Merge branch 'main' into tighten-compile-quant-tests

Author: sayakpaul

docs/source/en/_toctree.yml

@@ -366,6 +366,8 @@
         title: PixArtTransformer2DModel
       - local: api/models/prior_transformer
         title: PriorTransformer
+      - local: api/models/qwenimage_transformer2d
+        title: QwenImageTransformer2DModel
       - local: api/models/sana_transformer2d
         title: SanaTransformer2DModel
       - local: api/models/sd3_transformer2d
@@ -418,6 +420,8 @@
         title: AutoencoderKLMagvit
       - local: api/models/autoencoderkl_mochi
         title: AutoencoderKLMochi
+      - local: api/models/autoencoderkl_qwenimage
+        title: AutoencoderKLQwenImage
       - local: api/models/autoencoder_kl_wan
         title: AutoencoderKLWan
       - local: api/models/consistency_decoder_vae
@@ -554,6 +558,8 @@
       title: PixArt-α
     - local: api/pipelines/pixart_sigma
       title: PixArt-Σ
+    - local: api/pipelines/qwenimage
+      title: QwenImage
     - local: api/pipelines/sana
       title: Sana
     - local: api/pipelines/sana_sprint

docs/source/en/api/models/autoencoderkl_qwenimage.md

@@ -0,0 +1,35 @@
+<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# AutoencoderKLQwenImage
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AutoencoderKLQwenImage
+
+vae = AutoencoderKLQwenImage.from_pretrained("Qwen/QwenImage-20B", subfolder="vae")
+```
+
+## AutoencoderKLQwenImage
+
+[[autodoc]] AutoencoderKLQwenImage
+    - decode
+    - encode
+    - all
+
+## AutoencoderKLOutput
+
+[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/models/qwenimage_transformer2d.md

@@ -0,0 +1,28 @@
+<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# QwenImageTransformer2DModel
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import QwenImageTransformer2DModel
+
+transformer = QwenImageTransformer2DModel.from_pretrained("Qwen/QwenImage-20B", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## QwenImageTransformer2DModel
+
+[[autodoc]] QwenImageTransformer2DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -0,0 +1,33 @@
+<!-- Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License. -->
+
+# QwenImage
+
+<!-- TODO: update this section when model is out -->
+
+<Tip>
+
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+## QwenImagePipeline
+
+[[autodoc]] QwenImagePipeline
+  - all
+  - __call__
+
+## QwenImagePipeline
+
+[[autodoc]] pipelines.qwenimage.pipeline_output.QwenImagePipelineOutput

src/diffusers/models/autoencoders/autoencoder_kl_qwenimage.py

@@ -1,4 +1,4 @@
-# Copyright 2025 The Qwen-Image Team and The HuggingFace Team. All rights reserved.
+# Copyright 2025 The Qwen-Image Team, Wan Team and The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,6 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+#
+# We gratefully acknowledge the Wan Team for their outstanding contributions.
+# QwenImageVAE is further fine-tuned from the Wan Video VAE to achieve improved performance.
+# For more information about the Wan VAE, please refer to:
+# - GitHub: https://github.com/Wan-Video/Wan2.1
+# - arXiv: https://arxiv.org/abs/2503.20314
 
 from typing import List, Optional, Tuple, Union
 
@@ -668,6 +674,7 @@ class AutoencoderKLQwenImage(ModelMixin, ConfigMixin, FromOriginalModelMixin):
 
     _supports_gradient_checkpointing = False
 
+    # fmt: off
     @register_to_config
     def __init__(
         self,
@@ -678,43 +685,10 @@ def __init__(
         attn_scales: List[float] = [],
         temperal_downsample: List[bool] = [False, True, True],
         dropout: float = 0.0,
-        latents_mean: List[float] = [
-            -0.7571,
-            -0.7089,
-            -0.9113,
-            0.1075,
-            -0.1745,
-            0.9653,
-            -0.1517,
-            1.5508,
-            0.4134,
-            -0.0715,
-            0.5517,
-            -0.3632,
-            -0.1922,
-            -0.9497,
-            0.2503,
-            -0.2921,
-        ],
-        latents_std: List[float] = [
-            2.8184,
-            1.4541,
-            2.3275,
-            2.6558,
-            1.2196,
-            1.7708,
-            2.6052,
-            2.0743,
-            3.2687,
-            2.1526,
-            2.8652,
-            1.5579,
-            1.6382,
-            1.1253,
-            2.8251,
-            1.9160,
-        ],
+        latents_mean: List[float] = [-0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921],
+        latents_std: List[float] = [2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160],
     ) -> None:
+    # fmt: on
         super().__init__()
 
         self.z_dim = z_dim

src/diffusers/models/transformers/transformer_qwenimage.py

@@ -140,7 +140,7 @@ def apply_rotary_emb_qwen(
 
 
 class QwenTimestepProjEmbeddings(nn.Module):
-    def __init__(self, embedding_dim, pooled_projection_dim):
+    def __init__(self, embedding_dim):
         super().__init__()
 
         self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1000)
@@ -473,8 +473,6 @@ class QwenImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, Fro
         joint_attention_dim (`int`, defaults to `3584`):
             The number of dimensions to use for the joint attention (embedding/channel dimension of
             `encoder_hidden_states`).
-        pooled_projection_dim (`int`, defaults to `768`):
-            The number of dimensions to use for the pooled projection.
         guidance_embeds (`bool`, defaults to `False`):
             Whether to use guidance embeddings for guidance-distilled variant of the model.
         axes_dims_rope (`Tuple[int]`, defaults to `(16, 56, 56)`):
@@ -495,8 +493,7 @@ def __init__(
         attention_head_dim: int = 128,
         num_attention_heads: int = 24,
         joint_attention_dim: int = 3584,
-        pooled_projection_dim: int = 768,
-        guidance_embeds: bool = False,
+        guidance_embeds: bool = False,  # TODO: this should probably be removed
         axes_dims_rope: Tuple[int, int, int] = (16, 56, 56),
     ):
         super().__init__()
@@ -505,9 +502,7 @@ def __init__(
 
         self.pos_embed = QwenEmbedRope(theta=10000, axes_dim=list(axes_dims_rope), scale_rope=True)
 
-        self.time_text_embed = QwenTimestepProjEmbeddings(
-            embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
-        )
+        self.time_text_embed = QwenTimestepProjEmbeddings(embedding_dim=self.inner_dim)
 
         self.txt_norm = RMSNorm(joint_attention_dim, eps=1e-6)
 
@@ -538,10 +533,9 @@ def forward(
         timestep: torch.LongTensor = None,
         img_shapes: Optional[List[Tuple[int, int, int]]] = None,
         txt_seq_lens: Optional[List[int]] = None,
-        guidance: torch.Tensor = None,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance: torch.Tensor = None,  # TODO: this should probably be removed
+        attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
-        controlnet_blocks_repeat: bool = False,
     ) -> Union[torch.Tensor, Transformer2DModelOutput]:
         """
         The [`QwenTransformer2DModel`] forward method.
@@ -555,7 +549,7 @@ def forward(
                 Mask of the input conditions.
             timestep ( `torch.LongTensor`):
                 Used to indicate denoising step.
-            joint_attention_kwargs (`dict`, *optional*):
+            attention_kwargs (`dict`, *optional*):
                 A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                 `self.processor` in
                 [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
@@ -567,17 +561,17 @@ def forward(
             If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
             `tuple` where the first element is the sample tensor.
         """
-        if joint_attention_kwargs is not None:
-            joint_attention_kwargs = joint_attention_kwargs.copy()
-            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        if attention_kwargs is not None:
+            attention_kwargs = attention_kwargs.copy()
+            lora_scale = attention_kwargs.pop("scale", 1.0)
         else:
             lora_scale = 1.0
 
         if USE_PEFT_BACKEND:
             # weight the lora layers by setting `lora_scale` for each PEFT layer
             scale_lora_layers(self, lora_scale)
         else:
-            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
                 logger.warning(
                     "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
                 )
@@ -617,7 +611,7 @@ def forward(
                     encoder_hidden_states_mask=encoder_hidden_states_mask,
                     temb=temb,
                     image_rotary_emb=image_rotary_emb,
-                    joint_attention_kwargs=joint_attention_kwargs,
+                    joint_attention_kwargs=attention_kwargs,
                 )
 
         # Use only the image part (hidden_states) from the dual-stream blocks

src/diffusers/models/transformers/transformer_wan.py

@@ -180,6 +180,7 @@ def __init__(
         added_kv_proj_dim: Optional[int] = None,
         cross_attention_dim_head: Optional[int] = None,
         processor=None,
+        is_cross_attention=None,
     ):
         super().__init__()
 
@@ -207,6 +208,8 @@ def __init__(
             self.add_v_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=True)
             self.norm_added_k = torch.nn.RMSNorm(dim_head * heads, eps=eps)
 
+        self.is_cross_attention = cross_attention_dim_head is not None
+
         self.set_processor(processor)
 
     def fuse_projections(self):
@@ -324,7 +327,7 @@ def forward(
     ):
         timestep = self.timesteps_proj(timestep)
         if timestep_seq_len is not None:
-            timestep = timestep.unflatten(0, (1, timestep_seq_len))
+            timestep = timestep.unflatten(0, (-1, timestep_seq_len))
 
         time_embedder_dtype = next(iter(self.time_embedder.parameters())).dtype
         if timestep.dtype != time_embedder_dtype and time_embedder_dtype != torch.int8: