README.md

---

title: 🧩 DiffuseCraft Mod (SDXL/SD1.5 Models Text-to-Image)
emoji: 🧩🖼️📦
colorFrom: red
colorTo: pink
sdk: gradio
sdk_version: 5.45.0
app_file: app.py
pinned: true
header: mini
license: mit
duplicated_from: r3gm/DiffuseCraft
short_description: Stunning images using stable diffusion.
preload_from_hub:
  - madebyollin/sdxl-vae-fp16-fix config.json,diffusion_pytorch_model.safetensors
hf_oauth: true
---


## Using this Space programmatically

You can call this Space from Python (via `gradio_client`) or from plain `curl`.

> ⚠️ Note: This README may lag behind the actual API definition shown in the Space’s “View API” page.
> If something does not work, always double-check the latest argument list and endpoint names there.

Assumptions:

- Space ID: `John6666/DiffuseCraftMod`
- You have a valid Hugging Face access token: `hf_xxx...` (read access is enough)
- Replace `hf_xxx...` with your own token

---

### 1. Python examples (`gradio_client`)



Install:



```bash

pip install gradio_client
````



#### 1.1 Synchronous API – `generate_image`



```python

from gradio_client import Client



client = Client("John6666/DiffuseCraftMod", hf_token="hf_xxx...")



status, images, info = client.predict(

    # Core text controls

    prompt="Hello!!",

    negative_prompt=(

        "lowres, bad anatomy, bad hands, missing fingers, extra digit, "

        "fewer digits, worst quality, low quality"

    ),



    # Basic generation controls

    num_images=1,

    num_inference_steps=28,

    guidance_scale=7.0,

    clip_skip=0,

    seed=-1,



    # Canvas / model / task (optional, server has defaults)

    height=1024,

    width=1024,

    model_name="votepurchase/animagine-xl-3.1",

    vae_model="None",

    task="txt2img",



    # All other arguments are optional; defaults match the UI

    api_name="/generate_image",

)



print(status)   # e.g. "COMPLETE"

print(images)   # list of image paths / URLs

print(info)     # generation metadata (seed, model, etc.)

```

#### 1.2 Streaming API – `generate_image_stream`

```python

from gradio_client import Client



client = Client("John6666/DiffuseCraftMod", hf_token="hf_xxx...")



job = client.submit(

    prompt="Hello!!",

    negative_prompt=(

        "lowres, bad anatomy, bad hands, missing fingers, extra digit, "

        "fewer digits, worst quality, low quality"

    ),

    num_images=1,

    num_inference_steps=28,

    guidance_scale=7.0,

    clip_skip=0,

    seed=-1,

    height=1024,

    width=1024,

    model_name="votepurchase/animagine-xl-3.1",

    vae_model="None",

    task="txt2img",

    api_name="/generate_image_stream",

)



for status, images, info in job:

    # You will see progress messages, intermediate previews, and the final result.

    print(status, images, info)

```

You can stop iterating once you see a `"COMPLETE"` status if you only care about the final output.

---

### 2. `curl` examples

When calling from `curl`, include your HF token; anonymous calls may be rate-limited or rejected.

```bash

export HF_TOKEN="hf_xxx..."  # your Hugging Face access token

```

The `data` field is a positional array. The order must match the function signature.
For simplicity, the examples below only send the first few arguments and rely on server defaults for the rest.

#### 2.1 Synchronous API – `generate_image`



```bash

curl -X POST "https://john6666-diffusecraftmod.hf.space/call/generate_image" \
  -H "Authorization: Bearer $HF_TOKEN" \

  -H "Content-Type: application/json" \

  -d '{

    "data": [

      "Hello!!",  // prompt

      "lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, worst quality, low quality", // negative_prompt
      1,          // num_images

      28,         // num_inference_steps

      7.0,        // guidance_scale

      0,          // clip_skip

      -1          // seed

      // All subsequent parameters will use their default values

    ]

  }'

```


#### 2.2 Streaming API – `generate_image_stream`

```bash

curl -X POST "https://john6666-diffusecraftmod.hf.space/call/generate_image_stream" \

  -H "Authorization: Bearer $HF_TOKEN" \

  -H "Content-Type: application/json" \

  -d '{

    "data": [

      "Hello!!",

      "lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, worst quality, low quality",

      1,

      28,

      7.0,

      0,

      -1

    ]

  }'

```

For full parameter coverage (all advanced options such as LoRAs, ControlNet, IP-Adapter, etc.),
refer to the Space’s “View API” page and adapt the examples above accordingly.