GenVideo_app.py

import os

import gradio as gr
from gradio_imageslider import ImageSlider
import argparse
from SUPIR.util import HWC3, upscale_image, fix_resize, convert_dtype
import numpy as np
import torch
from SUPIR.util import create_SUPIR_model, load_QF_ckpt
from PIL import Image
from llava.llava_agent import LLavaAgent
from CKPT_PTH import LLAVA_MODEL_PATH
import einops
import copy
import time
import spaces
from huggingface_hub import hf_hub_download

from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
from diffusers.utils import export_to_gif
from diffusers.utils import export_to_video
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import uuid

hf_hub_download(repo_id="laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", filename="open_clip_pytorch_model.bin", local_dir="laion_CLIP-ViT-bigG-14-laion2B-39B-b160k")
hf_hub_download(repo_id="camenduru/SUPIR", filename="sd_xl_base_1.0_0.9vae.safetensors", local_dir="yushan777_SUPIR")
hf_hub_download(repo_id="camenduru/SUPIR", filename="SUPIR-v0F.ckpt", local_dir="yushan777_SUPIR")
hf_hub_download(repo_id="camenduru/SUPIR", filename="SUPIR-v0Q.ckpt", local_dir="yushan777_SUPIR")
hf_hub_download(repo_id="RunDiffusion/Juggernaut-XL-Lightning", filename="Juggernaut_RunDiffusionPhoto2_Lightning_4Steps.safetensors", local_dir="RunDiffusion_Juggernaut-XL-Lightning")

parser = argparse.ArgumentParser()
parser.add_argument("--opt", type=str, default='options/SUPIR_v0.yaml')
parser.add_argument("--ip", type=str, default='127.0.0.1')
parser.add_argument("--port", type=int, default='6688')
parser.add_argument("--no_llava", action='store_true', default=False)
parser.add_argument("--use_image_slider", action='store_true', default=False)
parser.add_argument("--log_history", action='store_true', default=False)
parser.add_argument("--loading_half_params", action='store_true', default=False)
parser.add_argument("--use_tile_vae", action='store_true', default=False)
parser.add_argument("--encoder_tile_size", type=int, default=512)
parser.add_argument("--decoder_tile_size", type=int, default=64)
parser.add_argument("--load_8bit_llava", action='store_true', default=False)
args = parser.parse_args()
server_ip = args.ip
server_port = args.port
use_llava = not args.no_llava

if torch.cuda.device_count() > 0:
    if torch.cuda.device_count() >= 2:
        SUPIR_device = 'cuda:0'
        LLaVA_device = 'cuda:1'
    elif torch.cuda.device_count() == 1:
        SUPIR_device = 'cuda:0'
        LLaVA_device = 'cuda:0'
    else:
        SUPIR_device = 'cpu'
        LLaVA_device = 'cpu'

    # load SUPIR
    model, default_setting = create_SUPIR_model(args.opt, SUPIR_sign='Q', load_default_setting=True)
    if args.loading_half_params:
        model = model.half()
    if args.use_tile_vae:
        model.init_tile_vae(encoder_tile_size=args.encoder_tile_size, decoder_tile_size=args.decoder_tile_size)
    model = model.to(SUPIR_device)
    model.first_stage_model.denoise_encoder_s1 = copy.deepcopy(model.first_stage_model.denoise_encoder)
    model.current_model = 'v0-Q'
    ckpt_Q, ckpt_F = load_QF_ckpt(args.opt)

    # load LLaVA
    if use_llava:
        llava_agent = LLavaAgent(LLAVA_MODEL_PATH, device=LLaVA_device, load_8bit=args.load_8bit_llava, load_4bit=False)
    else:
        llava_agent = None


# Available adapters (replace with your actual adapter names)
adapter_options = {
    "zoom-out":"guoyww/animatediff-motion-lora-zoom-out",
    "zoom-in":"guoyww/animatediff-motion-lora-zoom-in",
    "pan-left":"guoyww/animatediff-motion-lora-pan-left",
    "pan-right":"guoyww/animatediff-motion-lora-pan-right",
    "roll-clockwise":"guoyww/animatediff-motion-lora-rolling-clockwise",
    "roll-anticlockwise":"guoyww/animatediff-motion-lora-rolling-anticlockwise",
    "tilt-up":"guoyww/animatediff-motion-lora-tilt-up",
    "tilt-down":"guoyww/animatediff-motion-lora-tilt-down"
}

def load_cached_examples():
    examples = [
        ["a cat playing with a ball of yarn", "blurry", 7.5, 12, ["zoom-in"]],
        ["a dog running in a field", "dark, indoors", 8.0, 8, ["pan-left", "tilt-up"]],
    ]
    return examples

device = "cuda"
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
model_id = "SG161222/Realistic_Vision_V5.1_noVAE"

pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16).to(device)
scheduler = DDIMScheduler.from_pretrained(
    model_id,
    subfolder="scheduler",
    clip_sample=False,
    timestep_spacing="linspace",
    beta_schedule="linear",
    steps_offset=1,
)
pipe.scheduler = scheduler

@spaces.GPU
def generate_video(prompt,negative_prompt, guidance_scale, num_inference_steps, adapter_choices):

    pipe.to(device)

    # Set adapters based on user selection
    if adapter_choices:
        for i in range(len(adapter_choices)):
            adapter_name = adapter_choices[i]
            pipe.load_lora_weights(
                adapter_options[adapter_name], adapter_name=adapter_name,
            )
        pipe.set_adapters(adapter_choices, adapter_weights=[1.0] * len(adapter_choices))
        print(adapter_choices)

    output = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_frames=16,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
    )
    name = str(uuid.uuid4()).replace("-", "")
    path = f"/tmp/{name}.mp4"
    export_to_video(output.frames[0], path, fps=10)
    return path



iface = gr.Interface(
    theme=gr.themes.Soft(primary_hue="cyan", secondary_hue="teal"),
    fn=generate_video,
    inputs=[
        gr.Textbox(label="Prompt"),
        gr.Textbox(label="Negative Prompt"),
        gr.Slider(minimum=0.5, maximum=10, value=7.5, label="Guidance Scale"),
        gr.Slider(minimum=4, maximum=24, step=4, value=4, label="Inference Steps"),
        gr.CheckboxGroup(adapter_options.keys(), label="Adapter Choice",type='value'),
    ],
    outputs=gr.Video(label="Generated Video"),
    examples = [
        ["Urban ambiance, man walking, neon lights, rain, wet floor, high quality", "bad quality", 7.5, 24, []],
        ["Nature, farms, mountains in background, drone shot, high quality","bad quality" ,8.0, 24, []],
    ],
    cache_examples=True
)

iface.launch()