Hugging Face's logo

Spaces:
dubiousx
/
SUPIR
Running

App Files Community
SUPIR
File size: 3,302 Bytes 
a11fc96
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
import gradio as gr
import torch
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
import spaces

# 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()