Gertie01 / studio-nw6xjfbq: Report

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Image generation failed on model stabilityai/stable-diffusion-xl-base-1.0: ‘InferenceClient’ object has no attribute ‘image_generation’

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Simply put, you’re trying to use a feature that doesn’t currently exist and encountering an error.

Your Space fails because the code calls a method that doesn’t exist on huggingface_hub.InferenceClient. The class exposes task-specific helpers like text_to_image(...) for generation. There is no image_generation(...). So Python raises AttributeError. Replace the call and keep arguments in the supported shape. (Hugging Face)

What’s happening, in plain terms

  • You’re using the wrong API surface. InferenceClient provides one method per task. For images it’s text_to_image. Older tutorials or third-party wrappers sometimes mention client.post(...) or custom helpers; those aren’t on today’s client and trigger similar errors. (Hugging Face)
  • Router confusion is common. The Hugging Face OpenAI-compatible router is for chat completion only. It does not expose an OpenAI-style image API. Use InferenceClient.text_to_image or a provider SDK for images. (Hugging Face)
  • Model availability varies by provider. Some providers may not serve stabilityai/stable-diffusion-xl-base-1.0 directly. The Inference Providers docs show recommended, provider-backed choices like FLUX or SDXL-Lightning. (Hugging Face)
  • If you run SDXL yourself, use Diffusers. The SDXL model card shows working Diffusers code and prerequisites. (Hugging Face)

Minimal, beginner-safe fix

Replace the nonexistent image_generation(...) with the supported helper. Keep parameters as flat kwargs.

# pip install -U "huggingface_hub>=1.1.2" pillow  # docs: https://huggingface.co/docs/inference-providers/en/tasks/text-to-image
import os
from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",                 # or "fal-ai", "replicate", "together"
    api_key=os.environ["HF_TOKEN"],          # HF token with Inference Providers permission
)

# Returns a PIL.Image
image = client.text_to_image(                # ← correct method
    "a neon kitsune in a rainy Tokyo alley", # prompt
    model="stabilityai/stable-diffusion-xl-base-1.0",
    width=1024,
    height=1024,
    negative_prompt="blurry, low quality",
    num_inference_steps=30,
    guidance_scale=7.5,
)
image.save("out.png")
# ref and example: https://huggingface.co/docs/inference-providers/en/tasks/text-to-image

Why this works: text_to_image is the official image generation entrypoint on InferenceClient. The Inference Providers docs show this method and a working Python snippet. (Hugging Face)

Likely causes in your Space’s code, and exact remedies

  1. Wrong call name
  • Cause: client.image_generation(...).
  • Fix: client.text_to_image(...). Keep generation options as keyword args (e.g., width=, height=, num_inference_steps=). (Hugging Face)
  1. Legacy helper like client.post(...)
  • Cause: Old blog posts or wrappers still call .post.
  • Fix: Stop using .post. Call the task helper (text_to_image) or call the HTTP API yourself if you must. The community has multiple “no attribute .post” reports after client updates. (GitHub)
  1. Router misuse
  • Cause: Trying to create images through router.huggingface.co with an OpenAI-style Images API.
  • Fix: Use InferenceClient.text_to_image or a provider SDK. The OpenAI-compatible router covers chat completion only. (Hugging Face)
  1. Provider doesn’t serve your model
  • Cause: You pass stabilityai/stable-diffusion-xl-base-1.0 to a provider that doesn’t host it.
  • Fix: Either switch provider or pick a provider-backed model from the Text-to-Image page (e.g., FLUX.1, SDXL-Lightning). (Hugging Face)
  1. Running SDXL inside the Space
  • Cause: You want to avoid Providers and run the model yourself.
  • Fix: Use Diffusers as shown on the model card; requires a GPU and the SDXL license terms. (Hugging Face)

Gradio / Spaces patterns that don’t break

Provider call inside a Space

# pip install -U gradio "huggingface_hub>=1.1.2" pillow
import gradio as gr
from huggingface_hub import InferenceClient
import os

client = InferenceClient(provider="hf-inference", api_key=os.environ.get("HF_TOKEN"))

def generate(prompt, w, h, steps, guidance, neg):
    return client.text_to_image(
        prompt, model="stabilityai/stable-diffusion-xl-base-1.0",
        width=w, height=h, num_inference_steps=steps, guidance_scale=guidance,
        negative_prompt=neg
    )

demo = gr.Interface(
    fn=generate,
    inputs=[gr.Text(label="Prompt"), gr.Slider(512, 1344, 1024, step=64, label="Width"),
            gr.Slider(512, 1344, 1024, step=64, label="Height"),
            gr.Slider(5, 50, 30, step=1, label="Steps"),
            gr.Slider(1.0, 12.0, 7.5, step=0.5, label="Guidance"),
            gr.Text(label="Negative prompt", value="blurry, low quality")],
    outputs=gr.Image(type="pil"),
)
demo.launch()
# method shape: https://huggingface.co/docs/inference-providers/en/tasks/text-to-image

ZeroGPU on Spaces
If you rely on ZeroGPU, keep generation inside the function that runs under a short GPU slot. Use the decorator and avoid long warmups. (Hugging Face)

If you prefer to self-host SDXL in the Space

# pip install -U "diffusers>=0.30.0" transformers accelerate torch pillow
# model card diffusers snippet: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
import torch
from diffusers import StableDiffusionXLPipeline

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    use_safetensors=True,
).to("cuda")

def generate_local(prompt, w=1024, h=1024, steps=30, guidance=7.5, neg="blurry, low quality"):
    out = pipe(prompt=prompt, height=h, width=w,
               num_inference_steps=steps, guidance_scale=guidance,
               negative_prompt=neg)
    return out.images[0]
# full usage and refiner flow shown on the model card

The SDXL card documents base-only and base+refiner pipelines, install hints, and optimization tips. (Hugging Face)

Diagnostic checklist (run top-to-bottom)

  1. Confirm method availability
from huggingface_hub import InferenceClient, __version__
print(__version__)                 # expect ≥ 1.1.x
print(hasattr(InferenceClient(), "text_to_image"))  # True means your client exposes the helper

Use the helper if present; don’t invent image_generation. Docs show text_to_image. (Hugging Face)

  1. Use a provider-backed model first
    Try FLUX or SDXL-Lightning to confirm routing works. Then point back to SDXL base if your provider supports it. (Hugging Face)

  2. Passing options
    With InferenceClient, pass generation settings as keyword args, not nested under a parameters dict. The HTTP spec uses parameters, but the Python helper takes kwargs; the official snippet demonstrates the helper call. (Hugging Face)

  3. Do not send image calls to the OpenAI-compatible router
    The router is chat-only; images won’t work there. (Hugging Face)

  4. If you self-host
    Follow the SDXL model card Diffusers recipe and ensure a GPU. (Hugging Face)

Common symptoms → concrete fixes

Symptom Root cause Fix
AttributeError: 'InferenceClient' object has no attribute 'image_generation' No such method on the client. Call text_to_image(...). (Hugging Face)
AttributeError: 'InferenceClient' object has no attribute 'post' Legacy wrapper examples. Method removed/never existed. Use task helpers. Don’t call .post. (GitHub)
Calls to router.huggingface.co for images fail Router exposes chat completion only. Use InferenceClient.text_to_image or a provider SDK. (Hugging Face)
Provider returns “model not supported” Provider doesn’t host that ID. Choose a recommended model or change provider. (Hugging Face)
Diffusers errors in Space Missing GPU or packages. Follow the SDXL card diffusers section and GPU notes. (Hugging Face)

Short, curated extras

Official, stable

  • Text-to-Image with Inference Providers. Shows InferenceClient.text_to_image Python usage and what arguments are supported. Good for verifying method names. (Hugging Face)
  • SDXL model card (Diffusers recipes). Covers base vs refiner, install, and performance tips. Useful if you self-host. (Hugging Face)
  • ZeroGPU docs. If your Space uses on-demand GPU allocation. (Hugging Face)

Community signals on breaking calls

  • Missing .post on InferenceClient. Confirms legacy examples cause AttributeError. Useful sanity check if you still see attribute errors after renaming the method. (GitHub)
5

Minimal patch?