u pup

src/diffusers/modular_pipelines/flux/encoders.py

@@ -302,7 +302,7 @@ class FluxTextEncoderStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("prompt"),
+            InputParam("prompt", required=True),
             InputParam("prompt_2"),
             InputParam("max_sequence_length", type_hint=int, default=512, required=False),
             InputParam("joint_attention_kwargs"),

src/diffusers/modular_pipelines/flux2/encoders.py

@@ -80,7 +80,7 @@ class Flux2TextEncoderStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("prompt"),
+            InputParam("prompt", required=True),
             InputParam("max_sequence_length", type_hint=int, default=512, required=False),
             InputParam("text_encoder_out_layers", type_hint=Tuple[int], default=(10, 20, 30), required=False),
         ]
@@ -99,7 +99,7 @@ class Flux2TextEncoderStep(ModularPipelineBlocks):
     @staticmethod
     def check_inputs(block_state):
         prompt = block_state.prompt
-        if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
             raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
 
     @staticmethod
@@ -193,7 +193,7 @@ class Flux2RemoteTextEncoderStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("prompt"),
+            InputParam("prompt", required=True),
         ]
 
     @property
@@ -210,7 +210,7 @@ class Flux2RemoteTextEncoderStep(ModularPipelineBlocks):
     @staticmethod
     def check_inputs(block_state):
         prompt = block_state.prompt
-        if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
             raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(block_state.prompt)}")
 
     @torch.no_grad()
@@ -270,7 +270,7 @@ class Flux2KleinTextEncoderStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("prompt"),
+            InputParam("prompt", required=True),
             InputParam("max_sequence_length", type_hint=int, default=512, required=False),
             InputParam("text_encoder_out_layers", type_hint=Tuple[int], default=(9, 18, 27), required=False),
         ]
@@ -290,7 +290,7 @@ class Flux2KleinTextEncoderStep(ModularPipelineBlocks):
     def check_inputs(block_state):
         prompt = block_state.prompt
 
-        if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
             raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
 
     @staticmethod
@@ -405,7 +405,7 @@ class Flux2KleinBaseTextEncoderStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("prompt"),
+            InputParam("prompt", required=True),
             InputParam("max_sequence_length", type_hint=int, default=512, required=False),
             InputParam("text_encoder_out_layers", type_hint=Tuple[int], default=(9, 18, 27), required=False),
         ]
@@ -431,7 +431,7 @@ class Flux2KleinBaseTextEncoderStep(ModularPipelineBlocks):
     def check_inputs(block_state):
         prompt = block_state.prompt
 
-        if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
             raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
 
     @staticmethod

src/diffusers/modular_pipelines/qwenimage/modular_blocks_qwenimage.py

@@ -56,52 +56,7 @@ logger = logging.get_logger(__name__)
 
 
 # ====================
-# 1. TEXT ENCODER
-# ====================
-
-
-# auto_docstring
-class QwenImageAutoTextEncoderStep(AutoPipelineBlocks):
-    """
-    Text encoder step that encodes the text prompt into a text embedding. This is an auto pipeline block.
-
-      Components:
-          text_encoder (`Qwen2_5_VLForConditionalGeneration`): The text encoder to use tokenizer (`Qwen2Tokenizer`):
-          The tokenizer to use guider (`ClassifierFreeGuidance`)
-
-      Inputs:
-          prompt (`str`, *optional*):
-              The prompt or prompts to guide image generation.
-          negative_prompt (`str`, *optional*):
-              The prompt or prompts not to guide the image generation.
-          max_sequence_length (`int`, *optional*, defaults to 1024):
-              Maximum sequence length for prompt encoding.
-
-      Outputs:
-          prompt_embeds (`Tensor`):
-              The prompt embeddings.
-          prompt_embeds_mask (`Tensor`):
-              The encoder attention mask.
-          negative_prompt_embeds (`Tensor`):
-              The negative prompt embeddings.
-          negative_prompt_embeds_mask (`Tensor`):
-              The negative prompt embeddings mask.
-    """
-
-    model_name = "qwenimage"
-    block_classes = [QwenImageTextEncoderStep()]
-    block_names = ["text_encoder"]
-    block_trigger_inputs = ["prompt"]
-
-    @property
-    def description(self) -> str:
-        return "Text encoder step that encodes the text prompt into a text embedding. This is an auto pipeline block."
-        " - `QwenImageTextEncoderStep` (text_encoder) is used when `prompt` is provided."
-        " - if `prompt` is not provided, step will be skipped."
-
-
-# ====================
-# 2. VAE ENCODER
+# 1. VAE ENCODER
 # ====================
 
 
@@ -249,7 +204,7 @@ class QwenImageOptionalControlNetVaeEncoderStep(AutoPipelineBlocks):
 
 
 # ====================
-# 3. DENOISE (input -> prepare_latents -> set_timesteps -> prepare_rope_inputs -> denoise -> after_denoise)
+# 2. DENOISE (input -> prepare_latents -> set_timesteps -> prepare_rope_inputs -> denoise -> after_denoise)
 # ====================
 
 
@@ -1011,7 +966,7 @@ class QwenImageAutoCoreDenoiseStep(ConditionalPipelineBlocks):
 
 
 # ====================
-# 4. DECODE
+# 3. DECODE
 # ====================
 
 
@@ -1096,11 +1051,11 @@ class QwenImageAutoDecodeStep(AutoPipelineBlocks):
 
 
 # ====================
-# 5. AUTO BLOCKS & PRESETS
+# 4. AUTO BLOCKS & PRESETS
 # ====================
 AUTO_BLOCKS = InsertableDict(
     [
-        ("text_encoder", QwenImageAutoTextEncoderStep()),
+        ("text_encoder", QwenImageTextEncoderStep()),
         ("vae_encoder", QwenImageAutoVaeEncoderStep()),
         ("controlnet_vae_encoder", QwenImageOptionalControlNetVaeEncoderStep()),
         ("denoise", QwenImageAutoCoreDenoiseStep()),

src/diffusers/modular_pipelines/stable_diffusion_xl/encoders.py

@@ -244,7 +244,7 @@ class StableDiffusionXLTextEncoderStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("prompt"),
+            InputParam("prompt", required=True),
             InputParam("prompt_2"),
             InputParam("negative_prompt"),
             InputParam("negative_prompt_2"),

src/diffusers/modular_pipelines/wan/encoders.py

@@ -179,7 +179,7 @@ class WanTextEncoderStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("prompt"),
+            InputParam("prompt", required=True),
             InputParam("negative_prompt"),
             InputParam("max_sequence_length", default=512),
         ]

src/diffusers/modular_pipelines/z_image/encoders.py

@@ -149,7 +149,7 @@ class ZImageTextEncoderStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("prompt"),
+            InputParam("prompt", required=True),
             InputParam("negative_prompt"),
             InputParam("max_sequence_length", default=512),
         ]

tests/modular_pipelines/flux/test_modular_pipeline_flux.py

@@ -37,6 +37,7 @@ class TestFluxModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = FluxModularPipeline
     pipeline_blocks_class = FluxAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-modular"
+    default_repo_id = "hf-internal-testing/tiny-flux-pipe"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale"])
     batch_params = frozenset(["prompt"])
@@ -63,6 +64,7 @@ class TestFluxImg2ImgModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = FluxModularPipeline
     pipeline_blocks_class = FluxAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-modular"
+    default_repo_id = "hf-internal-testing/tiny-flux-pipe"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale", "image"])
     batch_params = frozenset(["prompt", "image"])
@@ -129,6 +131,7 @@ class TestFluxKontextModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = FluxKontextModularPipeline
     pipeline_blocks_class = FluxKontextAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-kontext-pipe"
+    default_repo_id = "hf-internal-testing/tiny-flux-kontext-pipe"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale", "image"])
     batch_params = frozenset(["prompt", "image"])

tests/modular_pipelines/flux2/test_modular_pipeline_flux2.py

@@ -32,6 +32,8 @@ class TestFlux2ModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = Flux2ModularPipeline
     pipeline_blocks_class = Flux2AutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-modular"
+    default_repo_id = "black-forest-labs/FLUX.2-dev"
+    default_repo_id = "hf-internal-testing/tiny-flux2"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale"])
     batch_params = frozenset(["prompt"])
@@ -60,6 +62,7 @@ class TestFlux2ImageConditionedModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = Flux2ModularPipeline
     pipeline_blocks_class = Flux2AutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-modular"
+    default_repo_id = "hf-internal-testing/tiny-flux2"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale", "image"])
     batch_params = frozenset(["prompt", "image"])

tests/modular_pipelines/flux2/test_modular_pipeline_flux2_klein.py

@@ -32,6 +32,7 @@ class TestFlux2ModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = Flux2KleinModularPipeline
     pipeline_blocks_class = Flux2KleinAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-modular"
+    default_repo_id = None  # TODO
 
     params = frozenset(["prompt", "height", "width"])
     batch_params = frozenset(["prompt"])
@@ -59,6 +60,7 @@ class TestFlux2ImageConditionedModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = Flux2KleinModularPipeline
     pipeline_blocks_class = Flux2KleinAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-modular"
+    default_repo_id = None  # TODO
 
     params = frozenset(["prompt", "height", "width", "image"])
     batch_params = frozenset(["prompt", "image"])

tests/modular_pipelines/flux2/test_modular_pipeline_flux2_klein_base.py

@@ -32,7 +32,7 @@ class TestFlux2ModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = Flux2KleinModularPipeline
     pipeline_blocks_class = Flux2KleinBaseAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-base-modular"
-
+    default_repo_id = "hf-internal-testing/tiny-flux2-klein"
     params = frozenset(["prompt", "height", "width"])
     batch_params = frozenset(["prompt"])
 
@@ -59,6 +59,7 @@ class TestFlux2ImageConditionedModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = Flux2KleinModularPipeline
     pipeline_blocks_class = Flux2KleinBaseAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-base-modular"
+    default_repo_id = "hf-internal-testing/tiny-flux2-klein"
 
     params = frozenset(["prompt", "height", "width", "image"])
     batch_params = frozenset(["prompt", "image"])

tests/modular_pipelines/qwen/test_modular_pipeline_qwenimage.py

@@ -34,6 +34,7 @@ class TestQwenImageModularPipelineFast(ModularPipelineTesterMixin, ModularGuider
     pipeline_class = QwenImageModularPipeline
     pipeline_blocks_class = QwenImageAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-qwenimage-modular"
+    default_repo_id = "Qwen/Qwen-Image"
 
     params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image", "mask_image"])
     batch_params = frozenset(["prompt", "negative_prompt", "image", "mask_image"])
@@ -60,6 +61,7 @@ class TestQwenImageEditModularPipelineFast(ModularPipelineTesterMixin, ModularGu
     pipeline_class = QwenImageEditModularPipeline
     pipeline_blocks_class = QwenImageEditAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-qwenimage-edit-modular"
+    default_repo_id = "Qwen/Qwen-Image-Edit"
 
     params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image", "mask_image"])
     batch_params = frozenset(["prompt", "negative_prompt", "image", "mask_image"])
@@ -86,6 +88,7 @@ class TestQwenImageEditPlusModularPipelineFast(ModularPipelineTesterMixin, Modul
     pipeline_class = QwenImageEditPlusModularPipeline
     pipeline_blocks_class = QwenImageEditPlusAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-qwenimage-edit-plus-modular"
+    default_repo_id = "Qwen/Qwen-Image-Edit-2509"
 
     # No `mask_image` yet.
     params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image"])

tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py

@@ -279,6 +279,8 @@ class TestSDXLModularPipelineFast(
     pipeline_class = StableDiffusionXLModularPipeline
     pipeline_blocks_class = StableDiffusionXLAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-sdxl-modular"
+    default_repo_id = "hf-internal-testing/tiny-sdxl-pipe"
+
     params = frozenset(
         [
             "prompt",
@@ -326,6 +328,7 @@ class TestSDXLImg2ImgModularPipelineFast(
     pipeline_class = StableDiffusionXLModularPipeline
     pipeline_blocks_class = StableDiffusionXLAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-sdxl-modular"
+    default_repo_id = "hf-internal-testing/tiny-sdxl-pipe"
     params = frozenset(
         [
             "prompt",
@@ -379,6 +382,7 @@ class SDXLInpaintingModularPipelineFastTests(
     pipeline_class = StableDiffusionXLModularPipeline
     pipeline_blocks_class = StableDiffusionXLAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-sdxl-modular"
+    default_repo_id = "hf-internal-testing/tiny-sdxl-pipe"
     params = frozenset(
         [
             "prompt",

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -37,6 +37,8 @@ class ModularPipelineTesterMixin:
     optional_params = frozenset(["num_inference_steps", "num_images_per_prompt", "latents", "output_type"])
     # this is modular specific: generator needs to be a intermediate input because it's mutable
     intermediate_params = frozenset(["generator"])
+    # prompt is required for most pipeline, with exceptions like qwen-image layer
+    required_params = frozenset(["prompt"])
 
     def get_generator(self, seed=0):
         generator = torch.Generator("cpu").manual_seed(seed)
@@ -55,6 +57,12 @@ class ModularPipelineTesterMixin:
             "You need to set the attribute `pretrained_model_name_or_path` in the child test class. See existing pipeline tests for reference."
         )
 
+    @property
+    def default_repo_id(self) -> str:
+        raise NotImplementedError(
+            "You need to set the attribute `default_repo_id` in the child test class. See existing pipeline tests for reference."
+        )
+
     @property
     def pipeline_blocks_class(self) -> Union[Callable, ModularPipelineBlocks]:
         raise NotImplementedError(
@@ -121,6 +129,7 @@ class ModularPipelineTesterMixin:
         pipe = self.get_pipeline()
         input_parameters = pipe.blocks.input_names
         optional_parameters = pipe.default_call_parameters
+        required_parameters = pipe.blocks.required_inputs
 
         def _check_for_parameters(parameters, expected_parameters, param_type):
             remaining_parameters = {param for param in parameters if param not in expected_parameters}
@@ -130,6 +139,98 @@ class ModularPipelineTesterMixin:
 
         _check_for_parameters(self.params, input_parameters, "input")
         _check_for_parameters(self.optional_params, optional_parameters, "optional")
+        _check_for_parameters(self.required_params, required_parameters, "required")
+
+    def test_loading_from_default_repo(self):
+        if self.default_repo_id is None:
+            return
+
+        try:
+            pipe = ModularPipeline.from_pretrained(self.default_repo_id)
+            assert pipe.blocks.__class__ == self.pipeline_blocks_class
+        except Exception as e:
+            assert False, f"Failed to load pipeline from default repo: {e}"
+
+    def test_modular_inference(self):
+        # run the pipeline to get the base output for comparison
+        pipe = self.get_pipeline()
+        pipe.to(torch_device, torch.float32)
+
+        inputs = self.get_dummy_inputs()
+        standard_output = pipe(**inputs, output="images")
+
+        # create text, denoise, decoder (and optional vae encoder) nodes
+        blocks = self.pipeline_blocks_class()
+
+        assert "text_encoder" in blocks.sub_blocks, "`text_encoder` block is not present in the pipeline"
+        assert "denoise" in blocks.sub_blocks, "`denoise` block is not present in the pipeline"
+        assert "decode" in blocks.sub_blocks, "`decode` block is not present in the pipeline"
+
+        # manually set the components in the sub_pipe
+        # a hack to workaround the fact the default pipeline properties are often incorrect for testing cases,
+        # #e.g. vae_scale_factor is ususally not 8 because vae is configured to be smaller for testing
+        def manually_set_all_components(pipe: ModularPipeline, sub_pipe: ModularPipeline):
+            for n, comp in pipe.components.items():
+                setattr(sub_pipe, n, comp)
+
+        # Initialize all nodes
+        text_node = blocks.sub_blocks["text_encoder"].init_pipeline(self.pretrained_model_name_or_path)
+        text_node.load_components(torch_dtype=torch.float32)
+        text_node.to(torch_device)
+        manually_set_all_components(pipe, text_node)
+
+        denoise_node = blocks.sub_blocks["denoise"].init_pipeline(self.pretrained_model_name_or_path)
+        denoise_node.load_components(torch_dtype=torch.float32)
+        denoise_node.to(torch_device)
+        manually_set_all_components(pipe, denoise_node)
+
+        decoder_node = blocks.sub_blocks["decode"].init_pipeline(self.pretrained_model_name_or_path)
+        decoder_node.load_components(torch_dtype=torch.float32)
+        decoder_node.to(torch_device)
+        manually_set_all_components(pipe, decoder_node)
+
+        if "vae_encoder" in blocks.sub_blocks:
+            vae_encoder_node = blocks.sub_blocks["vae_encoder"].init_pipeline(self.pretrained_model_name_or_path)
+            vae_encoder_node.load_components(torch_dtype=torch.float32)
+            vae_encoder_node.to(torch_device)
+            manually_set_all_components(pipe, vae_encoder_node)
+        else:
+            vae_encoder_node = None
+
+        def filter_inputs(available: dict, expected_keys) -> dict:
+            return {k: v for k, v in available.items() if k in expected_keys}
+
+        # prepare inputs for each node
+        inputs = self.get_dummy_inputs()
+
+        # 1. Text encoder: takes from inputs
+        text_inputs = filter_inputs(inputs, text_node.blocks.input_names)
+        text_output = text_node(**text_inputs)
+        text_output_dict = text_output.get_by_kwargs("denoiser_input_fields")
+
+        # 2. VAE encoder (optional): takes from inputs + text_output
+        if vae_encoder_node is not None:
+            vae_available = {**inputs, **text_output_dict}
+            vae_encoder_inputs = filter_inputs(vae_available, vae_encoder_node.blocks.input_names)
+            vae_encoder_output = vae_encoder_node(**vae_encoder_inputs)
+            vae_output_dict = vae_encoder_output.values
+        else:
+            vae_output_dict = {}
+
+        # 3. Denoise: takes from inputs + text_output + vae_output
+        denoise_available = {**inputs, **text_output_dict, **vae_output_dict}
+        denoise_inputs = filter_inputs(denoise_available, denoise_node.blocks.input_names)
+        denoise_output = denoise_node(**denoise_inputs)
+        latents = denoise_output.latents
+
+        # 4. Decoder: takes from inputs + denoise_output
+        decode_available = {**inputs, "latents": latents}
+        decode_inputs = filter_inputs(decode_available, decoder_node.blocks.input_names)
+        modular_output = decoder_node(**decode_inputs).images
+
+        assert modular_output.shape == standard_output.shape, (
+            f"Modular output should have same shape as standard output {standard_output.shape}, but got {modular_output.shape}"
+        )
 
     def test_inference_batch_consistent(self, batch_sizes=[2], batch_generator=True):
         pipe = self.get_pipeline().to(torch_device)