[SD3 LoRA Training] Fix errors when not training text encoders (#8743)

* fix

* fix things.

Co-authored-by: Linoy Tsaban <[email protected]>

* remove patch

* apply suggestions

---------

Co-authored-by: Linoy Tsaban <[email protected]>
Co-authored-by: sayakpaul <[email protected]>
Co-authored-by: Linoy Tsaban <[email protected]>

Author: 3 people

examples/dreambooth/train_dreambooth_lora_sd3.py

@@ -962,7 +962,7 @@ def encode_prompt(
             prompt=prompt,
             device=device if device is not None else text_encoder.device,
             num_images_per_prompt=num_images_per_prompt,
-            text_input_ids=text_input_ids_list[i],
+            text_input_ids=text_input_ids_list[i] if text_input_ids_list else None,
         )
         clip_prompt_embeds_list.append(prompt_embeds)
         clip_pooled_prompt_embeds_list.append(pooled_prompt_embeds)
@@ -976,7 +976,7 @@ def encode_prompt(
         max_sequence_length,
         prompt=prompt,
         num_images_per_prompt=num_images_per_prompt,
-        text_input_ids=text_input_ids_list[:-1],
+        text_input_ids=text_input_ids_list[-1] if text_input_ids_list else None,
         device=device if device is not None else text_encoders[-1].device,
     )
 
@@ -1491,6 +1491,9 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
         ) = accelerator.prepare(
             transformer, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler
         )
+        assert text_encoder_one is not None
+        assert text_encoder_two is not None
+        assert text_encoder_three is not None
     else:
         transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
             transformer, optimizer, train_dataloader, lr_scheduler
@@ -1598,7 +1601,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                         tokens_three = tokenize_prompt(tokenizer_three, prompts)
                         prompt_embeds, pooled_prompt_embeds = encode_prompt(
                             text_encoders=[text_encoder_one, text_encoder_two, text_encoder_three],
-                            tokenizers=[None, None, tokenizer_three],
+                            tokenizers=[None, None, None],
                             prompt=prompts,
                             max_sequence_length=args.max_sequence_length,
                             text_input_ids_list=[tokens_one, tokens_two, tokens_three],
@@ -1608,7 +1611,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                         prompt_embeds, pooled_prompt_embeds = encode_prompt(
                             text_encoders=[text_encoder_one, text_encoder_two, text_encoder_three],
                             tokenizers=[None, None, tokenizer_three],
-                            prompt=prompts,
+                            prompt=args.instance_prompt,
                             max_sequence_length=args.max_sequence_length,
                             text_input_ids_list=[tokens_one, tokens_two, tokens_three],
                         )
@@ -1685,10 +1688,12 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
 
                 accelerator.backward(loss)
                 if accelerator.sync_gradients:
-                    params_to_clip = itertools.chain(
-                        transformer_lora_parameters,
-                        text_lora_parameters_one,
-                        text_lora_parameters_two if args.train_text_encoder else transformer_lora_parameters,
+                    params_to_clip = (
+                        itertools.chain(
+                            transformer_lora_parameters, text_lora_parameters_one, text_lora_parameters_two
+                        )
+                        if args.train_text_encoder
+                        else transformer_lora_parameters
                     )
                     accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
@@ -1741,13 +1746,6 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                     text_encoder_one, text_encoder_two, text_encoder_three = load_text_encoders(
                         text_encoder_cls_one, text_encoder_cls_two, text_encoder_cls_three
                     )
-                else:
-                    text_encoder_three = text_encoder_cls_three.from_pretrained(
-                        args.pretrained_model_name_or_path,
-                        subfolder="text_encoder_3",
-                        revision=args.revision,
-                        variant=args.variant,
-                    )
                 pipeline = StableDiffusion3Pipeline.from_pretrained(
                     args.pretrained_model_name_or_path,
                     vae=vae,
@@ -1767,7 +1765,9 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                     pipeline_args=pipeline_args,
                     epoch=epoch,
                 )
-                del text_encoder_one, text_encoder_two, text_encoder_three
+                if not args.train_text_encoder:
+                    del text_encoder_one, text_encoder_two, text_encoder_three
+
                 torch.cuda.empty_cache()
                 gc.collect()