--- 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.