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chronoedit-modular

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modular_diffusers

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 ---
 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 model that has been pushed on the Hub. This model card has been automatically generated.
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-### Model Sources [optional]
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-## Uses
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-### Direct Use
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-### Recommendations
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-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.
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-## Training Details
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-#### Training Hyperparameters
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-## Evaluation
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-## 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).
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-## Technical Specifications [optional]
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 ---
 library_name: diffusers
+tags:
+- modular_diffusers
 ---
+# Modular ChronoEdit
+Modular implementation of [`nvidia/ChronoEdit-14B-Diffusers`](https://hf.co/nvidia/ChronoEdit-14B-Diffusers).
+## Code
+<details>
+<summary>Unfold</summary>
+```py
+"""
+Mimicked from https://huggingface.co/spaces/nvidia/ChronoEdit/blob/main/app.py
+"""
+from diffusers.modular_pipelines import WanModularPipeline, ModularPipelineBlocks
+from diffusers.utils import load_image
+from diffusers import UniPCMultistepScheduler
+import torch
+from PIL import Image
+repo_id = "diffusers-internal-dev/chronoedit-modular"
+blocks = ModularPipelineBlocks.from_pretrained(repo_id, trust_remote_code=True)
+pipe = WanModularPipeline(blocks, repo_id)
+pipe.load_components(
+    trust_remote_code=True,
+    device_map="cuda",
+    torch_dtype={"default": torch.bfloat16, "image_encoder": torch.float32},
+)
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=2.0)
+pipe.load_lora_weights("nvidia/ChronoEdit-14B-Diffusers", weight_name="lora/chronoedit_distill_lora.safetensors")
+pipe.fuse_lora(lora_scale=1.0)
+image = load_image("https://huggingface.co/spaces/nvidia/ChronoEdit/resolve/main/examples/3.png")
+prompt = "Transform the image so that inside the floral teacup of steaming tea, a small, cute mouse is sitting and taking a bath; the mouse should look relaxed and cheerful, with a tiny white bath towel draped over its head as if enjoying a spa moment, while the steam rises gently around it, blending seamlessly with the warm and cozy atmosphere."
+# image is resized within the pipeline unlike https://huggingface.co/spaces/nvidia/ChronoEdit/blob/main/app.py#L151
+# refer to `ChronoEditImageInputStep`.
+out = pipe(
+    image=image,
+    prompt=prompt,  # todo: enhance prompt
+    num_inference_steps=8,  # todo: implement temporal reasoning
+    num_frames=5,  # https://huggingface.co/spaces/nvidia/ChronoEdit/blob/main/app.py#L152
+    output_type="np",
+    generator=torch.manual_seed(0),
+)
+frames = out.values["videos"][0]
+Image.fromarray((frames[-1] * 255).clip(0, 255).astype("uint8")).save("demo.png")
+```
+</details>
+You can find it [here](./example.py) too.
+> [!TIP]
+> Make sure `diffusers` is installed from source: `pip install git+https://github.com/huggingface/diffusers`.
+## Results
+<table>
+  <tr>
+    <td><img src="https://huggingface.co/spaces/nvidia/ChronoEdit/resolve/main/examples/3.png" alt="First Image"></td>
+    <td><img src="./demo.png" alt="Edited Image"></td>
+  </tr>
+  <caption><i>Transform the image so that inside the floral teacup of steaming tea, a small, cute mouse is sitting and taking a bath; the mouse should look relaxed and cheerful, with a tiny white bath towel draped over its head as if enjoying a spa moment, while the steam rises gently around it, blending seamlessly with the warm and cozy atmosphere</i>.</caption>
+</table>
+## Notes
+1. This implementation doesn't have temporal reasoning.
+2. This doesn't use a separate prompt enhancer model.