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major refactoring of modules

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-07-03 09:18:38 -04:00
parent 772a5c9ad3
commit c4d9338d2e
214 changed files with 1154 additions and 1153 deletions
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modules/prompt_parser_xhinker.py
+1 -2
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@@ -731,7 +731,7 @@ def get_weighted_text_embeddings_sdxl_refiner(
for z in range(len(neg_weight_tensor_2)):
if neg_weight_tensor_2[z] != 1.0:
ow = neg_weight_tensor_2[z] - 1
# ow = neg_weight_tensor_2[z] - 1
# neg_weight = 1 + (math.exp(ow)/(math.exp(ow) + 1) - 0.5) * 2
# add weight method 1:
@@ -1330,7 +1330,6 @@ def get_weighted_text_embeddings_sd3(
sd3_neg_prompt_embeds = torch.cat([clip_neg_prompt_embeds, t5_neg_prompt_embeds], dim=-2)
# padding
import torch.nn.functional as F
size_diff = sd3_neg_prompt_embeds.size(1) - sd3_prompt_embeds.size(1)
# Calculate padding. Format for pad is (padding_left, padding_right, padding_top, padding_bottom, padding_front, padding_back)
# Since we are padding along the second dimension (axis=1), we need (0, 0, padding_top, padding_bottom, 0, 0)