Method image_to_image

sgm/inference/api.py:215–246

(
        self,
        params: SamplingParams,
        image,
        prompt: str,
        negative_prompt: str = "",
        samples: int = 1,
        return_latents: bool = False,
    )

        )

    def image_to_image(
        self,
        params: SamplingParams,
        image,
        prompt: str,
        negative_prompt: str = "",
        samples: int = 1,
        return_latents: bool = False,
    ):
        sampler = get_sampler_config(params)

        if params.img2img_strength < 1.0:
            sampler.discretization = Img2ImgDiscretizationWrapper(
                sampler.discretization,
                strength=params.img2img_strength,
            )
        height, width = image.shape[2], image.shape[3]
        value_dict = asdict(params)
        value_dict["prompt"] = prompt
        value_dict["negative_prompt"] = negative_prompt
        value_dict["target_width"] = width
        value_dict["target_height"] = height
        return do_img2img(
            image,
            self.model,
            sampler,
            value_dict,
            samples,
            force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
            return_latents=return_latents,
            filter=None,
        )

    def refiner(
        self,