| import gradio as gr | |
| from PIL import Image | |
| import diffusers | |
| import torch | |
| from diffusers import StableDiffusionPipeline | |
| from huggingface_hub import login | |
| login(token="insert your token here") | |
| # Load the model | |
| model_id = "insert your model path like CoWork/fullers-sdv2-1-768-object-fullersamberale001-v1" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") | |
| def inference(prompt, num_samples): | |
| all_images = [] | |
| prompt_update = 'insert your trained concept name like fullersamberale001' + prompt | |
| images = pipe(prompt_update, num_images_per_prompt=num_samples, num_inference_steps=50, guidance_scale=7.5).images | |
| all_images.extend(images) | |
| return all_images | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=inference, | |
| inputs=["textbox", "slider"], | |
| outputs="gallery", | |
| ) | |
| iface.launch() |