style

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

@@ -99,9 +99,3 @@ image.save("chroma-single-file.png")
 [[autodoc]] ChromaImg2ImgPipeline
 	- all
 	- __call__
-
-## ChromaInpaintPipeline
-
-[[autodoc]] ChromaInpaintPipeline
-  - all
-  - __call__

src/diffusers/__init__.py

@@ -460,7 +460,6 @@ else:
             "BriaFiboPipeline",
             "BriaPipeline",
             "ChromaImg2ImgPipeline",
-            "ChromaInpaintPipeline",
             "ChromaPipeline",
             "ChronoEditPipeline",
             "CLIPImageProjection",
@@ -1187,7 +1186,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             BriaFiboPipeline,
             BriaPipeline,
             ChromaImg2ImgPipeline,
-            ChromaInpaintPipeline,
             ChromaPipeline,
             ChronoEditPipeline,
             CLIPImageProjection,

src/diffusers/models/attention_dispatch.py

@@ -1573,6 +1573,8 @@ def _templated_context_parallel_attention(
     backward_op,
     _parallel_config: Optional["ParallelConfig"] = None,
 ):
+    if attn_mask is not None:
+        raise ValueError("Attention mask is not yet supported for templated attention.")
     if is_causal:
         raise ValueError("Causal attention is not yet supported for templated attention.")
     if enable_gqa:

src/diffusers/models/transformers/transformer_qwenimage.py

@@ -761,14 +761,11 @@ class QwenImageTransformer2DModel(
     _no_split_modules = ["QwenImageTransformerBlock"]
     _skip_layerwise_casting_patterns = ["pos_embed", "norm"]
     _repeated_blocks = ["QwenImageTransformerBlock"]
-    # Make CP plan compatible with https://github.com/huggingface/diffusers/pull/12702
     _cp_plan = {
-        "transformer_blocks.0": {
+        "": {
             "hidden_states": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
             "encoder_hidden_states": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
-        },
-        "transformer_blocks.*": {
-            "modulate_index": ContextParallelInput(split_dim=1, expected_dims=2, split_output=False),
+            "encoder_hidden_states_mask": ContextParallelInput(split_dim=1, expected_dims=2, split_output=False),
         },
         "pos_embed": {
             0: ContextParallelInput(split_dim=0, expected_dims=2, split_output=True),

src/diffusers/modular_pipelines/mellon_node_utils.py

@@ -23,18 +23,10 @@ logger = logging.getLogger(__name__)
 @dataclass(frozen=True)
 class MellonParam:
     """
-        Parameter definition for Mellon nodes.
+    Parameter definition for Mellon nodes.
 
-        Use factory methods for common params (e.g., MellonParam.seed()) or create custom ones with
-        MellonParam(name="...", label="...", type="...").
-
-        Example:
-    ```python
-            # Custom param
-            MellonParam(name="my_param", label="My Param", type="float", default=0.5)
-            # Output in Mellon node definition:
-            # "my_param": {"label": "My Param", "type": "float", "default": 0.5}
-    ```
+    Use factory methods for common params (e.g., MellonParam.seed()) or create custom ones with MellonParam(name="...",
+    label="...", type="...").
     """
 
     name: str
@@ -59,32 +51,14 @@ class MellonParam:
 
     @classmethod
     def image(cls) -> "MellonParam":
-        """
-        Image input parameter.
-
-        Mellon node definition:
-            "image": {"label": "Image", "type": "image", "display": "input"}
-        """
         return cls(name="image", label="Image", type="image", display="input", required_block_params=["image"])
 
     @classmethod
     def images(cls) -> "MellonParam":
-        """
-        Images output parameter.
-
-        Mellon node definition:
-            "images": {"label": "Images", "type": "image", "display": "output"}
-        """
         return cls(name="images", label="Images", type="image", display="output", required_block_params=["images"])
 
     @classmethod
     def control_image(cls, display: str = "input") -> "MellonParam":
-        """
-        Control image parameter for ControlNet.
-
-        Mellon node definition (display="input"):
-            "control_image": {"label": "Control Image", "type": "image", "display": "input"}
-        """
         return cls(
             name="control_image",
             label="Control Image",
@@ -95,25 +69,10 @@ class MellonParam:
 
     @classmethod
     def latents(cls, display: str = "input") -> "MellonParam":
-        """
-        Latents parameter.
-
-        Mellon node definition (display="input"):
-            "latents": {"label": "Latents", "type": "latents", "display": "input"}
-
-        Mellon node definition (display="output"):
-            "latents": {"label": "Latents", "type": "latents", "display": "output"}
-        """
         return cls(name="latents", label="Latents", type="latents", display=display, required_block_params=["latents"])
 
     @classmethod
     def image_latents(cls, display: str = "input") -> "MellonParam":
-        """
-        Image latents parameter for img2img workflows.
-
-        Mellon node definition (display="input"):
-            "image_latents": {"label": "Image Latents", "type": "latents", "display": "input"}
-        """
         return cls(
             name="image_latents",
             label="Image Latents",
@@ -124,12 +83,6 @@ class MellonParam:
 
     @classmethod
     def first_frame_latents(cls, display: str = "input") -> "MellonParam":
-        """
-        First frame latents for video generation.
-
-        Mellon node definition (display="input"):
-            "first_frame_latents": {"label": "First Frame Latents", "type": "latents", "display": "input"}
-        """
         return cls(
             name="first_frame_latents",
             label="First Frame Latents",
@@ -140,16 +93,6 @@ class MellonParam:
 
     @classmethod
     def image_latents_with_strength(cls) -> "MellonParam":
-        """
-        Image latents with strength-based onChange behavior. When connected, shows strength slider; when disconnected,
-        shows height/width.
-
-        Mellon node definition:
-            "image_latents": {
-                "label": "Image Latents", "type": "latents", "display": "input", "onChange": {"false": ["height",
-                "width"], "true": ["strength"]}
-            }
-        """
         return cls(
             name="image_latents",
             label="Image Latents",
@@ -162,34 +105,16 @@ class MellonParam:
     @classmethod
     def latents_preview(cls) -> "MellonParam":
         """
-        Latents preview output for visualizing latents in the UI.
-
-        Mellon node definition:
-            "latents_preview": {"label": "Latents Preview", "type": "latent", "display": "output"}
+        `Latents Preview` is a special output parameter that is used to preview the latents in the UI.
         """
         return cls(name="latents_preview", label="Latents Preview", type="latent", display="output")
 
     @classmethod
     def embeddings(cls, display: str = "output") -> "MellonParam":
-        """
-        Text embeddings parameter.
-
-        Mellon node definition (display="output"):
-            "embeddings": {"label": "Text Embeddings", "type": "embeddings", "display": "output"}
-
-        Mellon node definition (display="input"):
-            "embeddings": {"label": "Text Embeddings", "type": "embeddings", "display": "input"}
-        """
         return cls(name="embeddings", label="Text Embeddings", type="embeddings", display=display)
 
     @classmethod
     def image_embeds(cls, display: str = "output") -> "MellonParam":
-        """
-        Image embeddings parameter for IP-Adapter workflows.
-
-        Mellon node definition (display="output"):
-            "image_embeds": {"label": "Image Embeddings", "type": "image_embeds", "display": "output"}
-        """
         return cls(
             name="image_embeds",
             label="Image Embeddings",
@@ -200,15 +125,6 @@ class MellonParam:
 
     @classmethod
     def controlnet_conditioning_scale(cls, default: float = 0.5) -> "MellonParam":
-        """
-        ControlNet conditioning scale slider.
-
-        Mellon node definition (default=0.5):
-            "controlnet_conditioning_scale": {
-                "label": "Controlnet Conditioning Scale", "type": "float", "default": 0.5, "min": 0.0, "max": 1.0,
-                "step": 0.01
-            }
-        """
         return cls(
             name="controlnet_conditioning_scale",
             label="Controlnet Conditioning Scale",
@@ -222,15 +138,6 @@ class MellonParam:
 
     @classmethod
     def control_guidance_start(cls, default: float = 0.0) -> "MellonParam":
-        """
-        Control guidance start timestep.
-
-        Mellon node definition (default=0.0):
-            "control_guidance_start": {
-                "label": "Control Guidance Start", "type": "float", "default": 0.0, "min": 0.0, "max": 1.0, "step":
-                0.01
-            }
-        """
         return cls(
             name="control_guidance_start",
             label="Control Guidance Start",
@@ -244,14 +151,6 @@ class MellonParam:
 
     @classmethod
     def control_guidance_end(cls, default: float = 1.0) -> "MellonParam":
-        """
-        Control guidance end timestep.
-
-        Mellon node definition (default=1.0):
-            "control_guidance_end": {
-                "label": "Control Guidance End", "type": "float", "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01
-            }
-        """
         return cls(
             name="control_guidance_end",
             label="Control Guidance End",
@@ -265,12 +164,6 @@ class MellonParam:
 
     @classmethod
     def prompt(cls, default: str = "") -> "MellonParam":
-        """
-        Text prompt input as textarea.
-
-        Mellon node definition (default=""):
-            "prompt": {"label": "Prompt", "type": "string", "default": "", "display": "textarea"}
-        """
         return cls(
             name="prompt",
             label="Prompt",
@@ -282,12 +175,6 @@ class MellonParam:
 
     @classmethod
     def negative_prompt(cls, default: str = "") -> "MellonParam":
-        """
-        Negative prompt input as textarea.
-
-        Mellon node definition (default=""):
-            "negative_prompt": {"label": "Negative Prompt", "type": "string", "default": "", "display": "textarea"}
-        """
         return cls(
             name="negative_prompt",
             label="Negative Prompt",
@@ -299,12 +186,6 @@ class MellonParam:
 
     @classmethod
     def strength(cls, default: float = 0.5) -> "MellonParam":
-        """
-        Denoising strength for img2img.
-
-        Mellon node definition (default=0.5):
-            "strength": {"label": "Strength", "type": "float", "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}
-        """
         return cls(
             name="strength",
             label="Strength",
@@ -318,15 +199,6 @@ class MellonParam:
 
     @classmethod
     def guidance_scale(cls, default: float = 5.0) -> "MellonParam":
-        """
-        CFG guidance scale slider.
-
-        Mellon node definition (default=5.0):
-            "guidance_scale": {
-                "label": "Guidance Scale", "type": "float", "display": "slider", "default": 5.0, "min": 1.0, "max":
-                30.0, "step": 0.1
-            }
-        """
         return cls(
             name="guidance_scale",
             label="Guidance Scale",
@@ -340,12 +212,6 @@ class MellonParam:
 
     @classmethod
     def height(cls, default: int = 1024) -> "MellonParam":
-        """
-        Image height in pixels.
-
-        Mellon node definition (default=1024):
-            "height": {"label": "Height", "type": "int", "default": 1024, "min": 64, "step": 8}
-        """
         return cls(
             name="height",
             label="Height",
@@ -358,26 +224,12 @@ class MellonParam:
 
     @classmethod
     def width(cls, default: int = 1024) -> "MellonParam":
-        """
-        Image width in pixels.
-
-        Mellon node definition (default=1024):
-            "width": {"label": "Width", "type": "int", "default": 1024, "min": 64, "step": 8}
-        """
         return cls(
             name="width", label="Width", type="int", default=default, min=64, step=8, required_block_params=["width"]
         )
 
     @classmethod
     def seed(cls, default: int = 0) -> "MellonParam":
-        """
-        Random seed with randomize button.
-
-        Mellon node definition (default=0):
-            "seed": {
-                "label": "Seed", "type": "int", "default": 0, "min": 0, "max": 4294967295, "display": "random"
-            }
-        """
         return cls(
             name="seed",
             label="Seed",
@@ -391,14 +243,6 @@ class MellonParam:
 
     @classmethod
     def num_inference_steps(cls, default: int = 25) -> "MellonParam":
-        """
-        Number of denoising steps slider.
-
-        Mellon node definition (default=25):
-            "num_inference_steps": {
-                "label": "Steps", "type": "int", "default": 25, "min": 1, "max": 100, "display": "slider"
-            }
-        """
         return cls(
             name="num_inference_steps",
             label="Steps",
@@ -412,12 +256,6 @@ class MellonParam:
 
     @classmethod
     def num_frames(cls, default: int = 81) -> "MellonParam":
-        """
-        Number of video frames slider.
-
-        Mellon node definition (default=81):
-            "num_frames": {"label": "Frames", "type": "int", "default": 81, "min": 1, "max": 480, "display": "slider"}
-        """
         return cls(
             name="num_frames",
             label="Frames",
@@ -431,12 +269,6 @@ class MellonParam:
 
     @classmethod
     def layers(cls, default: int = 4) -> "MellonParam":
-        """
-        Number of layers slider (for layered diffusion).
-
-        Mellon node definition (default=4):
-            "layers": {"label": "Layers", "type": "int", "default": 4, "min": 1, "max": 10, "display": "slider"}
-        """
         return cls(
             name="layers",
             label="Layers",
@@ -450,24 +282,15 @@ class MellonParam:
 
     @classmethod
     def videos(cls) -> "MellonParam":
-        """
-        Video output parameter.
-
-        Mellon node definition:
-            "videos": {"label": "Videos", "type": "video", "display": "output"}
-        """
         return cls(name="videos", label="Videos", type="video", display="output", required_block_params=["videos"])
 
     @classmethod
     def vae(cls) -> "MellonParam":
         """
-        VAE model input.
+        VAE model info dict.
 
-        Mellon node definition:
-            "vae": {"label": "VAE", "type": "diffusers_auto_model", "display": "input"}
-
-        Note: The value received is a model info dict with keys like 'model_id', 'repo_id', 'execution_device'. Use
-        components.get_one(model_id) to retrieve the actual model.
+        Contains keys like 'model_id', 'repo_id', 'execution_device' etc. Use components.get_one(model_id) to retrieve
+        the actual model.
         """
         return cls(
             name="vae", label="VAE", type="diffusers_auto_model", display="input", required_block_params=["vae"]
@@ -476,13 +299,10 @@ class MellonParam:
     @classmethod
     def image_encoder(cls) -> "MellonParam":
         """
-        Image encoder model input.
+        Image Encoder model info dict.
 
-        Mellon node definition:
-            "image_encoder": {"label": "Image Encoder", "type": "diffusers_auto_model", "display": "input"}
-
-        Note: The value received is a model info dict with keys like 'model_id', 'repo_id', 'execution_device'. Use
-        components.get_one(model_id) to retrieve the actual model.
+        Contains keys like 'model_id', 'repo_id', 'execution_device' etc. Use components.get_one(model_id) to retrieve
+        the actual model.
         """
         return cls(
             name="image_encoder",
@@ -495,39 +315,30 @@ class MellonParam:
     @classmethod
     def unet(cls) -> "MellonParam":
         """
-        Denoising model (UNet/Transformer) input.
+        Denoising model (UNet/Transformer) info dict.
 
-        Mellon node definition:
-            "unet": {"label": "Denoise Model", "type": "diffusers_auto_model", "display": "input"}
-
-        Note: The value received is a model info dict with keys like 'model_id', 'repo_id', 'execution_device'. Use
-        components.get_one(model_id) to retrieve the actual model.
+        Contains keys like 'model_id', 'repo_id', 'execution_device' etc. Use components.get_one(model_id) to retrieve
+        the actual model.
         """
         return cls(name="unet", label="Denoise Model", type="diffusers_auto_model", display="input")
 
     @classmethod
     def scheduler(cls) -> "MellonParam":
         """
-        Scheduler model input.
+        Scheduler model info dict.
 
-        Mellon node definition:
-            "scheduler": {"label": "Scheduler", "type": "diffusers_auto_model", "display": "input"}
-
-        Note: The value received is a model info dict with keys like 'model_id', 'repo_id'. Use
-        components.get_one(model_id) to retrieve the actual scheduler.
+        Contains keys like 'model_id', 'repo_id' etc. Use components.get_one(model_id) to retrieve the actual
+        scheduler.
         """
         return cls(name="scheduler", label="Scheduler", type="diffusers_auto_model", display="input")
 
     @classmethod
     def controlnet(cls) -> "MellonParam":
         """
-        ControlNet model input.
+        ControlNet model info dict.
 
-        Mellon node definition:
-            "controlnet": {"label": "ControlNet Model", "type": "diffusers_auto_model", "display": "input"}
-
-        Note: The value received is a model info dict with keys like 'model_id', 'repo_id', 'execution_device'. Use
-        components.get_one(model_id) to retrieve the actual model.
+        Contains keys like 'model_id', 'repo_id', 'execution_device' etc. Use components.get_one(model_id) to retrieve
+        the actual model.
         """
         return cls(
             name="controlnet",
@@ -540,17 +351,12 @@ class MellonParam:
     @classmethod
     def text_encoders(cls) -> "MellonParam":
         """
-        Text encoders dict input (multiple encoders).
+        Dict of text encoder model info dicts.
 
-        Mellon node definition:
-            "text_encoders": {"label": "Text Encoders", "type": "diffusers_auto_models", "display": "input"}
-
-        Note: The value received is a dict of model info dicts:
-            {
-                'text_encoder': {'model_id': ..., 'execution_device': ..., ...}, 'tokenizer': {'model_id': ..., ...},
-                'repo_id': '...'
-            }
-        Use components.get_one(model_id) to retrieve each model.
+        Structure: {
+            'text_encoder': {'model_id': ..., 'execution_device': ..., ...}, 'tokenizer': {'model_id': ..., ...},
+            'repo_id': '...'
+        } Use components.get_one(model_id) to retrieve each model.
         """
         return cls(
             name="text_encoders",
@@ -563,20 +369,15 @@ class MellonParam:
     @classmethod
     def controlnet_bundle(cls, display: str = "input") -> "MellonParam":
         """
-        ControlNet bundle containing model and processed control inputs. Output from ControlNet node, input to Denoise
-        node.
+        ControlNet bundle containing model info and processed control inputs.
 
-        Mellon node definition (display="input"):
-            "controlnet_bundle": {"label": "ControlNet", "type": "custom_controlnet", "display": "input"}
+        Structure: {
+            'controlnet': {'model_id': ..., ...}, # controlnet model info dict 'control_image': ..., # processed
+            control image/embeddings 'controlnet_conditioning_scale': ..., ... # other inputs expected by denoise
+            blocks
+        }
 
-        Mellon node definition (display="output"):
-            "controlnet_bundle": {"label": "ControlNet", "type": "custom_controlnet", "display": "output"}
-
-        Note: The value is a dict containing:
-            {
-                'controlnet': {'model_id': ..., ...}, # controlnet model info 'control_image': ..., # processed control
-                image/embeddings 'controlnet_conditioning_scale': ..., # and other denoise block inputs
-            }
+        Output from Controlnet node, input to Denoise node.
         """
         return cls(
             name="controlnet_bundle",
@@ -588,25 +389,10 @@ class MellonParam:
 
     @classmethod
     def ip_adapter(cls) -> "MellonParam":
-        """
-        IP-Adapter input.
-
-        Mellon node definition:
-            "ip_adapter": {"label": "IP Adapter", "type": "custom_ip_adapter", "display": "input"}
-        """
         return cls(name="ip_adapter", label="IP Adapter", type="custom_ip_adapter", display="input")
 
     @classmethod
     def guider(cls) -> "MellonParam":
-        """
-        Custom guider input. When connected, hides the guidance_scale slider.
-
-        Mellon node definition:
-            "guider": {
-                "label": "Guider", "type": "custom_guider", "display": "input", "onChange": {false: ["guidance_scale"],
-                true: []}
-            }
-        """
         return cls(
             name="guider",
             label="Guider",
@@ -617,12 +403,6 @@ class MellonParam:
 
     @classmethod
     def doc(cls) -> "MellonParam":
-        """
-        Documentation output for inspecting the underlying modular pipeline.
-
-        Mellon node definition:
-            "doc": {"label": "Doc", "type": "string", "display": "output"}
-        """
         return cls(name="doc", label="Doc", type="string", display="output")
 
 
@@ -635,7 +415,6 @@ DEFAULT_NODE_SPECS = {
             MellonParam.height(),
             MellonParam.seed(),
             MellonParam.num_inference_steps(),
-            MellonParam.num_frames(),
             MellonParam.guidance_scale(),
             MellonParam.strength(),
             MellonParam.image_latents_with_strength(),
@@ -890,9 +669,6 @@ class MellonPipelineConfig:
     @property
     def node_params(self) -> Dict[str, Any]:
         """Lazily compute node_params from node_specs."""
-        if self.node_specs is None:
-            return self._node_params
-
         params = {}
         for node_type, spec in self.node_specs.items():
             if spec is None:
@@ -935,8 +711,7 @@ class MellonPipelineConfig:
         Note: The mellon_params are already in Mellon format when loading from JSON.
         """
         instance = cls.__new__(cls)
-        instance.node_specs = None
-        instance._node_params = data.get("node_params", {})
+        instance.node_params = data.get("node_params", {})
         instance.label = data.get("label", "")
         instance.default_repo = data.get("default_repo", "")
         instance.default_dtype = data.get("default_dtype", "")

src/diffusers/pipelines/__init__.py

@@ -155,7 +155,7 @@ else:
         "AudioLDM2UNet2DConditionModel",
     ]
     _import_structure["blip_diffusion"] = ["BlipDiffusionPipeline"]
-    _import_structure["chroma"] = ["ChromaPipeline", "ChromaImg2ImgPipeline", "ChromaInpaintPipeline"]
+    _import_structure["chroma"] = ["ChromaPipeline", "ChromaImg2ImgPipeline"]
     _import_structure["cogvideo"] = [
         "CogVideoXPipeline",
         "CogVideoXImageToVideoPipeline",
@@ -598,7 +598,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .blip_diffusion import BlipDiffusionPipeline
         from .bria import BriaPipeline
         from .bria_fibo import BriaFiboPipeline
-        from .chroma import ChromaImg2ImgPipeline, ChromaInpaintPipeline, ChromaPipeline
+        from .chroma import ChromaImg2ImgPipeline, ChromaPipeline
         from .chronoedit import ChronoEditPipeline
         from .cogvideo import (
             CogVideoXFunControlPipeline,

src/diffusers/pipelines/chroma/__init__.py

@@ -24,7 +24,6 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["pipeline_chroma"] = ["ChromaPipeline"]
     _import_structure["pipeline_chroma_img2img"] = ["ChromaImg2ImgPipeline"]
-    _import_structure["pipeline_chroma_inpainting"] = ["ChromaInpaintPipeline"]
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
         if not (is_transformers_available() and is_torch_available()):
@@ -34,7 +33,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     else:
         from .pipeline_chroma import ChromaPipeline
         from .pipeline_chroma_img2img import ChromaImg2ImgPipeline
-        from .pipeline_chroma_inpainting import ChromaInpaintPipeline
 else:
     import sys
 

src/diffusers/pipelines/longcat_image/pipeline_longcat_image.py

@@ -260,10 +260,10 @@ class LongCatImagePipeline(DiffusionPipeline, FromSingleFileMixin):
             text = self.text_processor.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
             all_text.append(text)
 
-        inputs = self.text_processor(text=all_text, padding=True, return_tensors="pt").to(self.text_encoder.device)
+        inputs = self.text_processor(text=all_text, padding=True, return_tensors="pt").to(device)
 
+        self.text_encoder.to(device)
         generated_ids = self.text_encoder.generate(**inputs, max_new_tokens=self.tokenizer_max_length)
-        generated_ids.to(device)
         generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
         output_text = self.text_processor.batch_decode(
             generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -632,21 +632,6 @@ class ChromaImg2ImgPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
-class ChromaInpaintPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
 class ChromaPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]