mirror of
https://github.com/vladmandic/automatic
synced 2026-09-17 16:24:33 +02:00
Vladimir Mandic
112 lines
5.2 KiB
Python
112 lines
5.2 KiB
Python
import time
|
|
import torch
|
|
import gradio as gr
|
|
import diffusers
|
|
from modules import scripts, processing, shared, images, devices, sd_models, sd_checkpoint, model_quant
|
|
|
|
|
|
repo_id = 'tencent/HunyuanVideo'
|
|
"""
|
|
prompt_template = { # default
|
|
"template": (
|
|
"<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "
|
|
"1. The main content and theme of the video."
|
|
"2. The color, shape, size, texture, quantity, text, and spatial relationships of the contents, including objects, people, and anything else."
|
|
"3. Actions, events, behaviors temporal relationships, physical movement changes of the contents."
|
|
"4. Background environment, light, style, atmosphere, and qualities."
|
|
"5. Camera angles, movements, and transitions used in the video."
|
|
"6. Thematic and aesthetic concepts associated with the scene, i.e. realistic, futuristic, fairy tale, etc<|eot_id|>"
|
|
"<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
|
|
),
|
|
"crop_start": 95,
|
|
}
|
|
"""
|
|
|
|
|
|
class Script(scripts.Script):
|
|
def title(self):
|
|
return 'Video: Hunyuan Video'
|
|
|
|
def show(self, is_img2img):
|
|
return not is_img2img if shared.native else False
|
|
|
|
# return signature is array of gradio components
|
|
def ui(self, _is_img2img):
|
|
def video_type_change(video_type):
|
|
return [
|
|
gr.update(visible=video_type != 'None'),
|
|
gr.update(visible=video_type == 'GIF' or video_type == 'PNG'),
|
|
gr.update(visible=video_type == 'MP4'),
|
|
gr.update(visible=video_type == 'MP4'),
|
|
]
|
|
|
|
with gr.Row():
|
|
gr.HTML('<a href="https://huggingface.co/tencent/HunyuanVideo">  Hunyuan Video</a><br>')
|
|
with gr.Row():
|
|
num_frames = gr.Slider(label='Frames', minimum=9, maximum=257, step=1, value=45)
|
|
with gr.Row():
|
|
video_type = gr.Dropdown(label='Video file', choices=['None', 'GIF', 'PNG', 'MP4'], value='None')
|
|
duration = gr.Slider(label='Duration', minimum=0.25, maximum=10, step=0.25, value=2, visible=False)
|
|
with gr.Row():
|
|
gif_loop = gr.Checkbox(label='Loop', value=True, visible=False)
|
|
mp4_pad = gr.Slider(label='Pad frames', minimum=0, maximum=24, step=1, value=1, visible=False)
|
|
mp4_interpolate = gr.Slider(label='Interpolate frames', minimum=0, maximum=24, step=1, value=0, visible=False)
|
|
video_type.change(fn=video_type_change, inputs=[video_type], outputs=[duration, gif_loop, mp4_pad, mp4_interpolate])
|
|
return [num_frames, video_type, duration, gif_loop, mp4_pad, mp4_interpolate]
|
|
|
|
def run(self, p: processing.StableDiffusionProcessing, num_frames, video_type, duration, gif_loop, mp4_pad, mp4_interpolate): # pylint: disable=arguments-differ, unused-argument
|
|
# set params
|
|
num_frames = int(num_frames)
|
|
p.width = 32 * int(p.width // 32)
|
|
p.height = 32 * int(p.height // 32)
|
|
p.task_args['output_type'] = 'pil'
|
|
p.task_args['generator'] = torch.manual_seed(p.seed)
|
|
p.task_args['num_frames'] = num_frames
|
|
# p.task_args['prompt_template'] = prompt_template
|
|
p.sampler_name = 'Default'
|
|
p.do_not_save_grid = True
|
|
p.ops.append('video')
|
|
|
|
# load model
|
|
cls = diffusers.HunyuanVideoPipeline
|
|
if shared.sd_model.__class__ != cls:
|
|
sd_models.unload_model_weights()
|
|
kwargs = {}
|
|
kwargs = model_quant.create_bnb_config(kwargs)
|
|
kwargs = model_quant.create_ao_config(kwargs)
|
|
transformer = diffusers.HunyuanVideoTransformer3DModel.from_pretrained(
|
|
repo_id,
|
|
subfolder="transformer",
|
|
torch_dtype=devices.dtype,
|
|
revision="refs/pr/18",
|
|
cache_dir = shared.opts.hfcache_dir,
|
|
**kwargs
|
|
)
|
|
shared.sd_model = cls.from_pretrained(
|
|
repo_id,
|
|
transformer=transformer,
|
|
revision="refs/pr/18",
|
|
cache_dir = shared.opts.hfcache_dir,
|
|
torch_dtype=devices.dtype,
|
|
**kwargs
|
|
)
|
|
shared.sd_model.scheduler._shift = 7.0 # pylint: disable=protected-access
|
|
sd_models.set_diffuser_options(shared.sd_model)
|
|
shared.sd_model.sd_checkpoint_info = sd_checkpoint.CheckpointInfo(repo_id)
|
|
shared.sd_model.sd_model_hash = None
|
|
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
|
|
shared.sd_model.vae.enable_slicing()
|
|
shared.sd_model.vae.enable_tiling()
|
|
devices.torch_gc(force=True)
|
|
shared.log.debug(f'Video: cls={shared.sd_model.__class__.__name__} args={p.task_args}')
|
|
|
|
# run processing
|
|
t0 = time.time()
|
|
processed = processing.process_images(p)
|
|
t1 = time.time()
|
|
if processed is not None and len(processed.images) > 0:
|
|
shared.log.info(f'Video: frames={len(processed.images)} time={t1-t0:.2f}')
|
|
if video_type != 'None':
|
|
images.save_video(p, filename=None, images=processed.images, video_type=video_type, duration=duration, loop=gif_loop, pad=mp4_pad, interpolate=mp4_interpolate)
|
|
return processed
|