File size: 5,120 Bytes
464d284
 
 
 
 
 
 
 
 
 
 
 
3a757b0
 
 
 
 
 
 
 
 
 
 
 
464d284
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f261a00
464d284
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
77b7165
 
464d284
 
77b7165
 
464d284
 
a9e7aa9
464d284
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
import os
import gc
import time
import random
import imageio
import torch
import gradio as gr
from diffusers.utils import load_image

from skyreels_v2_infer.modules import download_model
from skyreels_v2_infer.pipelines import Image2VideoPipeline, Text2VideoPipeline, PromptEnhancer, resizecrop

MODEL_ID_CONFIG = {
    "text2video": [
        "Skywork/SkyReels-V2-T2V-14B-540P",
        "Skywork/SkyReels-V2-T2V-14B-720P",
    ],
    "image2video": [
        "Skywork/SkyReels-V2-I2V-1.3B-540P",
        "Skywork/SkyReels-V2-I2V-14B-540P",
        "Skywork/SkyReels-V2-I2V-14B-720P",
    ],
}

def generate_video(
    prompt,
    model_id,
    resolution,
    num_frames,
    image=None,
    guidance_scale=6.0,
    shift=8.0,
    inference_steps=30,
    use_usp=False,
    offload=False,
    fps=24,
    seed=None,
    prompt_enhancer=False,
    teacache=False,
    teacache_thresh=0.2,
    use_ret_steps=False
):
    model_id = download_model(model_id)
    
    if resolution == "540P":
        height, width = 544, 960
    elif resolution == "720P":
        height, width = 720, 1280
    else:
        raise ValueError(f"Invalid resolution: {resolution}")

    if seed is None:
        random.seed(time.time())
        seed = int(random.randrange(4294967294))
    
    if image is not None:
        image = load_image(image).convert("RGB")
        image_width, image_height = image.size
        if image_height > image_width:
            height, width = width, height
        image = resizecrop(image, height, width)

    negative_prompt = (
        "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, "
        "overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, "
        "poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
        "three legs, many people in the background, walking backwards"
    )

    prompt_input = prompt
    if prompt_enhancer and image is None:
        enhancer = PromptEnhancer()
        prompt_input = enhancer(prompt_input)
        del enhancer
        gc.collect()
        torch.cuda.empty_cache()

    if image is None:
        pipe = Text2VideoPipeline(model_path=model_id, dit_path=model_id, use_usp=use_usp, offload=offload)
    else:
        pipe = Image2VideoPipeline(model_path=model_id, dit_path=model_id, use_usp=use_usp, offload=offload)

    if teacache:
        pipe.transformer.initialize_teacache(
            enable_teacache=True,
            num_steps=inference_steps,
            teacache_thresh=teacache_thresh,
            use_ret_steps=use_ret_steps,
            ckpt_dir=model_id,
        )

    kwargs = {
        "prompt": prompt_input,
        "negative_prompt": negative_prompt,
        "num_frames": num_frames,
        "num_inference_steps": inference_steps,
        "guidance_scale": guidance_scale,
        "shift": shift,
        "generator": torch.Generator(device="cuda").manual_seed(seed),
        "height": height,
        "width": width,
    }

    if image is not None:
        kwargs["image"] = image.convert("RGB")

    with torch.amp.autocast("cuda", dtype=pipe.transformer.dtype), torch.no_grad():
        video_frames = pipe(**kwargs)[0]

    os.makedirs("gradio_videos", exist_ok=True)
    timestamp = time.strftime("%Y%m%d_%H%M%S")
    output_path = f"gradio_videos/{prompt[:50].replace('/', '')}_{seed}_{timestamp}.mp4"
    imageio.mimwrite(output_path, video_frames, fps=fps, quality=8, output_params=["-loglevel", "error"])
    
    return output_path

# Gradio UI
resolution_options = ["540P", "720P"]
model_options = MODEL_ID_CONFIG["text2video"] + MODEL_ID_CONFIG["image2video"]

app = gr.Interface(
    fn=generate_video,
    inputs=[
        gr.Textbox(label="Prompt"),
        gr.Dropdown(choices=model_options, value="Skywork/SkyReels-V2-I2V-1.3B-540P", label="Model ID", interactive=False),
        gr.Radio(choices=resolution_options, value="540P", label="Resolution", interactive=False),
        gr.Slider(minimum=16, maximum=200, value=97, step=1, label="Number of Frames"),
        gr.Image(type="filepath", label="Input Image (optional)"),
        gr.Slider(minimum=1.0, maximum=20.0, value=5.0, step=0.1, label="Guidance Scale"),
        gr.Slider(minimum=0.0, maximum=20.0, value=3.0, step=0.1, label="Shift"),
        gr.Slider(minimum=1, maximum=100, value=30, step=1, label="Inference Steps"),
        gr.Checkbox(label="Use USP"),
        gr.Checkbox(label="Offload", value=True, interactive=False),
        gr.Slider(minimum=1, maximum=60, value=24, step=1, label="FPS"),
        gr.Number(label="Seed (optional, random if empty)", precision=0),
        gr.Checkbox(label="Prompt Enhancer"),
        gr.Checkbox(label="Use TeaCache"),
        gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.01, label="TeaCache Threshold"),
        gr.Checkbox(label="Use Retention Steps"),
    ],
    outputs=gr.Video(label="Generated Video"),
    title="SkyReels V2 Video Generator",
)

app.launch(show_api=False, show_error=True, ssr_mode=False)