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Delete unused functions from shared_invokeai_diffusion.py.

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Ryan Dick 2024-02-15 17:22:37 -05:00
parent 3e14bd6c45
commit a5c94fba43
1 changed files with 0 additions and 51 deletions
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51
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
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@ -592,54 +592,3 @@ class InvokeAIDiffuserComponent:
self.last_percent_through = percent_through self.last_percent_through = percent_through
return latents.to(device=dev) return latents.to(device=dev)
# todo: make this work
@classmethod
def apply_conjunction(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale):
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2) # aka sigmas
deltas = None
uncond_latents = None
weighted_cond_list = (
c_or_weighted_c_list if isinstance(c_or_weighted_c_list, list) else [(c_or_weighted_c_list, 1)]
)
# below is fugly omg
conditionings = [uc] + [c for c, weight in weighted_cond_list]
weights = [1] + [weight for c, weight in weighted_cond_list]
chunk_count = math.ceil(len(conditionings) / 2)
deltas = None
for chunk_index in range(chunk_count):
offset = chunk_index * 2
chunk_size = min(2, len(conditionings) - offset)
if chunk_size == 1:
c_in = conditionings[offset]
latents_a = forward_func(x_in[:-1], t_in[:-1], c_in)
latents_b = None
else:
c_in = torch.cat(conditionings[offset : offset + 2])
latents_a, latents_b = forward_func(x_in, t_in, c_in).chunk(2)
# first chunk is guaranteed to be 2 entries: uncond_latents + first conditioining
if chunk_index == 0:
uncond_latents = latents_a
deltas = latents_b - uncond_latents
else:
deltas = torch.cat((deltas, latents_a - uncond_latents))
if latents_b is not None:
deltas = torch.cat((deltas, latents_b - uncond_latents))
# merge the weighted deltas together into a single merged delta
per_delta_weights = torch.tensor(weights[1:], dtype=deltas.dtype, device=deltas.device)
normalize = False
if normalize:
per_delta_weights /= torch.sum(per_delta_weights)
reshaped_weights = per_delta_weights.reshape(per_delta_weights.shape + (1, 1, 1))
deltas_merged = torch.sum(deltas * reshaped_weights, dim=0, keepdim=True)
# old_return_value = super().forward(x, sigma, uncond, cond, cond_scale)
# assert(0 == len(torch.nonzero(old_return_value - (uncond_latents + deltas_merged * cond_scale))))
return uncond_latents + deltas_merged * global_guidance_scale