205 lines
8.4 KiB
Python
205 lines
8.4 KiB
Python
import torch
|
|
import math
|
|
import einops
|
|
|
|
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:
|
|
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")
|
|
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
|
|
|
|
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 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))
|
|
coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w))
|
|
ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w))
|
|
|
|
pass_1 = einops.rearrange(samples.gather(-2,coords_1), 'n c h w -> (n h w) c')
|
|
pass_2 = einops.rearrange(samples.gather(-2,coords_2), 'n c h w -> (n h w) c')
|
|
ratios = einops.rearrange(ratios, 'n c h w -> (n h w) c')
|
|
|
|
result = slerp(pass_1, pass_2, ratios)
|
|
result = einops.rearrange(result, '(n h w) c -> n c h w',n=n, h=h_new, w=w)
|
|
|
|
#linear w
|
|
ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new)
|
|
|
|
coords_1 = coords_1.expand((n, c, h_new, -1))
|
|
coords_2 = coords_2.expand((n, c, h_new, -1))
|
|
ratios = ratios.expand((n, 1, h_new, -1))
|
|
|
|
pass_1 = einops.rearrange(result.gather(-1,coords_1), 'n c h w -> (n h w) c')
|
|
pass_2 = einops.rearrange(result.gather(-1,coords_2), 'n c h w -> (n h w) c')
|
|
ratios = einops.rearrange(ratios, 'n c h w -> (n h w) c')
|
|
|
|
result = slerp(pass_1, pass_2, ratios)
|
|
result = einops.rearrange(result, '(n h w) c -> n c h w',n=n, h=h_new, w=w_new)
|
|
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):
|
|
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)
|