import torch import math def load_torch_file(ckpt, safe_load=False): if ckpt.lower().endswith(".safetensors"): import safetensors.torch sd = safetensors.torch.load_file(ckpt, device="cpu") else: if safe_load: pl_sd = torch.load(ckpt, map_location="cpu", weights_only=True) else: pl_sd = torch.load(ckpt, map_location="cpu") 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 transformers_convert(sd, prefix_from, prefix_to, number): 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 #slow and inefficient, should be optimized def bislerp(samples, width, height): shape = list(samples.shape) width_scale = (shape[3]) / (width ) height_scale = (shape[2]) / (height ) shape[3] = width shape[2] = height out1 = torch.empty(shape, dtype=samples.dtype, layout=samples.layout, device=samples.device) def algorithm(in1, in2, t): dims = in1.shape val = t #flatten to batches low = in1.reshape(dims[0], -1) high = in2.reshape(dims[0], -1) low_weight = torch.norm(low, dim=1, keepdim=True) low_weight[low_weight == 0] = 0.0000000001 low_norm = low/low_weight high_weight = torch.norm(high, dim=1, keepdim=True) high_weight[high_weight == 0] = 0.0000000001 high_norm = high/high_weight dot_prod = (low_norm*high_norm).sum(1) dot_prod[dot_prod > 0.9995] = 0.9995 dot_prod[dot_prod < -0.9995] = -0.9995 omega = torch.acos(dot_prod) so = torch.sin(omega) res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low_norm + (torch.sin(val*omega)/so).unsqueeze(1) * high_norm res *= (low_weight * (1.0-val) + high_weight * val) return res.reshape(dims) for x_dest in range(shape[3]): for y_dest in range(shape[2]): y = (y_dest + 0.5) * height_scale - 0.5 x = (x_dest + 0.5) * width_scale - 0.5 x1 = max(math.floor(x), 0) x2 = min(x1 + 1, samples.shape[3] - 1) wx = x - math.floor(x) y1 = max(math.floor(y), 0) y2 = min(y1 + 1, samples.shape[2] - 1) wy = y - math.floor(y) in1 = samples[:,:,y1,x1] in2 = samples[:,:,y1,x2] in3 = samples[:,:,y2,x1] in4 = samples[:,:,y2,x2] if (x1 == x2) and (y1 == y2): out_value = in1 elif (x1 == x2): out_value = algorithm(in1, in3, wy) elif (y1 == y2): out_value = algorithm(in1, in2, wx) else: o1 = algorithm(in1, in2, wx) o2 = algorithm(in3, in4, wx) out_value = algorithm(o1, o2, wy) out1[:,:,y_dest,x_dest] = out_value return out1 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): 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) def update(self, value): self.update_absolute(self.current + value)