Improve model card: add pipeline tag, library name, and sample usage
#1
by
nielsr
HF Staff
- opened
README.md
CHANGED
|
@@ -1,8 +1,11 @@
|
|
| 1 |
---
|
| 2 |
-
license: apache-2.0
|
| 3 |
base_model:
|
| 4 |
- stable-diffusion-v1-5/stable-diffusion-v1-5
|
|
|
|
|
|
|
|
|
|
| 5 |
---
|
|
|
|
| 6 |
<meta name="google-site-verification" content="-XQC-POJtlDPD3i2KSOxbFkSBde_Uq9obAIh_4mxTkM" />
|
| 7 |
|
| 8 |
|
|
@@ -60,6 +63,36 @@ The proposed framework is architected around two core components: SAA and IMR. (
|
|
| 60 |
|
| 61 |
2. Download our SAA-related and IMR-related checkpoints from [DynamicID Checkpoints on Hugging Face](https://huggingface.co/meteorite2023/DynamicID).
|
| 62 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 63 |
|
| 64 |
## 🌈 Gallery
|
| 65 |
|
|
|
|
| 1 |
---
|
|
|
|
| 2 |
base_model:
|
| 3 |
- stable-diffusion-v1-5/stable-diffusion-v1-5
|
| 4 |
+
license: apache-2.0
|
| 5 |
+
pipeline_tag: text-to-image
|
| 6 |
+
library_name: diffusers
|
| 7 |
---
|
| 8 |
+
|
| 9 |
<meta name="google-site-verification" content="-XQC-POJtlDPD3i2KSOxbFkSBde_Uq9obAIh_4mxTkM" />
|
| 10 |
|
| 11 |
|
|
|
|
| 63 |
|
| 64 |
2. Download our SAA-related and IMR-related checkpoints from [DynamicID Checkpoints on Hugging Face](https://huggingface.co/meteorite2023/DynamicID).
|
| 65 |
|
| 66 |
+
## ⚡ Sample Usage (Diffusers)
|
| 67 |
+
|
| 68 |
+
The official inference code is available in the [GitHub repository](https://github.com/ByteCat-bot/DynamicID), which provides detailed instructions for running the model. A typical usage with the `diffusers` library would involve loading the base Stable Diffusion pipeline and then integrating the DynamicID specific weights.
|
| 69 |
+
|
| 70 |
+
```python
|
| 71 |
+
import torch
|
| 72 |
+
from diffusers import StableDiffusionPipeline
|
| 73 |
+
|
| 74 |
+
# Load the base Stable Diffusion pipeline
|
| 75 |
+
# Ensure you have downloaded the base model locally or from Hugging Face Hub
|
| 76 |
+
pipeline = StableDiffusionPipeline.from_pretrained(
|
| 77 |
+
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
|
| 78 |
+
).to("cuda")
|
| 79 |
+
|
| 80 |
+
# Load DynamicID specific weights (e.g., LoRAs or custom UNet modifications)
|
| 81 |
+
# The precise method for loading these weights will be detailed in the official repository.
|
| 82 |
+
# For conceptual understanding, it might involve:
|
| 83 |
+
# pipeline.load_lora_weights("path/to/DynamicID/weights")
|
| 84 |
+
# Or integrating custom UNet/attention layers as per the DynamicID implementation.
|
| 85 |
+
|
| 86 |
+
# Refer to the official GitHub repository for the exact loading and inference pipeline.
|
| 87 |
+
# You would then pass your text prompt and identity reference images to the pipeline.
|
| 88 |
+
# Example (conceptual):
|
| 89 |
+
# prompt = "a photo of [person1] with a big smile and [person2] looking thoughtful"
|
| 90 |
+
# generated_image = pipeline(
|
| 91 |
+
# prompt=prompt,
|
| 92 |
+
# identity_references=[id_image_1, id_image_2], # Placeholder for identity images
|
| 93 |
+
# # Add other parameters as specified in the DynamicID code
|
| 94 |
+
# ).images[0]
|
| 95 |
+
```
|
| 96 |
|
| 97 |
## 🌈 Gallery
|
| 98 |
|