Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+  - image-generation
+  - diffusion
+  - imagenet
+  - flow-matching
+  - self-supervised
+datasets:
+  - imagenet-1k
+pipeline_tag: image-to-image
+library_name: pytorch
+---
+
+# Self-Flow ImageNet 256×256
+
+**Self-Flow** is a self-supervised training method for diffusion transformers that combines flow matching with a self-supervised feature reconstruction objective. This checkpoint is trained on ImageNet 256×256.
+
+### Key Features
+
+- **Architecture**: SiT-XL/2 with per-token timestep conditioning
+- **Training**: Flow matching + self-supervised feature reconstruction
+- **Resolution**: 256×256 pixels
+- **Parameters**: ~675M
+
+## Evaluation Results
+
+| Metric | Value |
+|--------|-------|
+| FID ↓ | 5.7 |
+| IS ↑ | 151.40  |
+| sFID ↓ | 4.97 |
+| Precision | 0.72 |
+| Recall | 0.67 |
+
+*Results computed on 50,000 generated samples vs ImageNet validation set.*
+
+## Usage
+
+
+### Download Checkpoint
+
+```python
+from huggingface_hub import hf_hub_download
+
+checkpoint_path = hf_hub_download(
+    repo_id="Hila/selfflow-imagenet256",
+    filename="selfflow_imagenet256.pt"
+)
+```
+and follow the instructions in our repository: https://github.com/black-forest-labs/Self-Flow
+
+## License
+
+This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
+