import torch import math import torch.nn.functional as F from comfy.ldm.modules.attention import optimized_attention from .utils import tensor_to_size 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 out = optimized_attention(q, k, v, extra_options["n_heads"]) 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 ipadapter_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 if weight_type == "style transfer precise": if layers == 11 and t_idx == 3: uncond = cond cond = cond * 0 elif layers == 16 and (t_idx == 4 or t_idx == 5): uncond = cond cond = cond * 0 elif weight_type == "composition precise": if layers == 11 and t_idx != 3: uncond = cond cond = cond * 0 elif layers == 16 and (t_idx != 4 and t_idx != 5): uncond = cond cond = cond * 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) if len(cond_or_uncond) == 3: # TODO: cosxl, I need to check this ip_k = torch.cat([(k_cond, k_uncond, k_cond)[i] for i in cond_or_uncond], dim=0) ip_v = torch.cat([(v_cond, v_uncond, v_cond)[i] for i in cond_or_uncond], dim=0) 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 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)