202 lines
8.3 KiB
Python
202 lines
8.3 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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if not 'weights_only' in torch.load.__code__.co_varnames:
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print("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
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safe_load = False
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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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def bislerp(samples, width, height):
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def slerp(b1, b2, r):
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'''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''
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c = b1.shape[-1]
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#norms
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b1_norms = torch.norm(b1, dim=-1, keepdim=True)
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b2_norms = torch.norm(b2, dim=-1, keepdim=True)
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#normalize
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b1_normalized = b1 / b1_norms
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b2_normalized = b2 / b2_norms
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#zero when norms are zero
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b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0
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b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0
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#slerp
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dot = (b1_normalized*b2_normalized).sum(1)
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omega = torch.acos(dot)
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so = torch.sin(omega)
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#technically not mathematically correct, but more pleasing?
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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
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res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c)
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#edge cases for same or polar opposites
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res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5]
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res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]
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return res
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def generate_bilinear_data(length_old, length_new):
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coords_1 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32)
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coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear")
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ratios = coords_1 - coords_1.floor()
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coords_1 = coords_1.to(torch.int64)
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coords_2 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32) + 1
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coords_2[:,:,:,-1] -= 1
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coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
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coords_2 = coords_2.to(torch.int64)
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return ratios, coords_1, coords_2
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n,c,h,w = samples.shape
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h_new, w_new = (height, width)
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#linear w
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ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new)
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coords_1 = coords_1.expand((n, c, h, -1))
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coords_2 = coords_2.expand((n, c, h, -1))
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ratios = ratios.expand((n, 1, h, -1))
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pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c))
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pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c))
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ratios = ratios.movedim(1, -1).reshape((-1,1))
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result = slerp(pass_1, pass_2, ratios)
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result = result.reshape(n, h, w_new, c).movedim(-1, 1)
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#linear h
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ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new)
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coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
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coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
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ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new))
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pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c))
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pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c))
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ratios = ratios.movedim(1, -1).reshape((-1,1))
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result = slerp(pass_1, pass_2, ratios)
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result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
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return result
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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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