# Deploy model endpoint for Stable Diffusion v2.1

In this tutorial, we will create a model endpoint for Stability AI’s **Stable Diffusion v2.1** model.

The tutorial focuses on:

* Step-by-step model endpoint creation and image generation
* Creating a model endpoint with custom model weights
* Supported parameters for image generation

For this tutorial, we’ll use the **Stable Diffusion v2.1** pre-built container. The advantage of using this is that the API handler is automatically created — no need to build one manually.

---

## A guide on model endpoint creation and image generation

### Step 1: Create a model endpoint for Stable Diffusion v2.1

1. Go to the [AI Platform](https://tir.e2enetworks.com).
2. Select a project.
3. Navigate to **Model Endpoints**.
4. Click **Create Endpoint**.
5. Choose the **Stable-Diffusion v2.1** model card under **Choose Framework**.
6. Pick a suitable GPU plan. You can keep default values for replicas, disk size, and endpoint details.
7. Add environment variables if required, otherwise proceed.
8. Skip custom model weights for now; we’ll use the default ones. (See [Creating model endpoint with custom model weights](#creating-model-endpoint-with-custom-model-weights) if needed.)
9. Complete endpoint creation.

---

### Step 2: Generate your API token

The model endpoint API requires an **auth token**.

1. Go to the **API Tokens** section in your project.
2. Click **Create Token** or use an existing one.
3. Copy the generated **Auth Token** — you’ll use it in the next step.


---

### Step 3: Generate images using text prompts

Once your model endpoint is **Ready**, test it using the **Sample API Request** shown on the model details page.

1. Launch a **Instance** with **PyTorch** or **StableDiffusion** image.
2. Open Jupyter Labs → start a new notebook `untitled.ipynb`.
3. Paste the Python **Sample API Request** from the endpoint.
4. Replace `$AUTH_TOKEN` with your actual token.
5. Run the cell to generate results.

The endpoint will return tensors (PyTorch format) representing images. To visualize them:

<details>
<summary>Click to expand code</summary>

```python
import torch
import torchvision.transforms as transforms

def display_images(tensor_image_data_list):
    '''convert PyTorch Tensors to PIL Image'''
    for tensor_data in tensor_image_data_list:
        tensor_image = torch.tensor(tensor_data.get("data"))
        pil_img = transforms.ToPILImage()(tensor_image)
        pil_img.show()
        # Uncomment to save images
        # pil_img.save(tensor_data.get("name"))

if response.status_code == 200:
    display_images(response.json().get("predictions"))
```

</details>

Your Stable Diffusion endpoint is now up and running!

You can modify text prompts or parameters to explore different image generations. See [Supported parameters for image generation](#supported-parameters-for-image-generation) for more details.

---

## Creating model endpoint with custom model weights

To use custom model weights for Stable Diffusion v2.1:

1. Download the [Stable-Diffusion-2-1 model](https://huggingface.co/stabilityai/stable-diffusion-2-1).
2. Upload it to your **Model Bucket (EOS)**.
3. Create an endpoint pointing to the uploaded weights.

### Step 1.1: Define a model in the dashboard

1. Go to the [AI Platform](https://tir.e2enetworks.com).
2. Choose your project.
3. Navigate to **Models** → **Create Model**.
4. Name it (e.g., `stable-diffusion`).
5. Select **Model Type: Custom**.
6. Click **CREATE**.
7. Note the details of the created EOS bucket.
8. Copy the **Setup Host** command from **Setup MinIO CLI**.

---

### Step 1.2: Start a new Instance

1. Go to **Notebooks** and launch one using **Diffusers Image**.
2. Choose a GPU plan (recommended).
3. In Jupyter Labs, open **Terminal**.
4. Run the copied **MinIO CLI Host setup** command.
5. Confirm `mc` CLI is ready.

---

### Step 1.3: Download Stable Diffusion v2.1 model

<details>
<summary>Click to expand code</summary>

```python
import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler

model_id = "stabilityai/stable-diffusion-2-1"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
```

</details>

```bash
pip install diffusers transformers accelerate scipy safetensors
```

Run a quick test:

```python
prompt = "a photograph of an astronaut riding a horse"
image = pipe(prompt).images[0]
image.show()
```

---

## Step 2: Upload the model to EOS

<details>
<summary>Click to expand code</summary>

```bash
cd $HOME/.cache/huggingface/hub/models--stabilityai--stable-diffusion-2-1/snapshots
mc cp -r * stable-diffusion/stable-diffusion-854588
```

</details>

```bash
ls $HOME/.cache/huggingface/hub
```

---

## Step 3: Create an endpoint for your model

Return to the dashboard → **Model Endpoints** → **Create Endpoint**.
Select the model from your EOS bucket. If your model isn’t in the root directory, provide the path containing `model_index.json`.

## Supported parameters for image generation

Each request can include additional parameters to control image output.

<details>
<summary>**prompt** (str or list) — Text input guiding image generation.</summary>

```json
{
  "prompt": [
    "a photo of an astronaut riding a horse",
    "a photo of a cat playing football"
  ]
}
```

</details>

<details>
<summary>**height / width** (int, defaults to 768) — Image dimensions in pixels.</summary>

```json
{
  "prompt": "a photo of an astronaut riding a horse",
  "height": 512,
  "width": 1024
}
```

</details>

<details>
<summary>**num_images_per_prompt** (int, defaults to 1) — Number of images to generate per prompt.</summary>

```json
{
  "prompt": ["horse", "cat"],
  "num_images_per_prompt": 2
}
```

</details>

<details>
<summary>**generator** (list[int]) — Random seed for deterministic output.</summary>

```json
{
  "prompt": ["horse", "cat"],
  "generator": [1024, 700]
}
```

</details>

<details>
<summary>**num_inference_steps** (int, defaults to 50) — Denoising steps for quality control.</summary>

```json
{
  "prompt": "horse",
  "generator": [2000],
  "num_inference_steps": 80
}
```

</details>

<details>
<summary>**negative_prompt** (str or list) — Prompts specifying what not to include in the image.</summary>

```json
{
  "prompt": "a horse riding an astronaut",
  "negative_prompt": "brown colored horse"
}
```

</details>

<details>
<summary>**guidance_scale** (float, defaults to 7.5) — Adjusts adherence to the prompt.</summary>

```json
{
  "prompt": "a photo of an astronaut riding a horse",
  "guidance_scale": 11
}
```

</details>

<details>
<summary>**guidance_rescale** (float, defaults to 0.7) — Adjusts overexposure correction.</summary>

```json
{
  "prompt": "horse",
  "generator": [2000],
  "guidance_rescale": 0.3
}
```

</details>

---

## Troubleshooting and best practices

* Verify MinIO credentials before upload.
* Ensure sufficient GPU resources before deployment.
* Use smaller prompts or lower inference steps for testing.
* If API calls fail, check logs under endpoint details.
* Keep `guidance_scale` between 6–9 for balanced image quality.


---