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fix IPAdapter and PuLID compatibility
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matt3o committed May 8, 2024
1 parent b772a18 commit d8c70a0
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367 changes: 178 additions & 189 deletions CrossAttentionPatch.py
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from comfy.ldm.modules.attention import optimized_attention
from .utils import tensor_to_size

class CrossAttentionPatch:
# forward for patching
def __init__(self, ipadapter=None, number=0, weight=1.0, cond=None, cond_alt=None, uncond=None, weight_type="linear", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False, embeds_scaling='V only'):
self.weights = [weight]
self.ipadapters = [ipadapter]
self.conds = [cond]
self.conds_alt = [cond_alt]
self.unconds = [uncond]
self.weight_types = [weight_type]
self.masks = [mask]
self.sigma_starts = [sigma_start]
self.sigma_ends = [sigma_end]
self.unfold_batch = [unfold_batch]
self.embeds_scaling = [embeds_scaling]
self.number = number
self.layers = 11 if '101_to_k_ip' in ipadapter.ip_layers.to_kvs else 16 # TODO: check if this is a valid condition to detect all models

self.k_key = str(self.number*2+1) + "_to_k_ip"
self.v_key = str(self.number*2+1) + "_to_v_ip"

def set_new_condition(self, ipadapter=None, number=0, weight=1.0, cond=None, cond_alt=None, uncond=None, weight_type="linear", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False, embeds_scaling='V only'):
self.weights.append(weight)
self.ipadapters.append(ipadapter)
self.conds.append(cond)
self.conds_alt.append(cond_alt)
self.unconds.append(uncond)
self.weight_types.append(weight_type)
self.masks.append(mask)
self.sigma_starts.append(sigma_start)
self.sigma_ends.append(sigma_end)
self.unfold_batch.append(unfold_batch)
self.embeds_scaling.append(embeds_scaling)
class Attn2Replace:
def __init__(self, callback=None, **kwargs):
self.callback = [callback]
self.kwargs = [kwargs]

def add(self, callback, **kwargs):
self.callback.append(callback)
self.kwargs.append(kwargs)

for key, value in kwargs.items():
setattr(self, key, value)

def __call__(self, q, k, v, extra_options):
dtype = q.dtype
cond_or_uncond = extra_options["cond_or_uncond"]
sigma = extra_options["sigmas"].detach().cpu()[0].item() if 'sigmas' in extra_options else 999999999.9
block_type = extra_options["block"][0]
#block_id = extra_options["block"][1]
t_idx = extra_options["transformer_index"]

# extra options for AnimateDiff
ad_params = extra_options['ad_params'] if "ad_params" in extra_options else None

b = q.shape[0]
seq_len = q.shape[1]
batch_prompt = b // len(cond_or_uncond)
out = optimized_attention(q, k, v, extra_options["n_heads"])
_, _, oh, ow = extra_options["original_shape"]

for weight, cond, cond_alt, uncond, ipadapter, mask, weight_type, sigma_start, sigma_end, unfold_batch, embeds_scaling in zip(self.weights, self.conds, self.conds_alt, self.unconds, self.ipadapters, self.masks, self.weight_types, self.sigma_starts, self.sigma_ends, self.unfold_batch, self.embeds_scaling):
if sigma <= sigma_start and sigma >= sigma_end:
if weight_type == 'ease in':
weight = weight * (0.05 + 0.95 * (1 - t_idx / self.layers))
elif weight_type == 'ease out':
weight = weight * (0.05 + 0.95 * (t_idx / self.layers))
elif weight_type == 'ease in-out':
weight = weight * (0.05 + 0.95 * (1 - abs(t_idx - (self.layers/2)) / (self.layers/2)))
elif weight_type == 'reverse in-out':
weight = weight * (0.05 + 0.95 * (abs(t_idx - (self.layers/2)) / (self.layers/2)))
elif weight_type == 'weak input' and block_type == 'input':
weight = weight * 0.2
elif weight_type == 'weak middle' and block_type == 'middle':
weight = weight * 0.2
elif weight_type == 'weak output' and block_type == 'output':
weight = weight * 0.2
elif weight_type == 'strong middle' and (block_type == 'input' or block_type == 'output'):
weight = weight * 0.2
elif isinstance(weight, dict):
if t_idx not in weight:
continue

weight = weight[t_idx]

if cond_alt is not None and t_idx in cond_alt:
cond = cond_alt[t_idx]
del cond_alt

if unfold_batch:
# Check AnimateDiff context window
if ad_params is not None and ad_params["sub_idxs"] is not None:
if isinstance(weight, torch.Tensor):
weight = tensor_to_size(weight, ad_params["full_length"])
weight = torch.Tensor(weight[ad_params["sub_idxs"]])
if torch.all(weight == 0):
continue
weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond
elif weight == 0:
continue

# if image length matches or exceeds full_length get sub_idx images
if cond.shape[0] >= ad_params["full_length"]:
cond = torch.Tensor(cond[ad_params["sub_idxs"]])
uncond = torch.Tensor(uncond[ad_params["sub_idxs"]])
# otherwise get sub_idxs images
else:
cond = tensor_to_size(cond, ad_params["full_length"])
uncond = tensor_to_size(uncond, ad_params["full_length"])
cond = cond[ad_params["sub_idxs"]]
uncond = uncond[ad_params["sub_idxs"]]
else:
if isinstance(weight, torch.Tensor):
weight = tensor_to_size(weight, batch_prompt)
if torch.all(weight == 0):
continue
weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond
elif weight == 0:
continue

cond = tensor_to_size(cond, batch_prompt)
uncond = tensor_to_size(uncond, batch_prompt)

k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond)
k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond)
v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond)
v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond)
else:
# TODO: should we always convert the weights to a tensor?
if isinstance(weight, torch.Tensor):
weight = tensor_to_size(weight, batch_prompt)
if torch.all(weight == 0):
continue
weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond
elif weight == 0:
continue

k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond).repeat(batch_prompt, 1, 1)
k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond).repeat(batch_prompt, 1, 1)
v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond).repeat(batch_prompt, 1, 1)
v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond).repeat(batch_prompt, 1, 1)

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 embeds_scaling == 'K+mean(V) w/ C penalty':
scaling = float(ip_k.shape[2]) / 1280.0
weight = weight * scaling
ip_k = ip_k * weight
ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True)
ip_v = (ip_v - ip_v_mean) + ip_v_mean * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
del ip_v_mean
elif embeds_scaling == 'K+V w/ C penalty':
scaling = float(ip_k.shape[2]) / 1280.0
weight = weight * scaling
ip_k = ip_k * weight
ip_v = ip_v * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
elif embeds_scaling == 'K+V':
ip_k = ip_k * weight
ip_v = ip_v * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
else:
#ip_v = ip_v * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
out_ip = out_ip * weight # I'm doing this to get the same results as before

if mask is not None:
mask_h = oh / math.sqrt(oh * ow / seq_len)
mask_h = int(mask_h) + int((seq_len % int(mask_h)) != 0)
mask_w = seq_len // mask_h

# check if using AnimateDiff and sliding context window
if (mask.shape[0] > 1 and ad_params is not None and ad_params["sub_idxs"] is not None):
# if mask length matches or exceeds full_length, get sub_idx masks
if mask.shape[0] >= ad_params["full_length"]:
mask = torch.Tensor(mask[ad_params["sub_idxs"]])
mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
else:
mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
mask = tensor_to_size(mask, ad_params["full_length"])
mask = mask[ad_params["sub_idxs"]]
else:
mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
mask = tensor_to_size(mask, batch_prompt)

mask = mask.repeat(len(cond_or_uncond), 1, 1)
mask = mask.view(mask.shape[0], -1, 1).repeat(1, 1, out.shape[2])

# covers cases where extreme aspect ratios can cause the mask to have a wrong size
mask_len = mask_h * mask_w
if mask_len < seq_len:
pad_len = seq_len - mask_len
pad1 = pad_len // 2
pad2 = pad_len - pad1
mask = F.pad(mask, (0, 0, pad1, pad2), value=0.0)
elif mask_len > seq_len:
crop_start = (mask_len - seq_len) // 2
mask = mask[:, crop_start:crop_start+seq_len, :]

out_ip = out_ip * mask

out = out + out_ip
sigma = extra_options["sigmas"].detach().cpu()[0].item() if 'sigmas' in extra_options else 999999999.9

for i, callback in enumerate(self.callback):
if sigma <= self.kwargs[i]["sigma_start"] and sigma >= self.kwargs[i]["sigma_end"]:
out = out + callback(out, q, k, v, extra_options, **self.kwargs[i])

return out.to(dtype=dtype)

def instantid_attention(out, q, k, v, extra_options, module_key='', ipadapter=None, weight=1.0, cond=None, cond_alt=None, uncond=None, weight_type="linear", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False, embeds_scaling='V only', **kwargs):
dtype = q.dtype
cond_or_uncond = extra_options["cond_or_uncond"]
block_type = extra_options["block"][0]
#block_id = extra_options["block"][1]
t_idx = extra_options["transformer_index"]
layers = 11 if '101_to_k_ip' in ipadapter.ip_layers.to_kvs else 16
k_key = module_key + "_to_k_ip"
v_key = module_key + "_to_v_ip"

# extra options for AnimateDiff
ad_params = extra_options['ad_params'] if "ad_params" in extra_options else None

b = q.shape[0]
seq_len = q.shape[1]
batch_prompt = b // len(cond_or_uncond)
_, _, oh, ow = extra_options["original_shape"]

if weight_type == 'ease in':
weight = weight * (0.05 + 0.95 * (1 - t_idx / layers))
elif weight_type == 'ease out':
weight = weight * (0.05 + 0.95 * (t_idx / layers))
elif weight_type == 'ease in-out':
weight = weight * (0.05 + 0.95 * (1 - abs(t_idx - (layers/2)) / (layers/2)))
elif weight_type == 'reverse in-out':
weight = weight * (0.05 + 0.95 * (abs(t_idx - (layers/2)) / (layers/2)))
elif weight_type == 'weak input' and block_type == 'input':
weight = weight * 0.2
elif weight_type == 'weak middle' and block_type == 'middle':
weight = weight * 0.2
elif weight_type == 'weak output' and block_type == 'output':
weight = weight * 0.2
elif weight_type == 'strong middle' and (block_type == 'input' or block_type == 'output'):
weight = weight * 0.2
elif isinstance(weight, dict):
if t_idx not in weight:
return 0

weight = weight[t_idx]

if cond_alt is not None and t_idx in cond_alt:
cond = cond_alt[t_idx]
del cond_alt

if unfold_batch:
# Check AnimateDiff context window
if ad_params is not None and ad_params["sub_idxs"] is not None:
if isinstance(weight, torch.Tensor):
weight = tensor_to_size(weight, ad_params["full_length"])
weight = torch.Tensor(weight[ad_params["sub_idxs"]])
if torch.all(weight == 0):
return 0
weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond
elif weight == 0:
return 0

# if image length matches or exceeds full_length get sub_idx images
if cond.shape[0] >= ad_params["full_length"]:
cond = torch.Tensor(cond[ad_params["sub_idxs"]])
uncond = torch.Tensor(uncond[ad_params["sub_idxs"]])
# otherwise get sub_idxs images
else:
cond = tensor_to_size(cond, ad_params["full_length"])
uncond = tensor_to_size(uncond, ad_params["full_length"])
cond = cond[ad_params["sub_idxs"]]
uncond = uncond[ad_params["sub_idxs"]]
else:
if isinstance(weight, torch.Tensor):
weight = tensor_to_size(weight, batch_prompt)
if torch.all(weight == 0):
return 0
weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond
elif weight == 0:
return 0

cond = tensor_to_size(cond, batch_prompt)
uncond = tensor_to_size(uncond, batch_prompt)

k_cond = ipadapter.ip_layers.to_kvs[k_key](cond)
k_uncond = ipadapter.ip_layers.to_kvs[k_key](uncond)
v_cond = ipadapter.ip_layers.to_kvs[v_key](cond)
v_uncond = ipadapter.ip_layers.to_kvs[v_key](uncond)
else:
# TODO: should we always convert the weights to a tensor?
if isinstance(weight, torch.Tensor):
weight = tensor_to_size(weight, batch_prompt)
if torch.all(weight == 0):
return 0
weight = weight.repeat(len(cond_or_uncond), 1, 1) # repeat for cond and uncond
elif weight == 0:
return 0

k_cond = ipadapter.ip_layers.to_kvs[k_key](cond).repeat(batch_prompt, 1, 1)
k_uncond = ipadapter.ip_layers.to_kvs[k_key](uncond).repeat(batch_prompt, 1, 1)
v_cond = ipadapter.ip_layers.to_kvs[v_key](cond).repeat(batch_prompt, 1, 1)
v_uncond = ipadapter.ip_layers.to_kvs[v_key](uncond).repeat(batch_prompt, 1, 1)

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 embeds_scaling == 'K+mean(V) w/ C penalty':
scaling = float(ip_k.shape[2]) / 1280.0
weight = weight * scaling
ip_k = ip_k * weight
ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True)
ip_v = (ip_v - ip_v_mean) + ip_v_mean * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
del ip_v_mean
elif embeds_scaling == 'K+V w/ C penalty':
scaling = float(ip_k.shape[2]) / 1280.0
weight = weight * scaling
ip_k = ip_k * weight
ip_v = ip_v * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
elif embeds_scaling == 'K+V':
ip_k = ip_k * weight
ip_v = ip_v * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
else:
#ip_v = ip_v * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
out_ip = out_ip * weight # I'm doing this to get the same results as before

if mask is not None:
mask_h = oh / math.sqrt(oh * ow / seq_len)
mask_h = int(mask_h) + int((seq_len % int(mask_h)) != 0)
mask_w = seq_len // mask_h

# check if using AnimateDiff and sliding context window
if (mask.shape[0] > 1 and ad_params is not None and ad_params["sub_idxs"] is not None):
# if mask length matches or exceeds full_length, get sub_idx masks
if mask.shape[0] >= ad_params["full_length"]:
mask = torch.Tensor(mask[ad_params["sub_idxs"]])
mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
else:
mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
mask = tensor_to_size(mask, ad_params["full_length"])
mask = mask[ad_params["sub_idxs"]]
else:
mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
mask = tensor_to_size(mask, batch_prompt)

mask = mask.repeat(len(cond_or_uncond), 1, 1)
mask = mask.view(mask.shape[0], -1, 1).repeat(1, 1, out.shape[2])

# covers cases where extreme aspect ratios can cause the mask to have a wrong size
mask_len = mask_h * mask_w
if mask_len < seq_len:
pad_len = seq_len - mask_len
pad1 = pad_len // 2
pad2 = pad_len - pad1
mask = F.pad(mask, (0, 0, pad1, pad2), value=0.0)
elif mask_len > seq_len:
crop_start = (mask_len - seq_len) // 2
mask = mask[:, crop_start:crop_start+seq_len, :]

out_ip = out_ip * mask

#out = out + out_ip

return out_ip.to(dtype=dtype)
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