try diffusers update

requirements.txt

@@ -29,7 +29,7 @@ trl==0.12.0
 tiktoken==0.6.0
 transformers==4.46.2
 accelerate==1.1.1
-diffusers==0.27.2
+diffusers==0.31.0
 bitsandbytes==0.44.1
 # extras
 rouge_score==0.1.2

src/autotrain/trainers/dreambooth/train.py

@@ -37,7 +37,7 @@
     DPMSolverMultistepScheduler,
     UNet2DConditionModel,
 )
-from diffusers.loaders import LoraLoaderMixin
+from diffusers.loaders import StableDiffusionLoraLoaderMixin
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params
 from diffusers.utils import convert_state_dict_to_diffusers, convert_unet_state_dict_to_peft, is_wandb_available
@@ -63,6 +63,7 @@ def log_validation(
     accelerator,
     pipeline_args,
     epoch,
+    torch_dtype,  # Add torch_dtype parameter
     is_final_validation=False,
 ):
     logger.info(
@@ -82,7 +83,7 @@ def log_validation(
 
     pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
 
-    pipeline = pipeline.to(accelerator.device)
+    pipeline = pipeline.to(accelerator.device, dtype=torch_dtype)  # Use torch_dtype
     pipeline.set_progress_bar_config(disable=True)
 
     # run inference
@@ -340,6 +341,10 @@ def main(args):
         project_config=accelerator_project_config,
     )
 
+    # Add MPS support check
+    if torch.backends.mps.is_available():
+        accelerator.native_amp = False
+
     if args.report_to == "wandb":
         if not is_wandb_available():
             raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
@@ -545,7 +550,7 @@ def save_model_hook(models, weights, output_dir):
                 # make sure to pop weight so that corresponding model is not saved again
                 weights.pop()
 
-            LoraLoaderMixin.save_lora_weights(
+            StableDiffusionLoraLoaderMixin.save_lora_weights(
                 output_dir,
                 unet_lora_layers=unet_lora_layers_to_save,
                 text_encoder_lora_layers=text_encoder_lora_layers_to_save,
@@ -565,7 +570,7 @@ def load_model_hook(models, input_dir):
             else:
                 raise ValueError(f"unexpected save model: {model.__class__}")
 
-        lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)
+        lora_state_dict, network_alphas = StableDiffusionLoraLoaderMixin.lora_state_dict(input_dir)
 
         unet_state_dict = {f'{k.replace("unet.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.")}
         unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)
@@ -948,6 +953,7 @@ def compute_text_embeddings(prompt):
                     accelerator,
                     pipeline_args,
                     epoch,
+                    torch_dtype=weight_dtype,
                 )
 
     # Save the lora layers
@@ -964,7 +970,7 @@ def compute_text_embeddings(prompt):
         else:
             text_encoder_state_dict = None
 
-        LoraLoaderMixin.save_lora_weights(
+        StableDiffusionLoraLoaderMixin.save_lora_weights(
             save_directory=args.output_dir,
             unet_lora_layers=unet_lora_state_dict,
             text_encoder_lora_layers=text_encoder_state_dict,

src/autotrain/trainers/dreambooth/train_xl.py

@@ -14,7 +14,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 
-import contextlib
 import gc
 import itertools
 import json
@@ -26,7 +25,6 @@
 from pathlib import Path
 
 import diffusers
-import numpy as np
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
@@ -36,7 +34,6 @@
 from diffusers import (
     AutoencoderKL,
     DDPMScheduler,
-    DPMSolverMultistepScheduler,
     EDMEulerScheduler,
     EulerDiscreteScheduler,
     StableDiffusionXLPipeline,
@@ -78,59 +75,6 @@ def determine_scheduler_type(pretrained_model_name_or_path, revision):
     return scheduler_type
 
 
-def log_validation(
-    pipeline,
-    args,
-    accelerator,
-    pipeline_args,
-    epoch,
-    is_final_validation=False,
-):
-    logger.info(
-        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
-        f" {args.validation_prompt}."
-    )
-
-    # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
-    scheduler_args = {}
-
-    if not args.do_edm_style_training:
-        if "variance_type" in pipeline.scheduler.config:
-            variance_type = pipeline.scheduler.config.variance_type
-
-            if variance_type in ["learned", "learned_range"]:
-                variance_type = "fixed_small"
-
-            scheduler_args["variance_type"] = variance_type
-
-        pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
-
-    pipeline = pipeline.to(accelerator.device)
-    pipeline.set_progress_bar_config(disable=True)
-
-    # run inference
-    generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
-    # Currently the context determination is a bit hand-wavy. We can improve it in the future if there's a better
-    # way to condition it. Reference: https://github.com/huggingface/diffusers/pull/7126#issuecomment-1968523051
-    inference_ctx = (
-        contextlib.nullcontext() if "playground" in args.pretrained_model_name_or_path else torch.cuda.amp.autocast()
-    )
-
-    with inference_ctx:
-        images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]
-
-    for tracker in accelerator.trackers:
-        phase_name = "test" if is_final_validation else "validation"
-        if tracker.name == "tensorboard":
-            np_images = np.stack([np.asarray(img) for img in images])
-            tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC")
-
-    del pipeline
-    torch.cuda.empty_cache()
-
-    return images
-
-
 def import_model_class_from_model_name_or_path(
     pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
 ):
@@ -1239,42 +1183,6 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
             if global_step >= args.max_train_steps:
                 break
 
-        if accelerator.is_main_process:
-            if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
-                # create pipeline
-                if not args.train_text_encoder:
-                    text_encoder_one = text_encoder_cls_one.from_pretrained(
-                        args.pretrained_model_name_or_path,
-                        subfolder="text_encoder",
-                        revision=args.revision,
-                        variant=args.variant,
-                    )
-                    text_encoder_two = text_encoder_cls_two.from_pretrained(
-                        args.pretrained_model_name_or_path,
-                        subfolder="text_encoder_2",
-                        revision=args.revision,
-                        variant=args.variant,
-                    )
-                pipeline = StableDiffusionXLPipeline.from_pretrained(
-                    args.pretrained_model_name_or_path,
-                    vae=vae,
-                    text_encoder=accelerator.unwrap_model(text_encoder_one),
-                    text_encoder_2=accelerator.unwrap_model(text_encoder_two),
-                    unet=accelerator.unwrap_model(unet),
-                    revision=args.revision,
-                    variant=args.variant,
-                    torch_dtype=weight_dtype,
-                )
-                pipeline_args = {"prompt": args.validation_prompt}
-
-                images = log_validation(
-                    pipeline,
-                    args,
-                    accelerator,
-                    pipeline_args,
-                    epoch,
-                )
-
     # Save the lora layers
     accelerator.wait_for_everyone()
     if accelerator.is_main_process: