183 lines
7.1 KiB
Python
183 lines
7.1 KiB
Python
import torch
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import math
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def load_torch_file(ckpt, safe_load=False):
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if ckpt.lower().endswith(".safetensors"):
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import safetensors.torch
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sd = safetensors.torch.load_file(ckpt, device="cpu")
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else:
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if safe_load:
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pl_sd = torch.load(ckpt, map_location="cpu", weights_only=True)
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else:
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pl_sd = torch.load(ckpt, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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if "state_dict" in pl_sd:
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sd = pl_sd["state_dict"]
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else:
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sd = pl_sd
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return sd
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def transformers_convert(sd, prefix_from, prefix_to, number):
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resblock_to_replace = {
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"ln_1": "layer_norm1",
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"ln_2": "layer_norm2",
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"mlp.c_fc": "mlp.fc1",
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"mlp.c_proj": "mlp.fc2",
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"attn.out_proj": "self_attn.out_proj",
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}
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for resblock in range(number):
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for x in resblock_to_replace:
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for y in ["weight", "bias"]:
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k = "{}.transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y)
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k_to = "{}.encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y)
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if k in sd:
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sd[k_to] = sd.pop(k)
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for y in ["weight", "bias"]:
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k_from = "{}.transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y)
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if k_from in sd:
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weights = sd.pop(k_from)
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shape_from = weights.shape[0] // 3
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for x in range(3):
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p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
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k_to = "{}.encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y)
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sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]
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return sd
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#slow and inefficient, should be optimized
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def bislerp(samples, width, height):
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shape = list(samples.shape)
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width_scale = (shape[3]) / (width )
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height_scale = (shape[2]) / (height )
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shape[3] = width
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shape[2] = height
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out1 = torch.empty(shape, dtype=samples.dtype, layout=samples.layout, device=samples.device)
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def algorithm(in1, w1, in2, w2):
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dims = in1.shape
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val = w2
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#flatten to batches
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low = in1.reshape(dims[0], -1)
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high = in2.reshape(dims[0], -1)
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low_norm = low/torch.norm(low, dim=1, keepdim=True)
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high_norm = high/torch.norm(high, dim=1, keepdim=True)
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# in case we divide by zero
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low_norm[low_norm != low_norm] = 0.0
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high_norm[high_norm != high_norm] = 0.0
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omega = torch.acos((low_norm*high_norm).sum(1))
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so = torch.sin(omega)
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res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
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return res.reshape(dims)
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for x_dest in range(shape[3]):
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for y_dest in range(shape[2]):
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y = (y_dest) * height_scale
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x = (x_dest) * width_scale
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x1 = max(math.floor(x), 0)
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x2 = min(x1 + 1, samples.shape[3] - 1)
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y1 = max(math.floor(y), 0)
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y2 = min(y1 + 1, samples.shape[2] - 1)
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in1 = samples[:,:,y1,x1]
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in2 = samples[:,:,y1,x2]
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in3 = samples[:,:,y2,x1]
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in4 = samples[:,:,y2,x2]
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if (x1 == x2) and (y1 == y2):
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out_value = in1
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elif (x1 == x2):
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out_value = algorithm(in1, (y2 - y), in3, (y - y1))
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elif (y1 == y2):
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out_value = algorithm(in1, (x2 - x), in2, (x - x1))
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else:
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o1 = algorithm(in1, (x2 - x), in2, (x - x1))
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o2 = algorithm(in3, (x2 - x), in4, (x - x1))
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out_value = algorithm(o1, (y2 - y), o2, (y - y1))
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out1[:,:,y_dest,x_dest] = out_value
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return out1
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def common_upscale(samples, width, height, upscale_method, crop):
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if crop == "center":
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old_width = samples.shape[3]
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old_height = samples.shape[2]
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old_aspect = old_width / old_height
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new_aspect = width / height
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x = 0
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y = 0
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if old_aspect > new_aspect:
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x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
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elif old_aspect < new_aspect:
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y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
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s = samples[:,:,y:old_height-y,x:old_width-x]
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else:
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s = samples
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if upscale_method == "bislerp":
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return bislerp(s, width, height)
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else:
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return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
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def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
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return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
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@torch.inference_mode()
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def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, pbar = None):
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output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device="cpu")
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for b in range(samples.shape[0]):
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s = samples[b:b+1]
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out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu")
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out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu")
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for y in range(0, s.shape[2], tile_y - overlap):
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for x in range(0, s.shape[3], tile_x - overlap):
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s_in = s[:,:,y:y+tile_y,x:x+tile_x]
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ps = function(s_in).cpu()
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mask = torch.ones_like(ps)
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feather = round(overlap * upscale_amount)
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for t in range(feather):
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mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
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mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
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mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
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mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
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out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask
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out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask
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if pbar is not None:
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pbar.update(1)
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output[b:b+1] = out/out_div
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return output
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PROGRESS_BAR_HOOK = None
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def set_progress_bar_global_hook(function):
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global PROGRESS_BAR_HOOK
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PROGRESS_BAR_HOOK = function
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class ProgressBar:
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def __init__(self, total):
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global PROGRESS_BAR_HOOK
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self.total = total
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self.current = 0
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self.hook = PROGRESS_BAR_HOOK
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def update_absolute(self, value, total=None):
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if total is not None:
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self.total = total
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if value > self.total:
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value = self.total
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self.current = value
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if self.hook is not None:
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self.hook(self.current, self.total)
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def update(self, value):
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self.update_absolute(self.current + value)
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