Delete GenVideo_app.py

GenVideo_app.py

@@ -1,158 +0,0 @@
-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()