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wip: update ldm_patched

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currently issues with calculate_sigmas call
This commit is contained in:
Manuel Schmid
2024-06-02 20:52:52 +02:00
parent ab01104d42
commit 3881fa0806
84 changed files with 5380 additions and 1923 deletions
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ldm_patched/modules/utils.py
+26 -4
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@@ -5,6 +5,7 @@ import ldm_patched.modules.checkpoint_pickle
import safetensors.torch
import numpy as np
from PIL import Image
import logging
def load_torch_file(ckpt, safe_load=False, device=None):
if device is None:
@@ -14,14 +15,14 @@ def load_torch_file(ckpt, safe_load=False, device=None):
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.")
logging.warning("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=device, weights_only=True)
else:
pl_sd = torch.load(ckpt, map_location=device, pickle_module=ldm_patched.modules.checkpoint_pickle)
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
logging.debug(f"Global Step: {pl_sd['global_step']}")
if "state_dict" in pl_sd:
sd = pl_sd["state_dict"]
else:
@@ -98,8 +99,22 @@ def transformers_convert(sd, prefix_from, prefix_to, number):
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 clip_text_transformers_convert(sd, prefix_from, prefix_to):
sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32)
tp = "{}text_projection.weight".format(prefix_from)
if tp in sd:
sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp)
tp = "{}text_projection".format(prefix_from)
if tp in sd:
sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous()
return sd
UNET_MAP_ATTENTIONS = {
"proj_in.weight",
"proj_in.bias",
@@ -169,6 +184,8 @@ UNET_MAP_BASIC = {
}
def unet_to_diffusers(unet_config):
if "num_res_blocks" not in unet_config:
return {}
num_res_blocks = unet_config["num_res_blocks"]
channel_mult = unet_config["channel_mult"]
transformer_depth = unet_config["transformer_depth"][:]
@@ -278,8 +295,11 @@ def set_attr(obj, attr, value):
for name in attrs[:-1]:
obj = getattr(obj, name)
prev = getattr(obj, attrs[-1])
setattr(obj, attrs[-1], torch.nn.Parameter(value, requires_grad=False))
del prev
setattr(obj, attrs[-1], value)
return prev
def set_attr_param(obj, attr, value):
return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))
def copy_to_param(obj, attr, value):
# inplace update tensor instead of replacing it
@@ -413,6 +433,8 @@ def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_am
out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
for y in range(0, s.shape[2], tile_y - overlap):
for x in range(0, s.shape[3], tile_x - overlap):
x = max(0, min(s.shape[-1] - overlap, x))
y = max(0, min(s.shape[-2] - overlap, y))
s_in = s[:,:,y:y+tile_y,x:x+tile_x]
ps = function(s_in).to(output_device)