| from diffusers import DiffusionPipeline, LCMScheduler | |
| import torch | |
| loaded_pipe = None | |
| loaded_pipe_id = None | |
| def load_model(pipe_id): | |
| global loaded_pipe, loaded_pipe_id | |
| if loaded_pipe_id != pipe_id: | |
| loaded_pipe = DiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to("cuda") | |
| loaded_pipe.scheduler = LCMScheduler.from_config(loaded_pipe.scheduler.config) | |
| loaded_pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl") | |
| loaded_pipe_id = pipe_id | |
| return loaded_pipe | |
| def generate_image(prompt, num_inference_steps, seed, guidance_scale, negative_prompt=None, pipe_id="Linaqruf/animagine-xl"): | |
| global loaded_pipe | |
| pipe = load_model(pipe_id) | |
| generator = torch.manual_seed(seed) | |
| image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, generator=generator, guidance_scale=guidance_scale).images[0] | |
| return image | |