import torch import math import struct import comfy.checkpoint_pickle import safetensors.torch def load_torch_file(ckpt, safe_load=False): if ckpt.lower().endswith(".safetensors"): sd = safetensors.torch.load_file(ckpt, device="cpu") else: if safe_load: if not 'weights_only' in torch.load.__code__.co_varnames: print("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.") safe_load = False if safe_load: pl_sd = torch.load(ckpt, map_location="cpu", weights_only=True) else: pl_sd = torch.load(ckpt, map_location="cpu", pickle_module=comfy.checkpoint_pickle) if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: sd = pl_sd return sd def save_torch_file(sd, ckpt, metadata=None): if metadata is not None: safetensors.torch.save_file(sd, ckpt, metadata=metadata) else: safetensors.torch.save_file(sd, ckpt) def transformers_convert(sd, prefix_from, prefix_to, number): keys_to_replace = { "{}positional_embedding": "{}embeddings.position_embedding.weight", "{}token_embedding.weight": "{}embeddings.token_embedding.weight", "{}ln_final.weight": "{}final_layer_norm.weight", "{}ln_final.bias": "{}final_layer_norm.bias", } for k in keys_to_replace: x = k.format(prefix_from) if x in sd: sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x) resblock_to_replace = { "ln_1": "layer_norm1", "ln_2": "layer_norm2", "mlp.c_fc": "mlp.fc1", "mlp.c_proj": "mlp.fc2", "attn.out_proj": "self_attn.out_proj", } for resblock in range(number): for x in resblock_to_replace: for y in ["weight", "bias"]: k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y) k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y) if k in sd: sd[k_to] = sd.pop(k) for y in ["weight", "bias"]: k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y) if k_from in sd: weights = sd.pop(k_from) shape_from = weights.shape[0] // 3 for x in range(3): p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"] k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y) sd[k_to] = weights[shape_from*x:shape_from*(x + 1)] return sd UNET_MAP_ATTENTIONS = { "proj_in.weight", "proj_in.bias", "proj_out.weight", "proj_out.bias", "norm.weight", "norm.bias", } TRANSFORMER_BLOCKS = { "norm1.weight", "norm1.bias", "norm2.weight", "norm2.bias", "norm3.weight", "norm3.bias", "attn1.to_q.weight", "attn1.to_k.weight", "attn1.to_v.weight", "attn1.to_out.0.weight", "attn1.to_out.0.bias", "attn2.to_q.weight", "attn2.to_k.weight", "attn2.to_v.weight", "attn2.to_out.0.weight", "attn2.to_out.0.bias", "ff.net.0.proj.weight", "ff.net.0.proj.bias", "ff.net.2.weight", "ff.net.2.bias", } UNET_MAP_RESNET = { "in_layers.2.weight": "conv1.weight", "in_layers.2.bias": "conv1.bias", "emb_layers.1.weight": "time_emb_proj.weight", "emb_layers.1.bias": "time_emb_proj.bias", "out_layers.3.weight": "conv2.weight", "out_layers.3.bias": "conv2.bias", "skip_connection.weight": "conv_shortcut.weight", "skip_connection.bias": "conv_shortcut.bias", "in_layers.0.weight": "norm1.weight", "in_layers.0.bias": "norm1.bias", "out_layers.0.weight": "norm2.weight", "out_layers.0.bias": "norm2.bias", } def unet_to_diffusers(unet_config): num_res_blocks = unet_config["num_res_blocks"] attention_resolutions = unet_config["attention_resolutions"] channel_mult = unet_config["channel_mult"] transformer_depth = unet_config["transformer_depth"] num_blocks = len(channel_mult) if not isinstance(num_res_blocks, list): num_res_blocks = [num_res_blocks] * num_blocks transformers_per_layer = [] res = 1 for i in range(num_blocks): transformers = 0 if res in attention_resolutions: transformers = transformer_depth[i] transformers_per_layer.append(transformers) res *= 2 transformers_mid = unet_config.get("transformer_depth_middle", transformers_per_layer[-1]) diffusers_unet_map = {} for x in range(num_blocks): n = 1 + (num_res_blocks[x] + 1) * x for i in range(num_res_blocks[x]): for b in UNET_MAP_RESNET: diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b) if transformers_per_layer[x] > 0: for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b) for t in range(transformers_per_layer[x]): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) n += 1 for k in ["weight", "bias"]: diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k) i = 0 for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b) for t in range(transformers_mid): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) for i, n in enumerate([0, 2]): for b in UNET_MAP_RESNET: diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b) num_res_blocks = list(reversed(num_res_blocks)) transformers_per_layer = list(reversed(transformers_per_layer)) for x in range(num_blocks): n = (num_res_blocks[x] + 1) * x l = num_res_blocks[x] + 1 for i in range(l): c = 0 for b in UNET_MAP_RESNET: diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b) c += 1 if transformers_per_layer[x] > 0: c += 1 for b in UNET_MAP_ATTENTIONS: diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b) for t in range(transformers_per_layer[x]): for b in TRANSFORMER_BLOCKS: diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) if i == l - 1: for k in ["weight", "bias"]: diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k) n += 1 return diffusers_unet_map def convert_sd_to(state_dict, dtype): keys = list(state_dict.keys()) for k in keys: state_dict[k] = state_dict[k].to(dtype) return state_dict def safetensors_header(safetensors_path, max_size=100*1024*1024): with open(safetensors_path, "rb") as f: header = f.read(8) length_of_header = struct.unpack(' max_size: return None return f.read(length_of_header) def bislerp(samples, width, height): def slerp(b1, b2, r): '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' c = b1.shape[-1] #norms b1_norms = torch.norm(b1, dim=-1, keepdim=True) b2_norms = torch.norm(b2, dim=-1, keepdim=True) #normalize b1_normalized = b1 / b1_norms b2_normalized = b2 / b2_norms #zero when norms are zero b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0 b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0 #slerp dot = (b1_normalized*b2_normalized).sum(1) omega = torch.acos(dot) so = torch.sin(omega) #technically not mathematically correct, but more pleasing? res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c) #edge cases for same or polar opposites res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1] return res def generate_bilinear_data(length_old, length_new): coords_1 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32) coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear") ratios = coords_1 - coords_1.floor() coords_1 = coords_1.to(torch.int64) coords_2 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32) + 1 coords_2[:,:,:,-1] -= 1 coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") coords_2 = coords_2.to(torch.int64) return ratios, coords_1, coords_2 n,c,h,w = samples.shape h_new, w_new = (height, width) #linear w ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new) coords_1 = coords_1.expand((n, c, h, -1)) coords_2 = coords_2.expand((n, c, h, -1)) ratios = ratios.expand((n, 1, h, -1)) pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c)) pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c)) ratios = ratios.movedim(1, -1).reshape((-1,1)) result = slerp(pass_1, pass_2, ratios) result = result.reshape(n, h, w_new, c).movedim(-1, 1) #linear h ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new) coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new)) pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c)) pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c)) ratios = ratios.movedim(1, -1).reshape((-1,1)) result = slerp(pass_1, pass_2, ratios) result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) return result def common_upscale(samples, width, height, upscale_method, crop): if crop == "center": old_width = samples.shape[3] old_height = samples.shape[2] old_aspect = old_width / old_height new_aspect = width / height x = 0 y = 0 if old_aspect > new_aspect: x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) elif old_aspect < new_aspect: y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) s = samples[:,:,y:old_height-y,x:old_width-x] else: s = samples if upscale_method == "bislerp": return bislerp(s, width, height) else: return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap))) @torch.inference_mode() def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, pbar = None): output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device="cpu") for b in range(samples.shape[0]): s = samples[b:b+1] out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu") out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu") for y in range(0, s.shape[2], tile_y - overlap): for x in range(0, s.shape[3], tile_x - overlap): s_in = s[:,:,y:y+tile_y,x:x+tile_x] ps = function(s_in).cpu() mask = torch.ones_like(ps) feather = round(overlap * upscale_amount) for t in range(feather): mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask if pbar is not None: pbar.update(1) output[b:b+1] = out/out_div return output PROGRESS_BAR_HOOK = None def set_progress_bar_global_hook(function): global PROGRESS_BAR_HOOK PROGRESS_BAR_HOOK = function class ProgressBar: def __init__(self, total): global PROGRESS_BAR_HOOK self.total = total self.current = 0 self.hook = PROGRESS_BAR_HOOK def update_absolute(self, value, total=None, preview=None): if total is not None: self.total = total if value > self.total: value = self.total self.current = value if self.hook is not None: self.hook(self.current, self.total, preview) def update(self, value): self.update_absolute(self.current + value)