update

fix?

.github/workflows/pr_test_peft_backend.yml

@@ -59,7 +59,7 @@ jobs:
 
     - name: Run fast PyTorch LoRA CPU tests with PEFT backend
       run: |
-        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
           -s -v \
           --make-reports=tests_${{ matrix.config.report }} \
           tests/lora/test_lora_layers_peft.py

docs/source/en/api/models/autoencoderkl.md

@@ -33,6 +33,9 @@ model = AutoencoderKL.from_single_file(url)
 ## AutoencoderKL
 
 [[autodoc]] AutoencoderKL
+    - decode
+    - encode
+    - all
 
 ## AutoencoderKLOutput
 

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -1279,7 +1279,7 @@ def main(args):
         for name, param in text_encoder_one.named_parameters():
             if "token_embedding" in name:
                 # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
-                param = param.to(dtype=torch.float32)
+                param.data = param.to(dtype=torch.float32)
                 param.requires_grad = True
                 text_lora_parameters_one.append(param)
             else:
@@ -1288,7 +1288,7 @@ def main(args):
         for name, param in text_encoder_two.named_parameters():
             if "token_embedding" in name:
                 # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
-                param = param.to(dtype=torch.float32)
+                param.data = param.to(dtype=torch.float32)
                 param.requires_grad = True
                 text_lora_parameters_two.append(param)
             else:
@@ -1725,19 +1725,19 @@ def main(args):
         num_train_epochs_text_encoder = int(args.train_text_encoder_frac * args.num_train_epochs)
     elif args.train_text_encoder_ti:  # args.train_text_encoder_ti
         num_train_epochs_text_encoder = int(args.train_text_encoder_ti_frac * args.num_train_epochs)
-
+    # flag used for textual inversion
+    pivoted = False
     for epoch in range(first_epoch, args.num_train_epochs):
         # if performing any kind of optimization of text_encoder params
         if args.train_text_encoder or args.train_text_encoder_ti:
             if epoch == num_train_epochs_text_encoder:
                 print("PIVOT HALFWAY", epoch)
                 # stopping optimization of text_encoder params
-                # re setting the optimizer to optimize only on unet params
-                optimizer.param_groups[1]["lr"] = 0.0
-                optimizer.param_groups[2]["lr"] = 0.0
+                # this flag is used to reset the optimizer to optimize only on unet params
+                pivoted = True
 
             else:
-                # still optimizng the text encoder
+                # still optimizing the text encoder
                 text_encoder_one.train()
                 text_encoder_two.train()
                 # set top parameter requires_grad = True for gradient checkpointing works
@@ -1747,6 +1747,12 @@ def main(args):
 
         unet.train()
         for step, batch in enumerate(train_dataloader):
+            if pivoted:
+                # stopping optimization of text_encoder params
+                # re setting the optimizer to optimize only on unet params
+                optimizer.param_groups[1]["lr"] = 0.0
+                optimizer.param_groups[2]["lr"] = 0.0
+
             with accelerator.accumulate(unet):
                 prompts = batch["prompts"]
                 # encode batch prompts when custom prompts are provided for each image -
@@ -1885,8 +1891,7 @@ def main(args):
 
                 # every step, we reset the embeddings to the original embeddings.
                 if args.train_text_encoder_ti:
-                    for idx, text_encoder in enumerate(text_encoders):
-                        embedding_handler.retract_embeddings()
+                    embedding_handler.retract_embeddings()
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:

src/diffusers/utils/testing_utils.py

@@ -519,10 +519,10 @@ def export_to_video(video_frames: List[np.ndarray], output_video_path: str = Non
 
 
 def load_hf_numpy(path) -> np.ndarray:
-    if not path.startswith("http://") or path.startswith("https://"):
-        path = Path(
-            "https://huggingface.co/datasets/fusing/diffusers-testing/resolve/main", urllib.parse.quote(path)
-        ).as_posix()
+    base_url = "https://huggingface.co/datasets/fusing/diffusers-testing/resolve/main"
+
+    if not path.startswith("http://") and not path.startswith("https://"):
+        path = os.path.join(base_url, urllib.parse.quote(path))
 
     return load_numpy(path)