update

docs/source/en/_toctree.yml

@@ -22,8 +22,6 @@
     title: Reproducibility
   - local: using-diffusers/schedulers
     title: Schedulers
-  - local: using-diffusers/guiders
-    title: Guiders
   - local: using-diffusers/automodel
     title: AutoModel
   - local: using-diffusers/other-formats
@@ -112,6 +110,8 @@
     title: ModularPipeline
   - local: modular_diffusers/components_manager
     title: ComponentsManager
+  - local: modular_diffusers/guiders
+    title: Guiders
   - local: modular_diffusers/custom_blocks
     title: Building Custom Blocks
   - local: modular_diffusers/mellon

docs/source/en/api/pipelines/hunyuan_video15.md

@@ -99,7 +99,7 @@ To update guider configuration, you can run `pipe.guider = pipe.guider.new(...)`
 pipe.guider = pipe.guider.new(guidance_scale=5.0)
 ```
 
-Read more on Guider [here](../../using-diffusers/guiders).
+Read more on Guider [here](../../modular_diffusers/guiders).
 
 
 

docs/source/en/api/pipelines/hunyuanimage21.md

@@ -30,7 +30,7 @@ HunyuanImage-2.1 comes in the following variants:
 
 ## HunyuanImage-2.1
 
-HunyuanImage-2.1 applies [Adaptive Projected Guidance (APG)](https://huggingface.co/papers/2410.02416) combined with Classifier-Free Guidance (CFG) in the denoising loop. `HunyuanImagePipeline` has a `guider` component (read more about [Guider](../../using-diffusers/guiders)) and does not take a `guidance_scale` parameter at runtime. To change guider-related parameters, e.g., `guidance_scale`, you can update the `guider` configuration instead.
+HunyuanImage-2.1 applies [Adaptive Projected Guidance (APG)](https://huggingface.co/papers/2410.02416) combined with Classifier-Free Guidance (CFG) in the denoising loop. `HunyuanImagePipeline` has a `guider` component (read more about [Guider](../modular_diffusers/guiders.md)) and does not take a `guidance_scale` parameter at runtime. To change guider-related parameters, e.g., `guidance_scale`, you can update the `guider` configuration instead.
 
 ```python
 import torch

docs/source/en/modular_diffusers/modular_pipeline.md

@@ -338,7 +338,7 @@ guider = ClassifierFreeGuidance(guidance_scale=5.0)
 pipeline.update_components(guider=guider)
 ```
 
-See the [Guiders](../using-diffusers/guiders) guide for more details on available guiders and how to configure them.
+See the [Guiders](./guiders) guide for more details on available guiders and how to configure them.
 
 ## Splitting a pipeline into stages
 

docs/source/en/modular_diffusers/overview.md

@@ -39,7 +39,7 @@ The Modular Diffusers docs are organized as shown below.
 
 - [ModularPipeline](./modular_pipeline) shows you how to create and convert pipeline blocks into an executable [`ModularPipeline`].
 - [ComponentsManager](./components_manager) shows you how to manage and reuse components across multiple pipelines.
-- [Guiders](../using-diffusers/guiders) shows you how to use different guidance methods in the pipeline.
+- [Guiders](./guiders) shows you how to use different guidance methods in the pipeline.
 
 ## Mellon Integration
 

docs/source/en/optimization/attention_backends.md

@@ -35,7 +35,7 @@ The [`~ModelMixin.set_attention_backend`] method iterates through all the module
 The example below demonstrates how to enable the `_flash_3_hub` implementation for FlashAttention-3 from the [`kernels`](https://github.com/huggingface/kernels) library, which allows you to instantly use optimized compute kernels from the Hub without requiring any setup.
 
 > [!NOTE]
-> FlashAttention-3 requires Ampere GPUs at a minimum.
+> FlashAttention-3 is not supported for non-Hopper architectures, in which case, use FlashAttention with `set_attention_backend("flash")`.
 
 ```py
 import torch

docs/source/en/optimization/memory.md

@@ -482,6 +482,144 @@ print(
 )  # (2880, 1, 960, 320) having a stride of 1 for the 2nd dimension proves that it works
 ```
 
+## torch.jit.trace
+
+[torch.jit.trace](https://pytorch.org/docs/stable/generated/torch.jit.trace.html) records the operations a model performs on a sample input and creates a new, optimized representation of the model based on the recorded execution path. During tracing, the model is optimized to reduce overhead from Python and dynamic control flows and operations are fused together for more efficiency. The returned executable or [ScriptFunction](https://pytorch.org/docs/stable/generated/torch.jit.ScriptFunction.html) can be compiled.
+
+```py
+import time
+import torch
+from diffusers import StableDiffusionPipeline
+import functools
+
+# torch disable grad
+torch.set_grad_enabled(False)
+
+# set variables
+n_experiments = 2
+unet_runs_per_experiment = 50
+
+# load sample inputs
+def generate_inputs():
+    sample = torch.randn((2, 4, 64, 64), device="cuda", dtype=torch.float16)
+    timestep = torch.rand(1, device="cuda", dtype=torch.float16) * 999
+    encoder_hidden_states = torch.randn((2, 77, 768), device="cuda", dtype=torch.float16)
+    return sample, timestep, encoder_hidden_states
+
+
+pipeline = StableDiffusionPipeline.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+).to("cuda")
+unet = pipeline.unet
+unet.eval()
+unet.to(memory_format=torch.channels_last)  # use channels_last memory format
+unet.forward = functools.partial(unet.forward, return_dict=False)  # set return_dict=False as default
+
+# warmup
+for _ in range(3):
+    with torch.inference_mode():
+        inputs = generate_inputs()
+        orig_output = unet(*inputs)
+
+# trace
+print("tracing..")
+unet_traced = torch.jit.trace(unet, inputs)
+unet_traced.eval()
+print("done tracing")
+
+# warmup and optimize graph
+for _ in range(5):
+    with torch.inference_mode():
+        inputs = generate_inputs()
+        orig_output = unet_traced(*inputs)
+
+# benchmarking
+with torch.inference_mode():
+    for _ in range(n_experiments):
+        torch.cuda.synchronize()
+        start_time = time.time()
+        for _ in range(unet_runs_per_experiment):
+            orig_output = unet_traced(*inputs)
+        torch.cuda.synchronize()
+        print(f"unet traced inference took {time.time() - start_time:.2f} seconds")
+    for _ in range(n_experiments):
+        torch.cuda.synchronize()
+        start_time = time.time()
+        for _ in range(unet_runs_per_experiment):
+            orig_output = unet(*inputs)
+        torch.cuda.synchronize()
+        print(f"unet inference took {time.time() - start_time:.2f} seconds")
+
+# save the model
+unet_traced.save("unet_traced.pt")
+```
+
+Replace the pipeline's UNet with the traced version.
+
+```py
+import torch
+from diffusers import StableDiffusionPipeline
+from dataclasses import dataclass
+
+@dataclass
+class UNet2DConditionOutput:
+    sample: torch.Tensor
+
+pipeline = StableDiffusionPipeline.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5",
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+).to("cuda")
+
+# use jitted unet
+unet_traced = torch.jit.load("unet_traced.pt")
+
+# del pipeline.unet
+class TracedUNet(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.in_channels = pipe.unet.config.in_channels
+        self.device = pipe.unet.device
+
+    def forward(self, latent_model_input, t, encoder_hidden_states):
+        sample = unet_traced(latent_model_input, t, encoder_hidden_states)[0]
+        return UNet2DConditionOutput(sample=sample)
+
+pipeline.unet = TracedUNet()
+
+with torch.inference_mode():
+    image = pipe([prompt] * 1, num_inference_steps=50).images[0]
+```
+
 ## Memory-efficient attention
 
-Diffusers supports multiple memory-efficient attention backends (FlashAttention, xFormers, SageAttention, and more) through [`~ModelMixin.set_attention_backend`]. Refer to the [Attention backends](./attention_backends) guide to learn how to switch between them.
+> [!TIP]
+> Memory-efficient attention optimizes for memory usage *and* [inference speed](./fp16#scaled-dot-product-attention)!
+
+The Transformers attention mechanism is memory-intensive, especially for long sequences, so you can try using different and more memory-efficient attention types.
+
+By default, if PyTorch >= 2.0 is installed, [scaled dot-product attention (SDPA)](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) is used. You don't need to make any additional changes to your code.
+
+SDPA supports [FlashAttention](https://github.com/Dao-AILab/flash-attention) and [xFormers](https://github.com/facebookresearch/xformers) as well as a native C++ PyTorch implementation. It automatically selects the most optimal implementation based on your input.
+
+You can explicitly use xFormers with the [`~ModelMixin.enable_xformers_memory_efficient_attention`] method.
+
+```py
+# pip install xformers
+import torch
+from diffusers import StableDiffusionXLPipeline
+
+pipeline = StableDiffusionXLPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16,
+).to("cuda")
+pipeline.enable_xformers_memory_efficient_attention()
+```
+
+Call [`~ModelMixin.disable_xformers_memory_efficient_attention`] to disable it.
+
+```py
+pipeline.disable_xformers_memory_efficient_attention()
+```

docs/source/en/optimization/xformers.md

@@ -23,7 +23,7 @@ pip install xformers
 > [!TIP]
 > The xFormers `pip` package requires the latest version of PyTorch. If you need to use a previous version of PyTorch, then we recommend [installing xFormers from the source](https://github.com/facebookresearch/xformers#installing-xformers).
 
-After xFormers is installed, you can use it with [`~ModelMixin.set_attention_backend`] as shown in the [Attention backends](./attention_backends) guide.
+After xFormers is installed, you can use `enable_xformers_memory_efficient_attention()` for faster inference and reduced memory consumption as shown in this [section](memory#memory-efficient-attention).
 
 > [!WARNING]
 > According to this [issue](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212), xFormers `v0.0.16` cannot be used for training (fine-tune or DreamBooth) in some GPUs. If you observe this problem, please install a development version as indicated in the issue comments.

docs/source/zh/_toctree.yml

@@ -14,8 +14,6 @@
   sections:
   - local: using-diffusers/schedulers
     title: Load schedulers and models
-  - local: using-diffusers/guiders
-    title: Guiders
 
 - title: Inference
   isExpanded: false
@@ -82,6 +80,8 @@
     title: ModularPipeline
   - local: modular_diffusers/components_manager
     title: ComponentsManager
+  - local: modular_diffusers/guiders
+    title: Guiders
 
 - title: Training
   isExpanded: false

scripts/convert_sana_video_to_diffusers.py

@@ -12,7 +12,6 @@ from termcolor import colored
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from diffusers import (
-    AutoencoderKLLTX2Video,
     AutoencoderKLWan,
     DPMSolverMultistepScheduler,
     FlowMatchEulerDiscreteScheduler,
@@ -25,10 +24,7 @@ from diffusers.utils.import_utils import is_accelerate_available
 
 CTX = init_empty_weights if is_accelerate_available else nullcontext
 
-ckpt_ids = [
-    "Efficient-Large-Model/SANA-Video_2B_480p/checkpoints/SANA_Video_2B_480p.pth",
-    "Efficient-Large-Model/SANA-Video_2B_720p/checkpoints/SANA_Video_2B_720p_LTXVAE.pth",
-]
+ckpt_ids = ["Efficient-Large-Model/SANA-Video_2B_480p/checkpoints/SANA_Video_2B_480p.pth"]
 # https://github.com/NVlabs/Sana/blob/main/inference_video_scripts/inference_sana_video.py
 
 
@@ -96,22 +92,12 @@ def main(args):
     if args.video_size == 480:
         sample_size = 30  # Wan-VAE: 8xp2 downsample factor
         patch_size = (1, 2, 2)
-        in_channels = 16
-        out_channels = 16
     elif args.video_size == 720:
-        sample_size = 22  # DC-AE-V: 32xp1 downsample factor
+        sample_size = 22  # Wan-VAE: 32xp1 downsample factor
         patch_size = (1, 1, 1)
-        in_channels = 32
-        out_channels = 32
     else:
         raise ValueError(f"Video size {args.video_size} is not supported.")
 
-    if args.vae_type == "ltx2":
-        sample_size = 22
-        patch_size = (1, 1, 1)
-        in_channels = 128
-        out_channels = 128
-
     for depth in range(layer_num):
         # Transformer blocks.
         converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop(
@@ -196,8 +182,8 @@ def main(args):
     # Transformer
     with CTX():
         transformer_kwargs = {
-            "in_channels": in_channels,
-            "out_channels": out_channels,
+            "in_channels": 16,
+            "out_channels": 16,
             "num_attention_heads": 20,
             "attention_head_dim": 112,
             "num_layers": 20,
@@ -249,12 +235,9 @@ def main(args):
     else:
         print(colored(f"Saving the whole Pipeline containing {args.model_type}", "green", attrs=["bold"]))
         # VAE
-        if args.vae_type == "ltx2":
-            vae_path = args.vae_path or "Lightricks/LTX-2"
-            vae = AutoencoderKLLTX2Video.from_pretrained(vae_path, subfolder="vae", torch_dtype=torch.float32)
-        else:
-            vae_path = args.vae_path or "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-            vae = AutoencoderKLWan.from_pretrained(vae_path, subfolder="vae", torch_dtype=torch.float32)
+        vae = AutoencoderKLWan.from_pretrained(
+            "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32
+        )
 
         # Text Encoder
         text_encoder_model_path = "Efficient-Large-Model/gemma-2-2b-it"
@@ -331,23 +314,7 @@ if __name__ == "__main__":
         choices=["flow-dpm_solver", "flow-euler", "uni-pc"],
         help="Scheduler type to use.",
     )
-    parser.add_argument(
-        "--vae_type",
-        default="wan",
-        type=str,
-        choices=["wan", "ltx2"],
-        help="VAE type to use for saving full pipeline (ltx2 uses patchify 1x1x1).",
-    )
-    parser.add_argument(
-        "--vae_path",
-        default=None,
-        type=str,
-        required=False,
-        help="Optional VAE path or repo id. If not set, a default is used per VAE type.",
-    )
-    parser.add_argument(
-        "--task", default="t2v", type=str, required=True, choices=["t2v", "i2v"], help="Task to convert, t2v or i2v."
-    )
+    parser.add_argument("--task", default="t2v", type=str, required=True, help="Task to convert, t2v or i2v.")
     parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.")
     parser.add_argument("--save_full_pipeline", action="store_true", help="save all the pipeline elements in one.")
     parser.add_argument("--dtype", default="fp32", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.")

src/diffusers/pipelines/sana_video/pipeline_sana_video.py

@@ -24,7 +24,7 @@ from transformers import Gemma2PreTrainedModel, GemmaTokenizer, GemmaTokenizerFa
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...loaders import SanaLoraLoaderMixin
-from ...models import AutoencoderDC, AutoencoderKLLTX2Video, AutoencoderKLWan, SanaVideoTransformer3DModel
+from ...models import AutoencoderDC, AutoencoderKLWan, SanaVideoTransformer3DModel
 from ...schedulers import DPMSolverMultistepScheduler
 from ...utils import (
     BACKENDS_MAPPING,
@@ -194,7 +194,7 @@ class SanaVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             The tokenizer used to tokenize the prompt.
         text_encoder ([`Gemma2PreTrainedModel`]):
             Text encoder model to encode the input prompts.
-        vae ([`AutoencoderKLWan`, `AutoencoderDC`, or `AutoencoderKLLTX2Video`]):
+        vae ([`AutoencoderKLWan` or `AutoencoderDCAEV`]):
             Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
         transformer ([`SanaVideoTransformer3DModel`]):
             Conditional Transformer to denoise the input latents.
@@ -213,7 +213,7 @@ class SanaVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
         self,
         tokenizer: GemmaTokenizer | GemmaTokenizerFast,
         text_encoder: Gemma2PreTrainedModel,
-        vae: AutoencoderDC | AutoencoderKLLTX2Video | AutoencoderKLWan,
+        vae: AutoencoderDC | AutoencoderKLWan,
         transformer: SanaVideoTransformer3DModel,
         scheduler: DPMSolverMultistepScheduler,
     ):
@@ -223,19 +223,8 @@ class SanaVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
         )
 
-        if getattr(self, "vae", None):
-            if isinstance(self.vae, AutoencoderKLLTX2Video):
-                self.vae_scale_factor_temporal = self.vae.config.temporal_compression_ratio
-                self.vae_scale_factor_spatial = self.vae.config.spatial_compression_ratio
-            elif isinstance(self.vae, (AutoencoderDC, AutoencoderKLWan)):
-                self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal
-                self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial
-            else:
-                self.vae_scale_factor_temporal = 4
-                self.vae_scale_factor_spatial = 8
-        else:
-            self.vae_scale_factor_temporal = 4
-            self.vae_scale_factor_spatial = 8
+        self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal if getattr(self, "vae", None) else 4
+        self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 8
 
         self.vae_scale_factor = self.vae_scale_factor_spatial
 
@@ -996,21 +985,14 @@ class SanaVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
                 if is_torch_version(">=", "2.5.0")
                 else torch_accelerator_module.OutOfMemoryError
             )
-            if isinstance(self.vae, AutoencoderKLLTX2Video):
-                latents_mean = self.vae.latents_mean
-                latents_std = self.vae.latents_std
-                z_dim = self.vae.config.latent_channels
-            elif isinstance(self.vae, AutoencoderKLWan):
-                latents_mean = torch.tensor(self.vae.config.latents_mean)
-                latents_std = torch.tensor(self.vae.config.latents_std)
-                z_dim = self.vae.config.z_dim
-            else:
-                latents_mean = torch.zeros(latents.shape[1], device=latents.device, dtype=latents.dtype)
-                latents_std = torch.ones(latents.shape[1], device=latents.device, dtype=latents.dtype)
-                z_dim = latents.shape[1]
-
-            latents_mean = latents_mean.view(1, z_dim, 1, 1, 1).to(latents.device, latents.dtype)
-            latents_std = 1.0 / latents_std.view(1, z_dim, 1, 1, 1).to(latents.device, latents.dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+                latents.device, latents.dtype
+            )
             latents = latents / latents_std + latents_mean
             try:
                 video = self.vae.decode(latents, return_dict=False)[0]

src/diffusers/pipelines/sana_video/pipeline_sana_video_i2v.py

@@ -26,7 +26,7 @@ from transformers import Gemma2PreTrainedModel, GemmaTokenizer, GemmaTokenizerFa
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import PipelineImageInput
 from ...loaders import SanaLoraLoaderMixin
-from ...models import AutoencoderDC, AutoencoderKLLTX2Video, AutoencoderKLWan, SanaVideoTransformer3DModel
+from ...models import AutoencoderDC, AutoencoderKLWan, SanaVideoTransformer3DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
 from ...utils import (
     BACKENDS_MAPPING,
@@ -184,7 +184,7 @@ class SanaImageToVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             The tokenizer used to tokenize the prompt.
         text_encoder ([`Gemma2PreTrainedModel`]):
             Text encoder model to encode the input prompts.
-        vae ([`AutoencoderKLWan`, `AutoencoderDC`, or `AutoencoderKLLTX2Video`]):
+        vae ([`AutoencoderKLWan` or `AutoencoderDCAEV`]):
             Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
         transformer ([`SanaVideoTransformer3DModel`]):
             Conditional Transformer to denoise the input latents.
@@ -203,7 +203,7 @@ class SanaImageToVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
         self,
         tokenizer: GemmaTokenizer | GemmaTokenizerFast,
         text_encoder: Gemma2PreTrainedModel,
-        vae: AutoencoderDC | AutoencoderKLLTX2Video | AutoencoderKLWan,
+        vae: AutoencoderDC | AutoencoderKLWan,
         transformer: SanaVideoTransformer3DModel,
         scheduler: FlowMatchEulerDiscreteScheduler,
     ):
@@ -213,19 +213,8 @@ class SanaImageToVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
         )
 
-        if getattr(self, "vae", None):
-            if isinstance(self.vae, AutoencoderKLLTX2Video):
-                self.vae_scale_factor_temporal = self.vae.config.temporal_compression_ratio
-                self.vae_scale_factor_spatial = self.vae.config.spatial_compression_ratio
-            elif isinstance(self.vae, (AutoencoderDC, AutoencoderKLWan)):
-                self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal
-                self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial
-            else:
-                self.vae_scale_factor_temporal = 4
-                self.vae_scale_factor_spatial = 8
-        else:
-            self.vae_scale_factor_temporal = 4
-            self.vae_scale_factor_spatial = 8
+        self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal if getattr(self, "vae", None) else 4
+        self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 8
 
         self.vae_scale_factor = self.vae_scale_factor_spatial
 
@@ -698,18 +687,14 @@ class SanaImageToVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             image_latents = retrieve_latents(self.vae.encode(image), sample_mode="argmax")
             image_latents = image_latents.repeat(batch_size, 1, 1, 1, 1)
 
-        if isinstance(self.vae, AutoencoderKLLTX2Video):
-            _latents_mean = self.vae.latents_mean
-            _latents_std = self.vae.latents_std
-        elif isinstance(self.vae, AutoencoderKLWan):
-            _latents_mean = torch.tensor(self.vae.config.latents_mean)
-            _latents_std = torch.tensor(self.vae.config.latents_std)
-        else:
-            _latents_mean = torch.zeros(image_latents.shape[1], device=image_latents.device, dtype=image_latents.dtype)
-            _latents_std = torch.ones(image_latents.shape[1], device=image_latents.device, dtype=image_latents.dtype)
-
-        latents_mean = _latents_mean.view(1, -1, 1, 1, 1).to(image_latents.device, image_latents.dtype)
-        latents_std = 1.0 / _latents_std.view(1, -1, 1, 1, 1).to(image_latents.device, image_latents.dtype)
+        latents_mean = (
+            torch.tensor(self.vae.config.latents_mean)
+            .view(1, -1, 1, 1, 1)
+            .to(image_latents.device, image_latents.dtype)
+        )
+        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, -1, 1, 1, 1).to(
+            image_latents.device, image_latents.dtype
+        )
         image_latents = (image_latents - latents_mean) * latents_std
 
         latents[:, :, 0:1] = image_latents.to(dtype)
@@ -1049,21 +1034,14 @@ class SanaImageToVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
                 if is_torch_version(">=", "2.5.0")
                 else torch_accelerator_module.OutOfMemoryError
             )
-            if isinstance(self.vae, AutoencoderKLLTX2Video):
-                latents_mean = self.vae.latents_mean
-                latents_std = self.vae.latents_std
-                z_dim = self.vae.config.latent_channels
-            elif isinstance(self.vae, AutoencoderKLWan):
-                latents_mean = torch.tensor(self.vae.config.latents_mean)
-                latents_std = torch.tensor(self.vae.config.latents_std)
-                z_dim = self.vae.config.z_dim
-            else:
-                latents_mean = torch.zeros(latents.shape[1], device=latents.device, dtype=latents.dtype)
-                latents_std = torch.ones(latents.shape[1], device=latents.device, dtype=latents.dtype)
-                z_dim = latents.shape[1]
-
-            latents_mean = latents_mean.view(1, z_dim, 1, 1, 1).to(latents.device, latents.dtype)
-            latents_std = 1.0 / latents_std.view(1, z_dim, 1, 1, 1).to(latents.device, latents.dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+                latents.device, latents.dtype
+            )
             latents = latents / latents_std + latents_mean
             try:
                 video = self.vae.decode(latents, return_dict=False)[0]

tests/models/testing_utils/parallelism.py

@@ -26,17 +26,9 @@ from diffusers.models._modeling_parallel import ContextParallelConfig
 from ...testing_utils import (
     is_context_parallel,
     require_torch_multi_accelerator,
-    torch_device,
 )
 
 
-# Device configuration mapping
-DEVICE_CONFIG = {
-    "cuda": {"backend": "nccl", "module": torch.cuda},
-    "xpu": {"backend": "xccl", "module": torch.xpu},
-}
-
-
 def _find_free_port():
     """Find a free port on localhost."""
     with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
@@ -55,17 +47,12 @@ def _context_parallel_worker(rank, world_size, master_port, model_class, init_di
         os.environ["RANK"] = str(rank)
         os.environ["WORLD_SIZE"] = str(world_size)
 
-        # Get device configuration
-        device_config = DEVICE_CONFIG.get(torch_device, DEVICE_CONFIG["cuda"])
-        backend = device_config["backend"]
-        device_module = device_config["module"]
-
         # Initialize process group
-        dist.init_process_group(backend=backend, rank=rank, world_size=world_size)
+        dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
 
         # Set device for this process
-        device_module.set_device(rank)
-        device = torch.device(f"{torch_device}:{rank}")
+        torch.cuda.set_device(rank)
+        device = torch.device(f"cuda:{rank}")
 
         # Create model
         model = model_class(**init_dict)
@@ -116,16 +103,10 @@ def _custom_mesh_worker(
         os.environ["RANK"] = str(rank)
         os.environ["WORLD_SIZE"] = str(world_size)
 
-        # Get device configuration
-        device_config = DEVICE_CONFIG.get(torch_device, DEVICE_CONFIG["cuda"])
-        backend = device_config["backend"]
-        device_module = device_config["module"]
+        dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
 
-        dist.init_process_group(backend=backend, rank=rank, world_size=world_size)
-
-        # Set device for this process
-        device_module.set_device(rank)
-        device = torch.device(f"{torch_device}:{rank}")
+        torch.cuda.set_device(rank)
+        device = torch.device(f"cuda:{rank}")
 
         model = model_class(**init_dict)
         model.to(device)
@@ -135,7 +116,7 @@ def _custom_mesh_worker(
 
         # DeviceMesh must be created after init_process_group, inside each worker process.
         mesh = torch.distributed.device_mesh.init_device_mesh(
-            torch_device, mesh_shape=mesh_shape, mesh_dim_names=mesh_dim_names
+            "cuda", mesh_shape=mesh_shape, mesh_dim_names=mesh_dim_names
         )
         cp_config = ContextParallelConfig(**cp_dict, mesh=mesh)
         model.enable_parallelism(config=cp_config)

tests/models/transformers/test_models_transformer_qwenimage.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2025 HuggingFace Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,84 +13,49 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import warnings
+import unittest
 
 import torch
 
 from diffusers import QwenImageTransformer2DModel
 from diffusers.models.transformers.transformer_qwenimage import compute_text_seq_len_from_mask
-from diffusers.utils.torch_utils import randn_tensor
 
 from ...testing_utils import enable_full_determinism, torch_device
-from ..testing_utils import (
-    AttentionTesterMixin,
-    BaseModelTesterConfig,
-    BitsAndBytesTesterMixin,
-    ContextParallelTesterMixin,
-    LoraHotSwappingForModelTesterMixin,
-    LoraTesterMixin,
-    MemoryTesterMixin,
-    ModelTesterMixin,
-    TorchAoTesterMixin,
-    TorchCompileTesterMixin,
-    TrainingTesterMixin,
-)
+from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
 
 
 enable_full_determinism()
 
 
-class QwenImageTransformerTesterConfig(BaseModelTesterConfig):
-    @property
-    def model_class(self):
-        return QwenImageTransformer2DModel
+class QwenImageTransformerTests(ModelTesterMixin, unittest.TestCase):
+    model_class = QwenImageTransformer2DModel
+    main_input_name = "hidden_states"
+    # We override the items here because the transformer under consideration is small.
+    model_split_percents = [0.7, 0.6, 0.6]
+
+    # Skip setting testing with default: AttnProcessor
+    uses_custom_attn_processor = True
 
     @property
-    def output_shape(self) -> tuple[int, int]:
+    def dummy_input(self):
+        return self.prepare_dummy_input()
+
+    @property
+    def input_shape(self):
         return (16, 16)
 
     @property
-    def input_shape(self) -> tuple[int, int]:
+    def output_shape(self):
         return (16, 16)
 
-    @property
-    def model_split_percents(self) -> list:
-        return [0.7, 0.6, 0.6]
-
-    @property
-    def main_input_name(self) -> str:
-        return "hidden_states"
-
-    @property
-    def generator(self):
-        return torch.Generator("cpu").manual_seed(0)
-
-    def get_init_dict(self) -> dict[str, int | list[int]]:
-        return {
-            "patch_size": 2,
-            "in_channels": 16,
-            "out_channels": 4,
-            "num_layers": 2,
-            "attention_head_dim": 16,
-            "num_attention_heads": 4,
-            "joint_attention_dim": 16,
-            "guidance_embeds": False,
-            "axes_dims_rope": (8, 4, 4),
-        }
-
-    def get_dummy_inputs(self) -> dict[str, torch.Tensor]:
+    def prepare_dummy_input(self, height=4, width=4):
         batch_size = 1
         num_latent_channels = embedding_dim = 16
-        height = width = 4
-        sequence_length = 8
+        sequence_length = 7
         vae_scale_factor = 4
 
-        hidden_states = randn_tensor(
-            (batch_size, height * width, num_latent_channels), generator=self.generator, device=torch_device
-        )
-        encoder_hidden_states = randn_tensor(
-            (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
-        )
+        hidden_states = torch.randn((batch_size, height * width, num_latent_channels)).to(torch_device)
+        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
         encoder_hidden_states_mask = torch.ones((batch_size, sequence_length)).to(torch_device, torch.long)
         timestep = torch.tensor([1.0]).to(torch_device).expand(batch_size)
         orig_height = height * 2 * vae_scale_factor
@@ -104,57 +70,89 @@ class QwenImageTransformerTesterConfig(BaseModelTesterConfig):
             "img_shapes": img_shapes,
         }
 
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "patch_size": 2,
+            "in_channels": 16,
+            "out_channels": 4,
+            "num_layers": 2,
+            "attention_head_dim": 16,
+            "num_attention_heads": 3,
+            "joint_attention_dim": 16,
+            "guidance_embeds": False,
+            "axes_dims_rope": (8, 4, 4),
+        }
+
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    def test_gradient_checkpointing_is_applied(self):
+        expected_set = {"QwenImageTransformer2DModel"}
+        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
-class TestQwenImageTransformer(QwenImageTransformerTesterConfig, ModelTesterMixin):
     def test_infers_text_seq_len_from_mask(self):
-        init_dict = self.get_init_dict()
-        inputs = self.get_dummy_inputs()
+        """Test that compute_text_seq_len_from_mask correctly infers sequence lengths and returns tensors."""
+        init_dict, inputs = self.prepare_init_args_and_inputs_for_common()
         model = self.model_class(**init_dict).to(torch_device)
 
+        # Test 1: Contiguous mask with padding at the end (only first 2 tokens valid)
         encoder_hidden_states_mask = inputs["encoder_hidden_states_mask"].clone()
-        encoder_hidden_states_mask[:, 2:] = 0
+        encoder_hidden_states_mask[:, 2:] = 0  # Only first 2 tokens are valid
 
         rope_text_seq_len, per_sample_len, normalized_mask = compute_text_seq_len_from_mask(
             inputs["encoder_hidden_states"], encoder_hidden_states_mask
         )
 
-        assert isinstance(rope_text_seq_len, int)
-        assert isinstance(per_sample_len, torch.Tensor)
-        assert int(per_sample_len.max().item()) == 2
-        assert normalized_mask.dtype == torch.bool
-        assert normalized_mask.sum().item() == 2
-        assert rope_text_seq_len >= inputs["encoder_hidden_states"].shape[1]
+        # Verify rope_text_seq_len is returned as an int (for torch.compile compatibility)
+        self.assertIsInstance(rope_text_seq_len, int)
 
+        # Verify per_sample_len is computed correctly (max valid position + 1 = 2)
+        self.assertIsInstance(per_sample_len, torch.Tensor)
+        self.assertEqual(int(per_sample_len.max().item()), 2)
+
+        # Verify mask is normalized to bool dtype
+        self.assertTrue(normalized_mask.dtype == torch.bool)
+        self.assertEqual(normalized_mask.sum().item(), 2)  # Only 2 True values
+
+        # Verify rope_text_seq_len is at least the sequence length
+        self.assertGreaterEqual(rope_text_seq_len, inputs["encoder_hidden_states"].shape[1])
+
+        # Test 2: Verify model runs successfully with inferred values
         inputs["encoder_hidden_states_mask"] = normalized_mask
         with torch.no_grad():
             output = model(**inputs)
-        assert output.sample.shape[1] == inputs["hidden_states"].shape[1]
+        self.assertEqual(output.sample.shape[1], inputs["hidden_states"].shape[1])
 
+        # Test 3: Different mask pattern (padding at beginning)
         encoder_hidden_states_mask2 = inputs["encoder_hidden_states_mask"].clone()
-        encoder_hidden_states_mask2[:, :3] = 0
-        encoder_hidden_states_mask2[:, 3:] = 1
+        encoder_hidden_states_mask2[:, :3] = 0  # First 3 tokens are padding
+        encoder_hidden_states_mask2[:, 3:] = 1  # Last 4 tokens are valid
 
         rope_text_seq_len2, per_sample_len2, normalized_mask2 = compute_text_seq_len_from_mask(
             inputs["encoder_hidden_states"], encoder_hidden_states_mask2
         )
 
-        assert int(per_sample_len2.max().item()) == 8
-        assert normalized_mask2.sum().item() == 5
+        # Max valid position is 6 (last token), so per_sample_len should be 7
+        self.assertEqual(int(per_sample_len2.max().item()), 7)
+        self.assertEqual(normalized_mask2.sum().item(), 4)  # 4 True values
 
+        # Test 4: No mask provided (None case)
         rope_text_seq_len_none, per_sample_len_none, normalized_mask_none = compute_text_seq_len_from_mask(
             inputs["encoder_hidden_states"], None
         )
-        assert rope_text_seq_len_none == inputs["encoder_hidden_states"].shape[1]
-        assert isinstance(rope_text_seq_len_none, int)
-        assert per_sample_len_none is None
-        assert normalized_mask_none is None
+        self.assertEqual(rope_text_seq_len_none, inputs["encoder_hidden_states"].shape[1])
+        self.assertIsInstance(rope_text_seq_len_none, int)
+        self.assertIsNone(per_sample_len_none)
+        self.assertIsNone(normalized_mask_none)
 
     def test_non_contiguous_attention_mask(self):
-        init_dict = self.get_init_dict()
-        inputs = self.get_dummy_inputs()
+        """Test that non-contiguous masks work correctly (e.g., [1, 0, 1, 0, 1, 0, 0])"""
+        init_dict, inputs = self.prepare_init_args_and_inputs_for_common()
         model = self.model_class(**init_dict).to(torch_device)
 
+        # Create a non-contiguous mask pattern: valid, padding, valid, padding, etc.
         encoder_hidden_states_mask = inputs["encoder_hidden_states_mask"].clone()
+        # Pattern: [True, False, True, False, True, False, False]
         encoder_hidden_states_mask[:, 1] = 0
         encoder_hidden_states_mask[:, 3] = 0
         encoder_hidden_states_mask[:, 5:] = 0
@@ -162,85 +160,95 @@ class TestQwenImageTransformer(QwenImageTransformerTesterConfig, ModelTesterMixi
         inferred_rope_len, per_sample_len, normalized_mask = compute_text_seq_len_from_mask(
             inputs["encoder_hidden_states"], encoder_hidden_states_mask
         )
-        assert int(per_sample_len.max().item()) == 5
-        assert inferred_rope_len == inputs["encoder_hidden_states"].shape[1]
-        assert isinstance(inferred_rope_len, int)
-        assert normalized_mask.dtype == torch.bool
+        self.assertEqual(int(per_sample_len.max().item()), 5)
+        self.assertEqual(inferred_rope_len, inputs["encoder_hidden_states"].shape[1])
+        self.assertIsInstance(inferred_rope_len, int)
+        self.assertTrue(normalized_mask.dtype == torch.bool)
 
         inputs["encoder_hidden_states_mask"] = normalized_mask
 
         with torch.no_grad():
             output = model(**inputs)
 
-        assert output.sample.shape[1] == inputs["hidden_states"].shape[1]
+        self.assertEqual(output.sample.shape[1], inputs["hidden_states"].shape[1])
 
     def test_txt_seq_lens_deprecation(self):
-        init_dict = self.get_init_dict()
-        inputs = self.get_dummy_inputs()
+        """Test that passing txt_seq_lens raises a deprecation warning."""
+        init_dict, inputs = self.prepare_init_args_and_inputs_for_common()
         model = self.model_class(**init_dict).to(torch_device)
 
+        # Prepare inputs with txt_seq_lens (deprecated parameter)
         txt_seq_lens = [inputs["encoder_hidden_states"].shape[1]]
 
+        # Remove encoder_hidden_states_mask to use the deprecated path
         inputs_with_deprecated = inputs.copy()
         inputs_with_deprecated.pop("encoder_hidden_states_mask")
         inputs_with_deprecated["txt_seq_lens"] = txt_seq_lens
 
-        with warnings.catch_warnings(record=True) as w:
-            warnings.simplefilter("always")
+        # Test that deprecation warning is raised
+        with self.assertWarns(FutureWarning) as warning_context:
             with torch.no_grad():
                 output = model(**inputs_with_deprecated)
 
-            future_warnings = [x for x in w if issubclass(x.category, FutureWarning)]
-            assert len(future_warnings) > 0, "Expected FutureWarning to be raised"
+        # Verify the warning message mentions the deprecation
+        warning_message = str(warning_context.warning)
+        self.assertIn("txt_seq_lens", warning_message)
+        self.assertIn("deprecated", warning_message)
+        self.assertIn("encoder_hidden_states_mask", warning_message)
 
-            warning_message = str(future_warnings[0].message)
-            assert "txt_seq_lens" in warning_message
-            assert "deprecated" in warning_message
-
-        assert output.sample.shape[1] == inputs["hidden_states"].shape[1]
+        # Verify the model still works correctly despite the deprecation
+        self.assertEqual(output.sample.shape[1], inputs["hidden_states"].shape[1])
 
     def test_layered_model_with_mask(self):
+        """Test QwenImageTransformer2DModel with use_layer3d_rope=True (layered model)."""
+        # Create layered model config
         init_dict = {
             "patch_size": 2,
             "in_channels": 16,
             "out_channels": 4,
             "num_layers": 2,
             "attention_head_dim": 16,
-            "num_attention_heads": 4,
+            "num_attention_heads": 3,
             "joint_attention_dim": 16,
-            "axes_dims_rope": (8, 4, 4),
-            "use_layer3d_rope": True,
-            "use_additional_t_cond": True,
+            "axes_dims_rope": (8, 4, 4),  # Must match attention_head_dim (8+4+4=16)
+            "use_layer3d_rope": True,  # Enable layered RoPE
+            "use_additional_t_cond": True,  # Enable additional time conditioning
         }
 
         model = self.model_class(**init_dict).to(torch_device)
 
+        # Verify the model uses QwenEmbedLayer3DRope
         from diffusers.models.transformers.transformer_qwenimage import QwenEmbedLayer3DRope
 
-        assert isinstance(model.pos_embed, QwenEmbedLayer3DRope)
+        self.assertIsInstance(model.pos_embed, QwenEmbedLayer3DRope)
 
+        # Test single generation with layered structure
         batch_size = 1
-        text_seq_len = 8
+        text_seq_len = 7
         img_h, img_w = 4, 4
         layers = 4
 
+        # For layered model: (layers + 1) because we have N layers + 1 combined image
         hidden_states = torch.randn(batch_size, (layers + 1) * img_h * img_w, 16).to(torch_device)
         encoder_hidden_states = torch.randn(batch_size, text_seq_len, 16).to(torch_device)
 
+        # Create mask with some padding
         encoder_hidden_states_mask = torch.ones(batch_size, text_seq_len).to(torch_device)
-        encoder_hidden_states_mask[0, 5:] = 0
+        encoder_hidden_states_mask[0, 5:] = 0  # Only 5 valid tokens
 
         timestep = torch.tensor([1.0]).to(torch_device)
 
+        # additional_t_cond for use_additional_t_cond=True (0 or 1 index for embedding)
         addition_t_cond = torch.tensor([0], dtype=torch.long).to(torch_device)
 
+        # Layer structure: 4 layers + 1 condition image
         img_shapes = [
             [
-                (1, img_h, img_w),
-                (1, img_h, img_w),
-                (1, img_h, img_w),
-                (1, img_h, img_w),
-                (1, img_h, img_w),
+                (1, img_h, img_w),  # layer 0
+                (1, img_h, img_w),  # layer 1
+                (1, img_h, img_w),  # layer 2
+                (1, img_h, img_w),  # layer 3
+                (1, img_h, img_w),  # condition image (last one gets special treatment)
             ]
         ]
 
@@ -254,113 +262,37 @@ class TestQwenImageTransformer(QwenImageTransformerTesterConfig, ModelTesterMixi
                 additional_t_cond=addition_t_cond,
             )
 
-        assert output.sample.shape[1] == hidden_states.shape[1]
+        self.assertEqual(output.sample.shape[1], hidden_states.shape[1])
 
 
-class TestQwenImageTransformerMemory(QwenImageTransformerTesterConfig, MemoryTesterMixin):
-    """Memory optimization tests for QwenImage Transformer."""
+class QwenImageTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
+    model_class = QwenImageTransformer2DModel
 
+    def prepare_init_args_and_inputs_for_common(self):
+        return QwenImageTransformerTests().prepare_init_args_and_inputs_for_common()
 
-class TestQwenImageTransformerTraining(QwenImageTransformerTesterConfig, TrainingTesterMixin):
-    """Training tests for QwenImage Transformer."""
+    def prepare_dummy_input(self, height, width):
+        return QwenImageTransformerTests().prepare_dummy_input(height=height, width=width)
 
-    def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"QwenImageTransformer2DModel"}
-        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
-
-
-class TestQwenImageTransformerAttention(QwenImageTransformerTesterConfig, AttentionTesterMixin):
-    """Attention processor tests for QwenImage Transformer."""
-
-
-class TestQwenImageTransformerContextParallel(QwenImageTransformerTesterConfig, ContextParallelTesterMixin):
-    """Context Parallel inference tests for QwenImage Transformer."""
-
-
-class TestQwenImageTransformerLoRA(QwenImageTransformerTesterConfig, LoraTesterMixin):
-    """LoRA adapter tests for QwenImage Transformer."""
-
-
-class TestQwenImageTransformerLoRAHotSwap(QwenImageTransformerTesterConfig, LoraHotSwappingForModelTesterMixin):
-    """LoRA hot-swapping tests for QwenImage Transformer."""
-
-    @property
-    def different_shapes_for_compilation(self):
-        return [(4, 4), (4, 8), (8, 8)]
-
-    def get_dummy_inputs(self, height: int = 4, width: int = 4) -> dict[str, torch.Tensor]:
-        batch_size = 1
-        num_latent_channels = embedding_dim = 16
-        sequence_length = 8
-        vae_scale_factor = 4
-
-        hidden_states = randn_tensor(
-            (batch_size, height * width, num_latent_channels), generator=self.generator, device=torch_device
-        )
-        encoder_hidden_states = randn_tensor(
-            (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
-        )
-        encoder_hidden_states_mask = torch.ones((batch_size, sequence_length)).to(torch_device, torch.long)
-        timestep = torch.tensor([1.0]).to(torch_device).expand(batch_size)
-        orig_height = height * 2 * vae_scale_factor
-        orig_width = width * 2 * vae_scale_factor
-        img_shapes = [(1, orig_height // vae_scale_factor // 2, orig_width // vae_scale_factor // 2)] * batch_size
-
-        return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "encoder_hidden_states_mask": encoder_hidden_states_mask,
-            "timestep": timestep,
-            "img_shapes": img_shapes,
-        }
-
-
-class TestQwenImageTransformerCompile(QwenImageTransformerTesterConfig, TorchCompileTesterMixin):
-    """Torch compile tests for QwenImage Transformer."""
-
-    @property
-    def different_shapes_for_compilation(self):
-        return [(4, 4), (4, 8), (8, 8)]
-
-    def get_dummy_inputs(self, height: int = 4, width: int = 4) -> dict[str, torch.Tensor]:
-        batch_size = 1
-        num_latent_channels = embedding_dim = 16
-        sequence_length = 8
-        vae_scale_factor = 4
-
-        hidden_states = randn_tensor(
-            (batch_size, height * width, num_latent_channels), generator=self.generator, device=torch_device
-        )
-        encoder_hidden_states = randn_tensor(
-            (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
-        )
-        encoder_hidden_states_mask = torch.ones((batch_size, sequence_length)).to(torch_device, torch.long)
-        timestep = torch.tensor([1.0]).to(torch_device).expand(batch_size)
-        orig_height = height * 2 * vae_scale_factor
-        orig_width = width * 2 * vae_scale_factor
-        img_shapes = [(1, orig_height // vae_scale_factor // 2, orig_width // vae_scale_factor // 2)] * batch_size
-
-        return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "encoder_hidden_states_mask": encoder_hidden_states_mask,
-            "timestep": timestep,
-            "img_shapes": img_shapes,
-        }
+    def test_torch_compile_recompilation_and_graph_break(self):
+        super().test_torch_compile_recompilation_and_graph_break()
 
     def test_torch_compile_with_and_without_mask(self):
-        init_dict = self.get_init_dict()
-        inputs = self.get_dummy_inputs()
+        """Test that torch.compile works with both None mask and padding mask."""
+        init_dict, inputs = self.prepare_init_args_and_inputs_for_common()
         model = self.model_class(**init_dict).to(torch_device)
         model.eval()
         model.compile(mode="default", fullgraph=True)
 
+        # Test 1: Run with None mask (no padding, all tokens are valid)
         inputs_no_mask = inputs.copy()
         inputs_no_mask["encoder_hidden_states_mask"] = None
 
+        # First run to allow compilation
         with torch.no_grad():
             output_no_mask = model(**inputs_no_mask)
 
+        # Second run to verify no recompilation
         with (
             torch._inductor.utils.fresh_inductor_cache(),
             torch._dynamo.config.patch(error_on_recompile=True),
@@ -368,15 +300,19 @@ class TestQwenImageTransformerCompile(QwenImageTransformerTesterConfig, TorchCom
         ):
             output_no_mask_2 = model(**inputs_no_mask)
 
-        assert output_no_mask.sample.shape[1] == inputs["hidden_states"].shape[1]
-        assert output_no_mask_2.sample.shape[1] == inputs["hidden_states"].shape[1]
+        self.assertEqual(output_no_mask.sample.shape[1], inputs["hidden_states"].shape[1])
+        self.assertEqual(output_no_mask_2.sample.shape[1], inputs["hidden_states"].shape[1])
 
+        # Test 2: Run with all-ones mask (should behave like None)
         inputs_all_ones = inputs.copy()
-        assert inputs_all_ones["encoder_hidden_states_mask"].all().item()
+        # Keep the all-ones mask
+        self.assertTrue(inputs_all_ones["encoder_hidden_states_mask"].all().item())
 
+        # First run to allow compilation
         with torch.no_grad():
             output_all_ones = model(**inputs_all_ones)
 
+        # Second run to verify no recompilation
         with (
             torch._inductor.utils.fresh_inductor_cache(),
             torch._dynamo.config.patch(error_on_recompile=True),
@@ -384,18 +320,21 @@ class TestQwenImageTransformerCompile(QwenImageTransformerTesterConfig, TorchCom
         ):
             output_all_ones_2 = model(**inputs_all_ones)
 
-        assert output_all_ones.sample.shape[1] == inputs["hidden_states"].shape[1]
-        assert output_all_ones_2.sample.shape[1] == inputs["hidden_states"].shape[1]
+        self.assertEqual(output_all_ones.sample.shape[1], inputs["hidden_states"].shape[1])
+        self.assertEqual(output_all_ones_2.sample.shape[1], inputs["hidden_states"].shape[1])
 
+        # Test 3: Run with actual padding mask (has zeros)
         inputs_with_padding = inputs.copy()
         mask_with_padding = inputs["encoder_hidden_states_mask"].clone()
-        mask_with_padding[:, 4:] = 0
+        mask_with_padding[:, 4:] = 0  # Last 3 tokens are padding
 
         inputs_with_padding["encoder_hidden_states_mask"] = mask_with_padding
 
+        # First run to allow compilation
         with torch.no_grad():
             output_with_padding = model(**inputs_with_padding)
 
+        # Second run to verify no recompilation
         with (
             torch._inductor.utils.fresh_inductor_cache(),
             torch._dynamo.config.patch(error_on_recompile=True),
@@ -403,15 +342,8 @@ class TestQwenImageTransformerCompile(QwenImageTransformerTesterConfig, TorchCom
         ):
             output_with_padding_2 = model(**inputs_with_padding)
 
-        assert output_with_padding.sample.shape[1] == inputs["hidden_states"].shape[1]
-        assert output_with_padding_2.sample.shape[1] == inputs["hidden_states"].shape[1]
+        self.assertEqual(output_with_padding.sample.shape[1], inputs["hidden_states"].shape[1])
+        self.assertEqual(output_with_padding_2.sample.shape[1], inputs["hidden_states"].shape[1])
 
-        assert not torch.allclose(output_no_mask.sample, output_with_padding.sample, atol=1e-3)
-
-
-class TestQwenImageTransformerBitsAndBytes(QwenImageTransformerTesterConfig, BitsAndBytesTesterMixin):
-    """BitsAndBytes quantization tests for QwenImage Transformer."""
-
-
-class TestQwenImageTransformerTorchAo(QwenImageTransformerTesterConfig, TorchAoTesterMixin):
-    """TorchAO quantization tests for QwenImage Transformer."""
+        # Verify that outputs are different (mask should affect results)
+        self.assertFalse(torch.allclose(output_no_mask.sample, output_with_padding.sample, atol=1e-3))

tests/pipelines/helios/test_helios.py

@@ -139,9 +139,9 @@ class HeliosPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
         self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3))
 
-    @unittest.skip("Helios uses a lot of mixed precision internally, which is not suitable for this test case")
+    # Override to set a more lenient max diff threshold.
     def test_save_load_float16(self):
-        pass
+        super().test_save_load_float16(expected_max_diff=0.03)
 
     @unittest.skip("Test not supported")
     def test_attention_slicing_forward_pass(self):

tests/pipelines/hunyuan_video/test_hunyuan_image2video.py

@@ -139,9 +139,7 @@ class HunyuanVideoImageToVideoPipelineFastTests(
             num_hidden_layers=2,
             image_size=224,
         )
-        llava_text_encoder_config = LlavaConfig(
-            vision_config=vision_config, text_config=text_config, pad_token_id=100, image_token_index=101
-        )
+        llava_text_encoder_config = LlavaConfig(vision_config, text_config, pad_token_id=100, image_token_index=101)
 
         clip_text_encoder_config = CLIPTextConfig(
             bos_token_id=0,