File size: 6,490 Bytes
8d7ec14 |
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 |
import torch
import gradio as gr
import os
import pathlib
import modules.infotext_utils as parameters_copypaste
from modules import script_callbacks
from modules.paths import models_path
from modules.ui_common import ToolButton, refresh_symbol
from modules.ui_components import ResizeHandleRow
from modules import shared
from modules_forge.forge_util import numpy_to_pytorch, pytorch_to_numpy, write_images_to_mp4
from ldm_patched.modules.sd import load_checkpoint_guess_config
from ldm_patched.contrib.external_video_model import VideoLinearCFGGuidance, SVD_img2vid_Conditioning
from ldm_patched.contrib.external import KSampler, VAEDecode
opVideoLinearCFGGuidance = VideoLinearCFGGuidance()
opSVD_img2vid_Conditioning = SVD_img2vid_Conditioning()
opKSampler = KSampler()
opVAEDecode = VAEDecode()
svd_root = os.path.join(models_path, 'svd')
os.makedirs(svd_root, exist_ok=True)
svd_filenames = []
def update_svd_filenames():
global svd_filenames
svd_filenames = [
pathlib.Path(x).name for x in
shared.walk_files(svd_root, allowed_extensions=[".pt", ".ckpt", ".safetensors"])
]
return svd_filenames
@torch.inference_mode()
@torch.no_grad()
def predict(filename, width, height, video_frames, motion_bucket_id, fps, augmentation_level,
sampling_seed, sampling_steps, sampling_cfg, sampling_sampler_name, sampling_scheduler,
sampling_denoise, guidance_min_cfg, input_image):
filename = os.path.join(svd_root, filename)
model_raw, _, vae, clip_vision = \
load_checkpoint_guess_config(filename, output_vae=True, output_clip=False, output_clipvision=True)
model = opVideoLinearCFGGuidance.patch(model_raw, guidance_min_cfg)[0]
init_image = numpy_to_pytorch(input_image)
positive, negative, latent_image = opSVD_img2vid_Conditioning.encode(
clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level)
output_latent = opKSampler.sample(model, sampling_seed, sampling_steps, sampling_cfg,
sampling_sampler_name, sampling_scheduler, positive,
negative, latent_image, sampling_denoise)[0]
output_pixels = opVAEDecode.decode(vae, output_latent)[0]
outputs = pytorch_to_numpy(output_pixels)
video_filename = write_images_to_mp4(outputs, fps=fps)
return outputs, video_filename
def on_ui_tabs():
with gr.Blocks() as svd_block:
with ResizeHandleRow():
with gr.Column():
input_image = gr.Image(label='Input Image', source='upload', type='numpy', height=400)
with gr.Row():
filename = gr.Dropdown(label="SVD Checkpoint Filename",
choices=svd_filenames,
value=svd_filenames[0] if len(svd_filenames) > 0 else None)
refresh_button = ToolButton(value=refresh_symbol, tooltip="Refresh")
refresh_button.click(
fn=lambda: gr.update(choices=update_svd_filenames()),
inputs=[], outputs=filename)
width = gr.Slider(label='Width', minimum=16, maximum=8192, step=8, value=1024)
height = gr.Slider(label='Height', minimum=16, maximum=8192, step=8, value=576)
video_frames = gr.Slider(label='Video Frames', minimum=1, maximum=4096, step=1, value=14)
motion_bucket_id = gr.Slider(label='Motion Bucket Id', minimum=1, maximum=1023, step=1, value=127)
fps = gr.Slider(label='Fps', minimum=1, maximum=1024, step=1, value=6)
augmentation_level = gr.Slider(label='Augmentation Level', minimum=0.0, maximum=10.0, step=0.01,
value=0.0)
sampling_steps = gr.Slider(label='Sampling Steps', minimum=1, maximum=200, step=1, value=20)
sampling_cfg = gr.Slider(label='CFG Scale', minimum=0.0, maximum=50.0, step=0.1, value=2.5)
sampling_denoise = gr.Slider(label='Sampling Denoise', minimum=0.0, maximum=1.0, step=0.01, value=1.0)
guidance_min_cfg = gr.Slider(label='Guidance Min Cfg', minimum=0.0, maximum=100.0, step=0.5, value=1.0)
sampling_sampler_name = gr.Radio(label='Sampler Name',
choices=['euler', 'euler_ancestral', 'heun', 'heunpp2', 'dpm_2',
'dpm_2_ancestral', 'lms', 'dpm_fast', 'dpm_adaptive',
'dpmpp_2s_ancestral', 'dpmpp_sde', 'dpmpp_sde_gpu',
'dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu',
'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu', 'ddpm', 'lcm', 'ddim',
'uni_pc', 'uni_pc_bh2'], value='euler')
sampling_scheduler = gr.Radio(label='Scheduler',
choices=['normal', 'karras', 'exponential', 'sgm_uniform', 'simple',
'ddim_uniform'], value='karras')
sampling_seed = gr.Number(label='Seed', value=12345, precision=0)
generate_button = gr.Button(value="Generate")
ctrls = [filename, width, height, video_frames, motion_bucket_id, fps, augmentation_level,
sampling_seed, sampling_steps, sampling_cfg, sampling_sampler_name, sampling_scheduler,
sampling_denoise, guidance_min_cfg, input_image]
with gr.Column():
output_video = gr.Video(autoplay=True)
output_gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain',
visible=True, height=1024, columns=4)
generate_button.click(predict, inputs=ctrls, outputs=[output_gallery, output_video])
PasteField = parameters_copypaste.PasteField
paste_fields = [
PasteField(width, "Size-1", api="width"),
PasteField(height, "Size-2", api="height"),
]
parameters_copypaste.add_paste_fields("svd", init_img=input_image, fields=paste_fields)
return [(svd_block, "SVD", "svd")]
update_svd_filenames()
script_callbacks.on_ui_tabs(on_ui_tabs)
|