Function run_img2img

scripts/demo/sampling.py:162–207

(
    state,
    version_dict,
    is_legacy=False,
    return_latents=False,
    filter=None,
    stage2strength=None,
)

def run_img2img(
    state,
    version_dict,
    is_legacy=False,
    return_latents=False,
    filter=None,
    stage2strength=None,
):
    img = load_img()
    if img is None:
        return None
    H, W = img.shape[2], img.shape[3]

    init_dict = {
        "orig_width": W,
        "orig_height": H,
        "target_width": W,
        "target_height": H,
    }
    value_dict = init_embedder_options(
        get_unique_embedder_keys_from_conditioner(state["model"].conditioner),
        init_dict,
        prompt=prompt,
        negative_prompt=negative_prompt,
    )
    strength = st.number_input(
        "**Img2Img Strength**", value=0.75, min_value=0.0, max_value=1.0
    )
    sampler, num_rows, num_cols = init_sampling(
        img2img_strength=strength,
        stage2strength=stage2strength,
    )
    num_samples = num_rows * num_cols

    if st.button("Sample"):
        out = do_img2img(
            repeat(img, "1 ... -> n ...", n=num_samples),
            state["model"],
            sampler,
            value_dict,
            num_samples,
            force_uc_zero_embeddings=["txt"] if not is_legacy else [],
            return_latents=return_latents,
            filter=filter,
        )
        return out


def apply_refiner(