Merge branch 'main' into RMS

examples/dreambooth/train_dreambooth_lora_sana.py

@@ -158,6 +158,9 @@ def log_validation(
         f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
         f" {args.validation_prompt}."
     )
+    if args.enable_vae_tiling:
+        pipeline.vae.enable_tiling(tile_sample_min_height=1024, tile_sample_stride_width=1024)
+
     pipeline.text_encoder = pipeline.text_encoder.to(torch.bfloat16)
     pipeline = pipeline.to(accelerator.device)
     pipeline.set_progress_bar_config(disable=True)
@@ -597,6 +600,7 @@ def parse_args(input_args=None):
         help="Whether to offload the VAE and the text encoder to CPU when they are not used.",
     )
     parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    parser.add_argument("--enable_vae_tiling", action="store_true", help="Enabla vae tiling in log validation")
 
     if input_args is not None:
         args = parser.parse_args(input_args)

src/diffusers/pipelines/mochi/pipeline_mochi.py

@@ -21,7 +21,7 @@
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...loaders import Mochi1LoraLoaderMixin
-from ...models.autoencoders import AutoencoderKL
+from ...models.autoencoders import AutoencoderKLMochi
 from ...models.transformers import MochiTransformer3DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
 from ...utils import (
@@ -151,8 +151,8 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin):
             Conditional Transformer architecture to denoise the encoded video latents.
         scheduler ([`FlowMatchEulerDiscreteScheduler`]):
             A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        vae ([`AutoencoderKLMochi`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
         text_encoder ([`T5EncoderModel`]):
             [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
             the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
@@ -171,7 +171,7 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin):
     def __init__(
         self,
         scheduler: FlowMatchEulerDiscreteScheduler,
-        vae: AutoencoderKL,
+        vae: AutoencoderKLMochi,
         text_encoder: T5EncoderModel,
         tokenizer: T5TokenizerFast,
         transformer: MochiTransformer3DModel,

tests/quantization/bnb/test_mixed_int8.py

@@ -18,6 +18,7 @@
 
 import numpy as np
 import pytest
+from huggingface_hub import hf_hub_download
 
 from diffusers import BitsAndBytesConfig, DiffusionPipeline, FluxTransformer2DModel, SD3Transformer2DModel, logging
 from diffusers.utils import is_accelerate_version
@@ -30,6 +31,7 @@
     numpy_cosine_similarity_distance,
     require_accelerate,
     require_bitsandbytes_version_greater,
+    require_peft_version_greater,
     require_torch,
     require_torch_gpu,
     require_transformers_version_greater,
@@ -509,6 +511,29 @@ def test_quality(self):
         max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice)
         self.assertTrue(max_diff < 1e-3)
 
+    @require_peft_version_greater("0.14.0")
+    def test_lora_loading(self):
+        self.pipeline_8bit.load_lora_weights(
+            hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd"
+        )
+        self.pipeline_8bit.set_adapters("hyper-sd", adapter_weights=0.125)
+
+        output = self.pipeline_8bit(
+            prompt=self.prompt,
+            height=256,
+            width=256,
+            max_sequence_length=64,
+            output_type="np",
+            num_inference_steps=8,
+            generator=torch.manual_seed(42),
+        ).images
+        out_slice = output[0, -3:, -3:, -1].flatten()
+
+        expected_slice = np.array([0.3916, 0.3916, 0.3887, 0.4243, 0.4155, 0.4233, 0.4570, 0.4531, 0.4248])
+
+        max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice)
+        self.assertTrue(max_diff < 1e-3)
+
 
 @slow
 class BaseBnb8bitSerializationTests(Base8bitTests):