Upload 5 files

WanImageProcessor/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

WanImageProcessor/README.md

@@ -0,0 +1,198 @@
+---
+library_name: diffusers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🧨 diffusers pipeline that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

WanImageProcessor/block.py

@@ -0,0 +1,96 @@
+from diffusers.modular_pipelines import (
+    PipelineBlock,
+    InputParam,
+    OutputParam,
+    ConfigSpec,
+)
+
+from diffusers.utils import load_image
+from PIL import Image
+from typing import Union, Tuple
+
+# copied from https://github.com/Wan-Video/Wan2.2/blob/388807310646ed5f318a99f8e8d9ad28c5b65373/wan/utils/utils.py#L136
+def best_output_size(w, h, dw, dh, expected_area):
+    # float output size
+    ratio = w / h
+    ow = (expected_area * ratio)**0.5
+    oh = expected_area / ow
+
+    # process width first
+    ow1 = int(ow // dw * dw)
+    oh1 = int(expected_area / ow1 // dh * dh)
+    assert ow1 % dw == 0 and oh1 % dh == 0 and ow1 * oh1 <= expected_area
+    ratio1 = ow1 / oh1
+
+    # process height first
+    oh2 = int(oh // dh * dh)
+    ow2 = int(expected_area / oh2 // dw * dw)
+    assert oh2 % dh == 0 and ow2 % dw == 0 and ow2 * oh2 <= expected_area
+    ratio2 = ow2 / oh2
+
+    # compare ratios
+    if max(ratio / ratio1, ratio1 / ratio) < max(ratio / ratio2,
+                                                 ratio2 / ratio):
+        return ow1, oh1
+    else:
+        return ow2, oh2
+
+class Wan225BI2VImageProcessor(PipelineBlock):
+
+    @property
+    def description(self):
+        return "default Image Processor for wan2.2 5b i2v, it resizes the image to the best output size and center-crop it"
+
+    @property
+    def inputs(self):
+        return [
+            InputParam(name="image", type_hint=Union[Image.Image, str], description= "the Image to process"),
+            InputParam(name="max_area", type_hint=int, description= "the maximum area of the Image to process")
+        ]
+    
+    @property
+    def intermediate_outputs(self):
+        return [
+            OutputParam(name="processed_image", type_hint=Image.Image, description= "the processed Image"),
+        ]
+    
+    @property
+    def expected_configs(self):
+        return [
+            ConfigSpec(name="patch_size", default=(1, 2, 2)),
+            ConfigSpec(name="vae_stride", default=(4, 16, 16)),
+        ]
+    
+    def __call__(self, components, state):
+
+        block_state = self.get_block_state(state)
+
+        if isinstance(block_state.image, str):
+            image = load_image(block_state.image).convert("RGB")
+        elif isinstance(block_state.image, Image.Image):
+            image = block_state.image
+        else:
+            raise ValueError(f"Invalid image type: {type(block_state.image)}; only support PIL Image or url string")
+        
+        ih, iw = image.height, image.width
+        dh, dw = components.patch_size[1] * components.vae_stride[1], components.patch_size[2] * components.vae_stride[2]
+        ow, oh = best_output_size(iw, ih, dw, dh, block_state.max_area)
+
+        scale = max(ow / iw, oh / ih)
+        resized_image = image.resize((round(iw * scale), round(ih * scale)), Image.LANCZOS)
+
+        # center-crop
+        x1 = (resized_image.width - ow) // 2
+        y1 = (resized_image.height - oh) // 2
+        cropped_image = resized_image.crop((x1, y1, x1 + ow, y1 + oh))
+        assert cropped_image.width == ow and cropped_image.height == oh
+
+        block_state.processed_image = cropped_image
+
+        print(f" initial image size: {image.size}")
+        print(f" processed image size: {cropped_image.size}")
+        
+
+        self.set_block_state(state, block_state)
+        return components, state
+

WanImageProcessor/config.json

@@ -0,0 +1,7 @@
+{
+  "_class_name": "Wan225BI2VImageProcessor",
+  "_diffusers_version": "0.35.0.dev0",
+  "auto_map": {
+    "ModularPipelineBlocks": "block.Wan225BI2VImageProcessor"
+  }
+}

WanImageProcessor/modular_model_index.json

@@ -0,0 +1,15 @@
+{
+  "_blocks_class_name": "Wan225BI2VImageProcessor",
+  "_class_name": "ModularPipeline",
+  "_diffusers_version": "0.35.0.dev0",
+  "patch_size": [
+    1,
+    2,
+    2
+  ],
+  "vae_stride": [
+    4,
+    16,
+    16
+  ]
+}