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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/ldm/modules/diffusionmodules/model.py
+7 -7
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@@ -3,8 +3,8 @@ import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from typing import Optional, Any
import logging
from ldm_patched.modules import model_management
import ldm_patched.modules.ops
@@ -190,7 +190,7 @@ def slice_attention(q, k, v):
steps *= 2
if steps > 128:
raise e
print("out of memory error, increasing steps and trying again", steps)
logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
return r1
@@ -235,7 +235,7 @@ def pytorch_attention(q, k, v):
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
out = out.transpose(2, 3).reshape(B, C, H, W)
except model_management.OOM_EXCEPTION as e:
print("scaled_dot_product_attention OOMed: switched to slice attention")
logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
return out
@@ -268,13 +268,13 @@ class AttnBlock(nn.Module):
padding=0)
if model_management.xformers_enabled_vae():
print("Using xformers attention in VAE")
logging.info("Using xformers attention in VAE")
self.optimized_attention = xformers_attention
elif model_management.pytorch_attention_enabled():
print("Using pytorch attention in VAE")
logging.info("Using pytorch attention in VAE")
self.optimized_attention = pytorch_attention
else:
print("Using split attention in VAE")
logging.info("Using split attention in VAE")
self.optimized_attention = normal_attention
def forward(self, x):
@@ -562,7 +562,7 @@ class Decoder(nn.Module):
block_in = ch*ch_mult[self.num_resolutions-1]
curr_res = resolution // 2**(self.num_resolutions-1)
self.z_shape = (1,z_channels,curr_res,curr_res)
print("Working with z of shape {} = {} dimensions.".format(
logging.debug("Working with z of shape {} = {} dimensions.".format(
self.z_shape, np.prod(self.z_shape)))
# z to block_in