Merge branch 'main' into flux-kontext-modular

Author: sayakpaul

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -1399,6 +1399,7 @@ def main(args):
                 torch_dtype = torch.float16
             elif args.prior_generation_precision == "bf16":
                 torch_dtype = torch.bfloat16
+
             pipeline = FluxPipeline.from_pretrained(
                 args.pretrained_model_name_or_path,
                 torch_dtype=torch_dtype,
@@ -1419,7 +1420,8 @@ def main(args):
             for example in tqdm(
                 sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
             ):
-                images = pipeline(example["prompt"]).images
+                with torch.autocast(device_type=accelerator.device.type, dtype=torch_dtype):
+                    images = pipeline(prompt=example["prompt"]).images
 
                 for i, image in enumerate(images):
                     hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()

examples/dreambooth/train_dreambooth_lora_flux.py

@@ -1131,6 +1131,7 @@ def main(args):
                 torch_dtype = torch.float16
             elif args.prior_generation_precision == "bf16":
                 torch_dtype = torch.bfloat16
+
             pipeline = FluxPipeline.from_pretrained(
                 args.pretrained_model_name_or_path,
                 torch_dtype=torch_dtype,
@@ -1151,16 +1152,16 @@ def main(args):
             for example in tqdm(
                 sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
             ):
-                images = pipeline(example["prompt"]).images
+                with torch.autocast(device_type=accelerator.device.type, dtype=torch_dtype):
+                    images = pipeline(prompt=example["prompt"]).images
 
                 for i, image in enumerate(images):
                     hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
                     image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
                     image.save(image_filename)
 
             del pipeline
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+            free_memory()
 
     # Handle the repository creation
     if accelerator.is_main_process:
@@ -1728,6 +1729,10 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                             device=accelerator.device,
                             prompt=args.instance_prompt,
                         )
+                    else:
+                        prompt_embeds, pooled_prompt_embeds, text_ids = compute_text_embeddings(
+                            prompts, text_encoders, tokenizers
+                        )
 
                 # Convert images to latent space
                 if args.cache_latents: