fix to work with Diffusers 0.17.0

library/lpw_stable_diffusion.py

@@ -464,7 +464,7 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
         scheduler: SchedulerMixin,
-        clip_skip: int,
+        # clip_skip: int,
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPFeatureExtractor,
         requires_safety_checker: bool = True,
@@ -479,7 +479,7 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
             feature_extractor=feature_extractor,
             requires_safety_checker=requires_safety_checker,
         )
-        self.clip_skip = clip_skip
+        # self.clip_skip = clip_skip
         self.__init__additional__()
 
     # else:

library/model_util.py

@@ -5,7 +5,7 @@ import math
 import os
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextConfig, logging
-from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline #, UNet2DConditionModel
+from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline  # , UNet2DConditionModel
 from safetensors.torch import load_file, save_file
 from library.original_unet import UNet2DConditionModel
 
@@ -127,17 +127,17 @@ def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
         new_item = new_item.replace("norm.weight", "group_norm.weight")
         new_item = new_item.replace("norm.bias", "group_norm.bias")
 
-        new_item = new_item.replace("q.weight", "query.weight")
-        new_item = new_item.replace("q.bias", "query.bias")
+        new_item = new_item.replace("q.weight", "to_q.weight")
+        new_item = new_item.replace("q.bias", "to_q.bias")
 
-        new_item = new_item.replace("k.weight", "key.weight")
-        new_item = new_item.replace("k.bias", "key.bias")
+        new_item = new_item.replace("k.weight", "to_k.weight")
+        new_item = new_item.replace("k.bias", "to_k.bias")
 
-        new_item = new_item.replace("v.weight", "value.weight")
-        new_item = new_item.replace("v.bias", "value.bias")
+        new_item = new_item.replace("v.weight", "to_v.weight")
+        new_item = new_item.replace("v.bias", "to_v.bias")
 
-        new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
-        new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
+        new_item = new_item.replace("proj_out.weight", "to_out.0.weight")
+        new_item = new_item.replace("proj_out.bias", "to_out.0.bias")
 
         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
 
@@ -192,8 +192,8 @@ def assign_to_checkpoint(
                 new_path = new_path.replace(replacement["old"], replacement["new"])
 
         # proj_attn.weight has to be converted from conv 1D to linear
-        if "proj_attn.weight" in new_path:
-            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
+        if ".attentions." in new_path and ".0.to_" in new_path and old_checkpoint[path["old"]].ndim > 2:
+            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0]
         else:
             checkpoint[new_path] = old_checkpoint[path["old"]]
 
@@ -362,7 +362,7 @@ def convert_ldm_unet_checkpoint(v2, checkpoint, config):
 
     # SDのv2では1*1のconv2dがlinearに変わっている
     # 誤って Diffusers 側を conv2d のままにしてしまったので、変換必要
-    if v2 and not config.get('use_linear_projection', False):
+    if v2 and not config.get("use_linear_projection", False):
         linear_transformer_to_conv(new_checkpoint)
 
     return new_checkpoint

library/train_util.py

@@ -3467,11 +3467,11 @@ def sample_images(
         unet=unet,
         tokenizer=tokenizer,
         scheduler=scheduler,
-        clip_skip=args.clip_skip,
         safety_checker=None,
         feature_extractor=None,
         requires_safety_checker=False,
     )
+    pipeline.clip_skip = args.clip_skip     # Pipelineのコンストラクタにckip_skipを追加できないので後から設定する
     pipeline.to(device)
 
     save_dir = args.output_dir + "/sample"