nodes/cubiq-ComfyUI_IPAdapter_plus
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fix regression

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matt3o
2023-11-07 20:08:38 +01:00
parent d723aeb8c9
commit 4fefd8b9e5
1 changed files with 11 additions and 15 deletions
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IPAdapterPlus.py
+11 -15
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@@ -199,16 +199,17 @@ class CrossAttentionPatch:
self.dtype = dtype
self.device = 'cuda'
self.number = number
self.weight_type = weight_type
self.weight_type = [weight_type]
self.masks = [mask]
def set_new_condition(self, weight, ipadapter, cond, uncond, dtype, number, mask=None):
def set_new_condition(self, weight, ipadapter, cond, uncond, dtype, number, weight_type, mask=None):
self.weights.append(weight)
self.ipadapters.append(ipadapter)
self.conds.append(cond)
self.unconds.append(uncond)
self.masks.append(mask)
self.dtype = dtype
self.weight_type.append(weight_type)
self.device = 'cuda'
def __call__(self, n, context_attn2, value_attn2, extra_options):
@@ -223,20 +224,20 @@ class CrossAttentionPatch:
out = optimized_attention(q, k, v, extra_options["n_heads"])
k = v = []
for weight, cond, uncond, ipadapter, mask in zip(self.weights, self.conds, self.unconds, self.ipadapters, self.masks):
for weight, cond, uncond, ipadapter, mask, weight_type in zip(self.weights, self.conds, self.unconds, self.ipadapters, self.masks, self.weight_type):
k_cond = ipadapter.ip_layers.to_kvs[self.number*2](cond).repeat(batch_prompt, 1, 1)
k_uncond = ipadapter.ip_layers.to_kvs[self.number*2](uncond).repeat(batch_prompt, 1, 1)
v_cond = ipadapter.ip_layers.to_kvs[self.number*2+1](cond).repeat(batch_prompt, 1, 1)
v_uncond = ipadapter.ip_layers.to_kvs[self.number*2+1](uncond).repeat(batch_prompt, 1, 1)
if self.weight_type.startswith("linear"):
if weight_type.startswith("linear"):
ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0) * weight
ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0) * weight
else:
ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0)
ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0)
if self.weight_type.startswith("channel"):
if weight_type.startswith("channel"):
# code by Lvmin Zhang at Stanford University as also seen on Fooocus IPAdapter implementation
# please read licensing notes https://github.com/lllyasviel/Fooocus/blob/main/fooocus_extras/ip_adapter.py#L225
ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True)
@@ -247,16 +248,11 @@ class CrossAttentionPatch:
ip_k = ip_k * W
ip_v = ip_v_offset + ip_v_mean * W
k.append(ip_k)
v.append(ip_v)
k = torch.cat(k, dim=1)
v = torch.cat(v, dim=1)
if self.weight_type.startswith("original"):
out += optimized_attention(q, ip_k, ip_v, extra_options["n_heads"]) * weight
else:
out += optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
if weight_type.startswith("original"):
out_ip = out_ip * weight
out = out + out_ip
return out.to(dtype=org_dtype)