Spaces:
Runtime error
Runtime error
File size: 5,407 Bytes
3b06696 a3d2317 3b16b97 3b06696 bf4a508 3b06696 a3595a3 2e6eea6 a3595a3 3b06696 b49ce0a 3b06696 3b16b97 3b06696 294a061 3b06696 a3595a3 3b06696 a3595a3 3b06696 aeacc98 3b06696 a3595a3 3b06696 a3595a3 3b06696 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
import math
import os
from glob import glob
from pathlib import Path
from typing import Optional
import cv2
import numpy as np
import torch
from einops import rearrange, repeat
from omegaconf import OmegaConf
from PIL import Image
from torchvision.transforms import ToTensor
from scripts.util.detection.nsfw_and_watermark_dectection import \
DeepFloydDataFiltering
from sgm.inference.helpers import embed_watermark
from sgm.util import default, instantiate_from_config
from huggingface_hub import hf_hub_download
import gradio as gr
import uuid
from simple_video_sample import sample
num_frames = 25
num_steps = 30
model_config = "scripts/sampling/configs/svd_xt.yaml"
device = "cuda"
#hf_hub_download(repo_id="stabilityai/stable-video-diffusion-img2vid-xt", filename="svd_xt.safetensors", local_dir="checkpoints", token=os.getenv("HF_TOKEN"))
css = '''
.gradio-container{max-width:850px !important}
'''
def sample(
input_path: str,
num_frames: Optional[int] = 25,
num_steps: Optional[int] = 30,
version: str = "svd_xt",
fps_id: int = 6,
motion_bucket_id: int = 127,
cond_aug: float = 0.02,
seed: int = 23,
decoding_t: int = 7, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
):
output_folder = str(uuid.uuid4())
sample(input_path, version, output_folder, decoding_t)
return f"{output_folder}/000000.mp4"
def get_unique_embedder_keys_from_conditioner(conditioner):
return list(set([x.input_key for x in conditioner.embedders]))
def get_batch(keys, value_dict, N, T, device):
batch = {}
batch_uc = {}
for key in keys:
if key == "fps_id":
batch[key] = (
torch.tensor([value_dict["fps_id"]])
.to(device)
.repeat(int(math.prod(N)))
)
elif key == "motion_bucket_id":
batch[key] = (
torch.tensor([value_dict["motion_bucket_id"]])
.to(device)
.repeat(int(math.prod(N)))
)
elif key == "cond_aug":
batch[key] = repeat(
torch.tensor([value_dict["cond_aug"]]).to(device),
"1 -> b",
b=math.prod(N),
)
elif key == "cond_frames":
batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0])
elif key == "cond_frames_without_noise":
batch[key] = repeat(
value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0]
)
else:
batch[key] = value_dict[key]
if T is not None:
batch["num_video_frames"] = T
for key in batch.keys():
if key not in batch_uc and isinstance(batch[key], torch.Tensor):
batch_uc[key] = torch.clone(batch[key])
return batch, batch_uc
def resize_image(image_path, output_size=(1024, 576)):
with Image.open(image_path) as image:
# Calculate aspect ratios
target_aspect = output_size[0] / output_size[1] # Aspect ratio of the desired size
image_aspect = image.width / image.height # Aspect ratio of the original image
# Resize then crop if the original image is larger
if image_aspect > target_aspect:
# Resize the image to match the target height, maintaining aspect ratio
new_height = output_size[1]
new_width = int(new_height * image_aspect)
resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
# Calculate coordinates for cropping
left = (new_width - output_size[0]) / 2
top = 0
right = (new_width + output_size[0]) / 2
bottom = output_size[1]
else:
# Resize the image to match the target width, maintaining aspect ratio
new_width = output_size[0]
new_height = int(new_width / image_aspect)
resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
# Calculate coordinates for cropping
left = 0
top = (new_height - output_size[1]) / 2
right = output_size[0]
bottom = (new_height + output_size[1]) / 2
# Crop the image
cropped_image = resized_image.crop((left, top, right, bottom))
return cropped_image
with gr.Blocks(css=css) as demo:
gr.Markdown('''# Stable Video Diffusion - Image2Video - XT
Generate 25 frames of video from a single image with SDV-XT. [Join the waitlist](https://stability.ai/contact) for the text-to-video web experience
''')
with gr.Column():
image = gr.Image(label="Upload your image (it will be center cropped to 1024x576)", type="filepath")
generate_btn = gr.Button("Generate")
#with gr.Accordion("Advanced options", open=False):
# cond_aug = gr.Slider(label="Conditioning augmentation", value=0.02, minimum=0.0)
# seed = gr.Slider(label="Seed", value=42, minimum=0, maximum=int(1e9), step=1)
#decoding_t = gr.Slider(label="Decode frames at a time", value=6, minimum=1, maximum=14, interactive=False)
# saving_fps = gr.Slider(label="Saving FPS", value=6, minimum=6, maximum=48, step=6)
with gr.Column():
video = gr.Video()
image.upload(fn=resize_image, inputs=image, outputs=image)
generate_btn.click(fn=sample, inputs=[image], outputs=video, api_name="video")
demo.launch()
|