.inner_model(x, sigma, cond=self.edited_conditioning)
if self.warmup < self.warmup_max: