external/MV-Adapter/scripts/inference_t2mv_sd.py

import argparse

import torch
from diffusers import AutoencoderKL, DDPMScheduler, LCMScheduler, UNet2DConditionModel

from mvadapter.pipelines.pipeline_mvadapter_t2mv_sd import MVAdapterT2MVSDPipeline
from mvadapter.schedulers.scheduling_shift_snr import ShiftSNRScheduler
from mvadapter.utils.mesh_utils import get_orthogonal_camera
from mvadapter.utils.geometry import get_plucker_embeds_from_cameras_ortho
from mvadapter.utils import make_image_grid


def prepare_pipeline(
    base_model,
    vae_model,
    unet_model,
    lora_model,
    adapter_path,
    scheduler,
    num_views,
    device,
    dtype,
):
    # Load vae and unet if provided
    pipe_kwargs = {}
    if vae_model is not None:
        pipe_kwargs["vae"] = AutoencoderKL.from_pretrained(vae_model)
    if unet_model is not None:
        pipe_kwargs["unet"] = UNet2DConditionModel.from_pretrained(unet_model)

    # Prepare pipeline
    pipe: MVAdapterT2MVSDPipeline
    pipe = MVAdapterT2MVSDPipeline.from_pretrained(base_model, **pipe_kwargs)

    # Load scheduler if provided
    scheduler_class = None
    if scheduler == "ddpm":
        scheduler_class = DDPMScheduler
    elif scheduler == "lcm":
        scheduler_class = LCMScheduler

    pipe.scheduler = ShiftSNRScheduler.from_scheduler(
        pipe.scheduler,
        shift_mode="interpolated",
        shift_scale=8.0,
        scheduler_class=scheduler_class,
    )
    pipe.init_custom_adapter(num_views=num_views)
    pipe.load_custom_adapter(
        adapter_path, weight_name="mvadapter_t2mv_sd21.safetensors"
    )

    pipe.to(device=device, dtype=dtype)
    pipe.cond_encoder.to(device=device, dtype=dtype)

    # load lora if provided
    if lora_model is not None:
        model_, name_ = lora_model.rsplit("/", 1)
        pipe.load_lora_weights(model_, weight_name=name_)

    # vae slicing for lower memory usage
    pipe.enable_vae_slicing()

    return pipe


def run_pipeline(
    pipe,
    num_views,
    text,
    height,
    width,
    num_inference_steps,
    guidance_scale,
    seed,
    negative_prompt,
    lora_scale=1.0,
    device="cuda",
):
    # Prepare cameras
    cameras = get_orthogonal_camera(
        elevation_deg=[0, 0, 0, 0, 0, 0],
        distance=[1.8] * num_views,
        left=-0.55,
        right=0.55,
        bottom=-0.55,
        top=0.55,
        azimuth_deg=[x - 90 for x in [0, 45, 90, 180, 270, 315]],
        device=device,
    )

    plucker_embeds = get_plucker_embeds_from_cameras_ortho(
        cameras.c2w, [1.1] * num_views, width
    )
    control_images = ((plucker_embeds + 1.0) / 2.0).clamp(0, 1)

    pipe_kwargs = {}
    if seed != -1:
        pipe_kwargs["generator"] = torch.Generator(device=device).manual_seed(seed)

    images = pipe(
        text,
        height=height,
        width=width,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        num_images_per_prompt=num_views,
        control_image=control_images,
        control_conditioning_scale=1.0,
        negative_prompt=negative_prompt,
        cross_attention_kwargs={"scale": lora_scale},
        **pipe_kwargs,
    ).images

    return images


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Models
    parser.add_argument(
        "--base_model", type=str, default="stabilityai/stable-diffusion-2-1-base"
    )
    parser.add_argument("--vae_model", type=str, default=None)
    parser.add_argument("--unet_model", type=str, default=None)
    parser.add_argument("--scheduler", type=str, default=None)
    parser.add_argument("--lora_model", type=str, default=None)
    parser.add_argument("--adapter_path", type=str, default="huanngzh/mv-adapter")
    parser.add_argument("--num_views", type=int, default=6)
    # Device
    parser.add_argument("--device", type=str, default="cuda")
    # Inference
    parser.add_argument("--text", type=str, required=True)
    parser.add_argument("--num_inference_steps", type=int, default=50)
    parser.add_argument("--guidance_scale", type=float, default=7.0)
    parser.add_argument("--seed", type=int, default=-1)
    parser.add_argument(
        "--negative_prompt",
        type=str,
        default="watermark, ugly, deformed, noisy, blurry, low contrast",
    )
    parser.add_argument("--lora_scale", type=float, default=1.0)
    parser.add_argument("--output", type=str, default="output.png")
    args = parser.parse_args()

    pipe = prepare_pipeline(
        base_model=args.base_model,
        vae_model=args.vae_model,
        unet_model=args.unet_model,
        lora_model=args.lora_model,
        adapter_path=args.adapter_path,
        scheduler=args.scheduler,
        num_views=args.num_views,
        device=args.device,
        dtype=torch.float16,
    )
    images = run_pipeline(
        pipe,
        num_views=args.num_views,
        text=args.text,
        height=512,
        width=512,
        num_inference_steps=args.num_inference_steps,
        guidance_scale=args.guidance_scale,
        seed=args.seed,
        negative_prompt=args.negative_prompt,
        lora_scale=args.lora_scale,
        device=args.device,
    )
    make_image_grid(images, rows=1).save(args.output)