| import gradio as gr | |
| from diffusers import DiffusionPipeline | |
| import torch | |
| def get_device(): | |
| if torch.cuda.is_available(): | |
| return "cuda" | |
| else: | |
| return "cpu" | |
| def generate_image(prompt): | |
| pipe_id = "SG161222/Realistic_Vision_V6.0_B1_noVAE" | |
| pipe = DiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to("cuda") | |
| pipe.load_lora_weights("timdpaep/t1m") | |
| prompt = "professional photo, closeup photo of t1mLora, wearing black sweater, nature, gloomy, cloudy weather, bokeh <lora:t1m01:1>" | |
| lora_scale= 0.9 | |
| image = pipe( | |
| prompt, num_inference_steps=10, cross_attention_kwargs={"scale": lora_scale}, generator=torch.manual_seed(0) | |
| ).to(get_device()).images[0] | |
| return image | |
| iface = gr.Interface(fn=generate_image, inputs="textbox", outputs="image") | |
| iface.launch() |