from huggingface_hub import InferenceClient import torch device="cuda" if torch.cuda.is_available() else "cpu" def T2I(prompt, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28): # Initialize the model client model = InferenceClient(model="black-forest-labs/FLUX.1-dev").to(device) # Prepare the request parameters payload = { "prompt": prompt, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps } # Remove None values to avoid sending unsupported arguments payload = {k: v for k, v in payload.items() if v is not None} # Make the request to generate an image return model.text_to_image(**payload)