import torch
import math
import struct
import comfy.checkpoint_pickle
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", 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 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
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('<Q', header)[0]
if length_of_header > 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)