model/models/imagenhub_models.py

import imagen_hub

class ImagenHubModel():
    def __init__(self, model_name):
        self.model = imagen_hub.load(model_name)
    
    def __call__(self, *args, **kwargs):
        return self.model.infer_one_image(*args, **kwargs)
    
class PNP(ImagenHubModel):
    def __init__(self):
        super().__init__('PNP')
    
    def __call__(self, *args, **kwargs):
        if "num_inversion_steps" not in kwargs:
            kwargs["num_inversion_steps"] = 200
        return super().__call__(*args, **kwargs)

class Prompt2prompt(ImagenHubModel):
    def __init__(self):
        super().__init__('Prompt2prompt')
    
    def __call__(self, *args, **kwargs):
        if "num_inner_steps" not in kwargs:
            kwargs["num_inner_steps"] = 3
        return super().__call__(*args, **kwargs)
    
def load_imagenhub_model(model_name, model_type=None):
    if model_name == 'PNP':
        return PNP()
    if model_name == 'Prompt2prompt':
        return Prompt2prompt()
    return ImagenHubModel(model_name)


# for name in ['DeepFloydIF', 'PixArtAlpha', 'Kandinsky']: #, 'OpenJourney', 'LCM', 'SD' 'SDXL'
#     # 
#     pipe = ImagenHubModel(name)
#     result = pipe(prompt='a cute dog is playing a ball')
#     print(result)

# for name in ['SD']:
#     from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
#     import torch
#     pipe = DiffusionPipeline.from_pretrained(
#             "stabilityai/stable-diffusion-2-base",
#             torch_dtype=torch.float16,
#             safety_checker=None,
#         ).to("cuda")
#     pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)