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inputs.py

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+# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# 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.
+
+from diffusers.modular_pipelines import ModularPipelineBlocks, InputParam, OutputParam, ModularPipeline, PipelineState
+import numpy as np
+import torch
+import PIL
+from typing import List
+from diffusers.modular_pipelines.wan.before_denoise import WanInputStep
+
+
+def calculate_dimensions(image, mod_value):
+    """
+    Calculate output dimensions based on resolution settings.
+
+    Args:
+        image: PIL Image
+        mod_value: Modulo value for dimension alignment
+
+    Returns:
+        Tuple of (width, height)
+    """
+
+    # Get max area from preset or override
+    target_area = 720 * 1280
+
+    # Calculate dimensions maintaining aspect ratio
+    aspect_ratio = image.height / image.width
+    calculated_height = round(np.sqrt(target_area * aspect_ratio)) // mod_value * mod_value
+    calculated_width = round(np.sqrt(target_area / aspect_ratio)) // mod_value * mod_value
+
+    return calculated_width, calculated_height
+
+
+# Make the input step aware of `negative_prompt_embeds`.
+# ChronoEdit uses a `guidance_scale` of 1.
+class ChronoEditInputStep(WanInputStep):
+    model_name = "chronoedit"
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [
+            InputParam("num_videos_per_prompt", default=1),
+            InputParam(
+                "prompt_embeds",
+                required=True,
+                type_hint=torch.Tensor,
+                description="Pre-generated text embeddings. Can be generated from text_encoder step.",
+            ),
+            InputParam(
+                "negative_prompt_embeds",
+                type_hint=torch.Tensor,
+                description="Pre-generated negative text embeddings. Can be generated from text_encoder step.",
+            ),
+        ]
+
+
+class ChronoEditImageInputStep(ModularPipelineBlocks):
+    model_name = "chronoedit"
+
+    @property
+    def inputs(self) -> List[InputParam]:
+        return [InputParam(name="image")]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam(name="image", type_hint=PIL.Image.Image),
+            OutputParam(name="height", type_hint=int, description="The height set w.r.t input image and specs"),
+            OutputParam(name="width", type_hint=int, description="The width set w.r.t input image and specs"),
+        ]
+
+    def __call__(self, components: ModularPipeline, state: PipelineState) -> PipelineState:
+        block_state = self.get_block_state(state)
+        image = block_state.image
+        mod_value = components.vae_scale_factor_spatial * components.transformer.config.patch_size[1]
+        
+        width, height = calculate_dimensions(image, mod_value)
+        block_state.image = image.resize((width, height))
+        block_state.height = height 
+        block_state.width = width
+        
+        self.set_block_state(state, block_state)
+        return components, state