Spaces:
Running
Running
| import gradio as gr | |
| import torch | |
| from diffusers import LCMScheduler, AutoPipelineForText2Image | |
| # model_id = "Lykon/dreamshaper-7" | |
| # prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k" | |
| def run(checkpoint, prompt): | |
| model_id = checkpoint | |
| adapter_id = "latent-consistency/lcm-lora-sdv1-5" | |
| pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float32, safety_checker=None).to("cpu") | |
| pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) | |
| # load and fuse lcm lora | |
| pipe.load_lora_weights(adapter_id) | |
| pipe.fuse_lora() | |
| # disable guidance_scale by passing 0 | |
| image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0, width=512, height=512).images[0] | |
| return image | |
| with gr.Blocks() as demo: | |
| input_checkpoint = gr.Text(value="Lykon/dreamshaper-8", label="Checkpoint") | |
| input_prompt = gr.Text(value="Self-portrait oil painting, a beautiful cyborg with golden hair, 8k", label="Prompt") | |
| out = gr.Image(type="pil") | |
| btn = gr.Button("Run") | |
| btn.click(fn=run, inputs=[input_checkpoint, input_prompt], outputs=out) | |
| demo.launch() |