Method __call__

inference/ace_plus_diffusers.py:69–117

(self,
                 reference_image=None,
                 edit_image=None,
                 edit_mask=None,
                 prompt='',
                 task=None,
                 output_height=1024,
                 output_width=1024,
                 sampler='flow_euler',
                 sample_steps=28,
                 guide_scale=50,
                 lora_path=None,
                 seed=-1,
                 tar_index=0,
                 align=0,
                 repainting_scale=0,
                 **kwargs)

    @torch.no_grad()
    def __call__(self,
                 reference_image=None,
                 edit_image=None,
                 edit_mask=None,
                 prompt='',
                 task=None,
                 output_height=1024,
                 output_width=1024,
                 sampler='flow_euler',
                 sample_steps=28,
                 guide_scale=50,
                 lora_path=None,
                 seed=-1,
                 tar_index=0,
                 align=0,
                 repainting_scale=0,
                 **kwargs):
        if isinstance(prompt, str):
            prompt = [prompt]
        seed = seed if seed >= 0 else random.randint(0, 2 ** 32 - 1)
        # edit_image, edit_mask, change_image, content_image, out_h, out_w, slice_w
        image, mask, _, _, out_h, out_w, slice_w = self.image_processor.preprocess(reference_image, edit_image, edit_mask,
                                                                             width = output_width,
                                                                             height = output_height,
                                                                             repainting_scale = repainting_scale)
        h, w = image.shape[1:]
        generator = torch.Generator("cpu").manual_seed(seed)
        masked_image_latents = self.prepare_input(image, mask,
                                               batch_size=len(prompt) , height=h, width=w, generator = generator)

        if lora_path is not None:
            with FS.get_from(lora_path) as local_path:
                self.pipe.load_lora_weights(local_path)



        image = self.pipe(
            prompt=prompt,
            masked_image_latents=masked_image_latents,
            height=h,
            width=w,
            guidance_scale=guide_scale,
            num_inference_steps=sample_steps,
            max_sequence_length=512,
            generator=generator
        ).images[0]
        if lora_path is not None:
            self.pipe.unload_lora_weights()
        return self.image_processor.postprocess(image, slice_w, out_w, out_h), seed


if __name__ == '__main__':