Merge branch 'main' into auraflow-gguf

Author: AstraliteHeart

docs/source/en/tutorials/using_peft_for_inference.md

@@ -56,7 +56,7 @@ image
 
 With the `adapter_name` parameter, it is really easy to use another adapter for inference! Load the [nerijs/pixel-art-xl](https://huggingface.co/nerijs/pixel-art-xl) adapter that has been fine-tuned to generate pixel art images and call it `"pixel"`.
 
-The pipeline automatically sets the first loaded adapter (`"toy"`) as the active adapter, but you can activate the `"pixel"` adapter with the [`~PeftAdapterMixin.set_adapters`] method:
+The pipeline automatically sets the first loaded adapter (`"toy"`) as the active adapter, but you can activate the `"pixel"` adapter with the [`~loaders.peft.PeftAdapterMixin.set_adapters`] method:
 
 ```python
 pipe.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
@@ -85,7 +85,7 @@ By default, if the most up-to-date versions of PEFT and Transformers are detecte
 
 You can also merge different adapter checkpoints for inference to blend their styles together.
 
-Once again, use the [`~PeftAdapterMixin.set_adapters`] method to activate the `pixel` and `toy` adapters and specify the weights for how they should be merged.
+Once again, use the [`~loaders.peft.PeftAdapterMixin.set_adapters`] method to activate the `pixel` and `toy` adapters and specify the weights for how they should be merged.
 
 ```python
 pipe.set_adapters(["pixel", "toy"], adapter_weights=[0.5, 1.0])
@@ -114,7 +114,7 @@ Impressive! As you can see, the model generated an image that mixed the characte
 > [!TIP]
 > Through its PEFT integration, Diffusers also offers more efficient merging methods which you can learn about in the [Merge LoRAs](../using-diffusers/merge_loras) guide!
 
-To return to only using one adapter, use the [`~PeftAdapterMixin.set_adapters`] method to activate the `"toy"` adapter:
+To return to only using one adapter, use the [`~loaders.peft.PeftAdapterMixin.set_adapters`] method to activate the `"toy"` adapter:
 
 ```python
 pipe.set_adapters("toy")
@@ -127,7 +127,7 @@ image = pipe(
 image
 ```
 
-Or to disable all adapters entirely, use the [`~PeftAdapterMixin.disable_lora`] method to return the base model.
+Or to disable all adapters entirely, use the [`~loaders.peft.PeftAdapterMixin.disable_lora`] method to return the base model.
 
 ```python
 pipe.disable_lora()
@@ -141,7 +141,7 @@ image
 
 ### Customize adapters strength
 
-For even more customization, you can control how strongly the adapter affects each part of the pipeline. For this, pass a dictionary with the control strengths (called "scales") to [`~PeftAdapterMixin.set_adapters`].
+For even more customization, you can control how strongly the adapter affects each part of the pipeline. For this, pass a dictionary with the control strengths (called "scales") to [`~loaders.peft.PeftAdapterMixin.set_adapters`].
 
 For example, here's how you can turn on the adapter for the `down` parts, but turn it off for the `mid` and `up` parts:
 ```python
@@ -214,7 +214,7 @@ list_adapters_component_wise
 {"text_encoder": ["toy", "pixel"], "unet": ["toy", "pixel"], "text_encoder_2": ["toy", "pixel"]}
 ```
 
-The [`~PeftAdapterMixin.delete_adapters`] function completely removes an adapter and their LoRA layers from a model.
+The [`~loaders.peft.PeftAdapterMixin.delete_adapters`] function completely removes an adapter and their LoRA layers from a model.
 
 ```py
 pipe.delete_adapters("toy")

examples/community/rerender_a_video.py

@@ -30,10 +30,17 @@
 from diffusers.pipelines.controlnet.pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import BaseOutput, deprecate, logging
+from diffusers.utils import BaseOutput, deprecate, is_torch_xla_available, logging
 from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -775,7 +782,7 @@ def __call__(
         self.attn_state.reset()
 
         # 4.1 prepare frames
-        image = self.image_processor.preprocess(frames[0]).to(dtype=torch.float32)
+        image = self.image_processor.preprocess(frames[0]).to(dtype=self.dtype)
         first_image = image[0]  # C, H, W
 
         # 4.2 Prepare controlnet_conditioning_image
@@ -919,8 +926,8 @@ def __call__(
             prev_image = frames[idx - 1]
             control_image = control_frames[idx]
             # 5.1 prepare frames
-            image = self.image_processor.preprocess(image).to(dtype=torch.float32)
-            prev_image = self.image_processor.preprocess(prev_image).to(dtype=torch.float32)
+            image = self.image_processor.preprocess(image).to(dtype=self.dtype)
+            prev_image = self.image_processor.preprocess(prev_image).to(dtype=self.dtype)
 
             warped_0, bwd_occ_0, bwd_flow_0 = get_warped_and_mask(
                 self.flow_model, first_image, image[0], first_result, False, self.device
@@ -1100,6 +1107,9 @@ def denoising_loop(latents, mask=None, xtrg=None, noise_rescale=None):
                             if callback is not None and i % callback_steps == 0:
                                 callback(i, t, latents)
 
+                        if XLA_AVAILABLE:
+                            xm.mark_step()
+
                     return latents
 
             if mask_start_t <= mask_end_t:

examples/flux-control/train_control_lora_flux.py

@@ -923,11 +923,28 @@ def load_model_hook(models, input_dir):
                         transformer_ = model
                     else:
                         raise ValueError(f"unexpected save model: {model.__class__}")
-
             else:
                 transformer_ = FluxTransformer2DModel.from_pretrained(
                     args.pretrained_model_name_or_path, subfolder="transformer"
                 ).to(accelerator.device, weight_dtype)
+
+                # Handle input dimension doubling before adding adapter
+                with torch.no_grad():
+                    initial_input_channels = transformer_.config.in_channels
+                    new_linear = torch.nn.Linear(
+                        transformer_.x_embedder.in_features * 2,
+                        transformer_.x_embedder.out_features,
+                        bias=transformer_.x_embedder.bias is not None,
+                        dtype=transformer_.dtype,
+                        device=transformer_.device,
+                    )
+                    new_linear.weight.zero_()
+                    new_linear.weight[:, :initial_input_channels].copy_(transformer_.x_embedder.weight)
+                    if transformer_.x_embedder.bias is not None:
+                        new_linear.bias.copy_(transformer_.x_embedder.bias)
+                    transformer_.x_embedder = new_linear
+                    transformer_.register_to_config(in_channels=initial_input_channels * 2)
+
                 transformer_.add_adapter(transformer_lora_config)
 
             lora_state_dict = FluxControlPipeline.lora_state_dict(input_dir)

examples/text_to_image/train_text_to_image_sdxl.py

@@ -919,7 +919,7 @@ def preprocess_train(examples):
         # fingerprint used by the cache for the other processes to load the result
         # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
         new_fingerprint = Hasher.hash(args)
-        new_fingerprint_for_vae = Hasher.hash(vae_path)
+        new_fingerprint_for_vae = Hasher.hash((vae_path, args))
         train_dataset_with_embeddings = train_dataset.map(
             compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint
         )

src/diffusers/loaders/lora_pipeline.py

@@ -2466,7 +2466,9 @@ def _maybe_expand_lora_state_dict(cls, transformer, lora_state_dict):
                 continue
 
             base_param_name = (
-                f"{k.replace(prefix, '')}.base_layer.weight" if is_peft_loaded else f"{k.replace(prefix, '')}.weight"
+                f"{k.replace(prefix, '')}.base_layer.weight"
+                if is_peft_loaded and f"{k.replace(prefix, '')}.base_layer.weight" in transformer_state_dict
+                else f"{k.replace(prefix, '')}.weight"
             )
             base_weight_param = transformer_state_dict[base_param_name]
             lora_A_param = lora_state_dict[f"{prefix}{k}.lora_A.weight"]

src/diffusers/models/embeddings.py

@@ -1248,7 +1248,8 @@ def forward(self, ids: torch.Tensor) -> torch.Tensor:
         sin_out = []
         pos = ids.float()
         is_mps = ids.device.type == "mps"
-        freqs_dtype = torch.float32 if is_mps else torch.float64
+        is_npu = ids.device.type == "npu"
+        freqs_dtype = torch.float32 if (is_mps or is_npu) else torch.float64
         for i in range(n_axes):
             cos, sin = get_1d_rotary_pos_embed(
                 self.axes_dim[i],

src/diffusers/models/transformers/sana_transformer.py

@@ -250,14 +250,14 @@ def __init__(
         inner_dim = num_attention_heads * attention_head_dim
 
         # 1. Patch Embedding
-        interpolation_scale = interpolation_scale if interpolation_scale is not None else max(sample_size // 64, 1)
         self.patch_embed = PatchEmbed(
             height=sample_size,
             width=sample_size,
             patch_size=patch_size,
             in_channels=in_channels,
             embed_dim=inner_dim,
             interpolation_scale=interpolation_scale,
+            pos_embed_type="sincos" if interpolation_scale is not None else None,
         )
 
         # 2. Additional condition embeddings

src/diffusers/models/transformers/transformer_flux.py

@@ -85,11 +85,11 @@ def __init__(self, dim, num_attention_heads, attention_head_dim, mlp_ratio=4.0):
 
     def forward(
         self,
-        hidden_states: torch.FloatTensor,
-        temb: torch.FloatTensor,
-        image_rotary_emb=None,
-        joint_attention_kwargs=None,
-    ):
+        hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> torch.Tensor:
         residual = hidden_states
         norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
         mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))
@@ -117,15 +117,22 @@ class FluxTransformerBlock(nn.Module):
 
     Reference: https://arxiv.org/abs/2403.03206
 
-    Parameters:
-        dim (`int`): The number of channels in the input and output.
-        num_attention_heads (`int`): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`): The number of channels in each head.
-        context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the
-            processing of `context` conditions.
+    Args:
+        dim (`int`):
+            The embedding dimension of the block.
+        num_attention_heads (`int`):
+            The number of attention heads to use.
+        attention_head_dim (`int`):
+            The number of dimensions to use for each attention head.
+        qk_norm (`str`, defaults to `"rms_norm"`):
+            The normalization to use for the query and key tensors.
+        eps (`float`, defaults to `1e-6`):
+            The epsilon value to use for the normalization.
     """
 
-    def __init__(self, dim, num_attention_heads, attention_head_dim, qk_norm="rms_norm", eps=1e-6):
+    def __init__(
+        self, dim: int, num_attention_heads: int, attention_head_dim: int, qk_norm: str = "rms_norm", eps: float = 1e-6
+    ):
         super().__init__()
 
         self.norm1 = AdaLayerNormZero(dim)
@@ -164,12 +171,12 @@ def __init__(self, dim, num_attention_heads, attention_head_dim, qk_norm="rms_no
 
     def forward(
         self,
-        hidden_states: torch.FloatTensor,
-        encoder_hidden_states: torch.FloatTensor,
-        temb: torch.FloatTensor,
-        image_rotary_emb=None,
-        joint_attention_kwargs=None,
-    ):
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
 
         norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
@@ -227,16 +234,30 @@ class FluxTransformer2DModel(
 
     Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
 
-    Parameters:
-        patch_size (`int`): Patch size to turn the input data into small patches.
-        in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
-        num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use.
-        num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use.
-        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
-        num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
-        joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
-        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
-        guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
+    Args:
+        patch_size (`int`, defaults to `1`):
+            Patch size to turn the input data into small patches.
+        in_channels (`int`, defaults to `64`):
+            The number of channels in the input.
+        out_channels (`int`, *optional*, defaults to `None`):
+            The number of channels in the output. If not specified, it defaults to `in_channels`.
+        num_layers (`int`, defaults to `19`):
+            The number of layers of dual stream DiT blocks to use.
+        num_single_layers (`int`, defaults to `38`):
+            The number of layers of single stream DiT blocks to use.
+        attention_head_dim (`int`, defaults to `128`):
+            The number of dimensions to use for each attention head.
+        num_attention_heads (`int`, defaults to `24`):
+            The number of attention heads to use.
+        joint_attention_dim (`int`, defaults to `4096`):
+            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)`):
+            The dimensions to use for the rotary positional embeddings.
     """
 
     _supports_gradient_checkpointing = True
@@ -259,39 +280,39 @@ def __init__(
     ):
         super().__init__()
         self.out_channels = out_channels or in_channels
-        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
+        self.inner_dim = num_attention_heads * attention_head_dim
 
         self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
 
         text_time_guidance_cls = (
             CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
         )
         self.time_text_embed = text_time_guidance_cls(
-            embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim
+            embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
         )
 
-        self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.inner_dim)
-        self.x_embedder = nn.Linear(self.config.in_channels, self.inner_dim)
+        self.context_embedder = nn.Linear(joint_attention_dim, self.inner_dim)
+        self.x_embedder = nn.Linear(in_channels, self.inner_dim)
 
         self.transformer_blocks = nn.ModuleList(
             [
                 FluxTransformerBlock(
                     dim=self.inner_dim,
-                    num_attention_heads=self.config.num_attention_heads,
-                    attention_head_dim=self.config.attention_head_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
                 )
-                for i in range(self.config.num_layers)
+                for _ in range(num_layers)
             ]
         )
 
         self.single_transformer_blocks = nn.ModuleList(
             [
                 FluxSingleTransformerBlock(
                     dim=self.inner_dim,
-                    num_attention_heads=self.config.num_attention_heads,
-                    attention_head_dim=self.config.attention_head_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
                 )
-                for i in range(self.config.num_single_layers)
+                for _ in range(num_single_layers)
             ]
         )
 
@@ -418,16 +439,16 @@ def forward(
         controlnet_single_block_samples=None,
         return_dict: bool = True,
         controlnet_blocks_repeat: bool = False,
-    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
         """
         The [`FluxTransformer2DModel`] forward method.
 
         Args:
-            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+            hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
                 Input `hidden_states`.
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+            encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
                 Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
-            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+            pooled_projections (`torch.Tensor` of shape `(batch_size, projection_dim)`): Embeddings projected
                 from the embeddings of input conditions.
             timestep ( `torch.LongTensor`):
                 Used to indicate denoising step.

src/diffusers/pipelines/auto_pipeline.py

@@ -528,7 +528,9 @@ def from_pipe(cls, pipeline, **kwargs):
             if k not in text_2_image_kwargs
         }
 
-        missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(text_2_image_kwargs.keys())
+        missing_modules = (
+            set(expected_modules) - set(text_2_image_cls._optional_components) - set(text_2_image_kwargs.keys())
+        )
 
         if len(missing_modules) > 0:
             raise ValueError(
@@ -838,7 +840,9 @@ def from_pipe(cls, pipeline, **kwargs):
             if k not in image_2_image_kwargs
         }
 
-        missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(image_2_image_kwargs.keys())
+        missing_modules = (
+            set(expected_modules) - set(image_2_image_cls._optional_components) - set(image_2_image_kwargs.keys())
+        )
 
         if len(missing_modules) > 0:
             raise ValueError(
@@ -1141,7 +1145,9 @@ def from_pipe(cls, pipeline, **kwargs):
             if k not in inpainting_kwargs
         }
 
-        missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(inpainting_kwargs.keys())
+        missing_modules = (
+            set(expected_modules) - set(inpainting_cls._optional_components) - set(inpainting_kwargs.keys())
+        )
 
         if len(missing_modules) > 0:
             raise ValueError(