mirror of https://github.com/vladmandic/automatic
173 lines
8.4 KiB
Python
173 lines
8.4 KiB
Python
import time
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import torch
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import gradio as gr
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import transformers
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import diffusers
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from modules import scripts, processing, shared, images, devices, sd_models, sd_checkpoint, model_quant, timer
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repo_id = 'tencent/HunyuanVideo'
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default_template = """Describe the video by detailing the following aspects:
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1. The main content and theme of the video.
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2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.
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3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.
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4. Background environment, light, style and atmosphere.
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5. Camera angles, movements, and transitions used in the video.
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6. Thematic and aesthetic concepts associated with the scene, i.e. realistic, futuristic, fairy tale, etc.
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"""
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def get_template(template: str = None):
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# diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video.DEFAULT_PROMPT_TEMPLATE
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base_template_pre = "<|start_header_id|>system<|end_header_id|>\n\n"
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base_template_post = "<|eot_id|>\n"
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base_template_end = "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
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if template is None or len(template) == 0:
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template = default_template
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template_lines = '\n'.join([line for line in template.split('\n') if len(line) > 0])
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prompt_template = {
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"crop_start": 95,
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"template": base_template_pre + template_lines + base_template_post + base_template_end
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}
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return prompt_template
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def hijack_decode(*args, **kwargs):
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t0 = time.time()
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vae: diffusers.AutoencoderKLHunyuanVideo = shared.sd_model.vae
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shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, exclude=['vae'])
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res = shared.sd_model.vae.orig_decode(*args, **kwargs)
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t1 = time.time()
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timer.process.add('vae', t1-t0)
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shared.log.debug(f'Video: decoder={vae.__class__.__name__} tile={vae.tile_sample_min_width}:{vae.tile_sample_min_height}:{vae.tile_sample_min_num_frames} stride={vae.tile_sample_stride_width}:{vae.tile_sample_stride_height}:{vae.tile_sample_stride_num_frames} time={t1-t0:.2f}')
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return res
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def hijack_encode_prompt(*args, **kwargs):
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t0 = time.time()
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res = shared.sd_model.vae.orig_encode_prompt(*args, **kwargs)
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t1 = time.time()
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timer.process.add('te', t1-t0)
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shared.log.debug(f'Video: encode cls={shared.sd_model.text_encoder.__class__.__name__} time={t1-t0:.2f}')
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shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
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return res
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class Script(scripts.Script):
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def title(self):
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return 'Video: Hunyuan Video'
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def show(self, is_img2img):
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return not is_img2img if shared.native else False
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# return signature is array of gradio components
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def ui(self, _is_img2img):
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def video_type_change(video_type):
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return [
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gr.update(visible=video_type != 'None'),
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gr.update(visible=video_type == 'GIF' or video_type == 'PNG'),
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gr.update(visible=video_type == 'MP4'),
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gr.update(visible=video_type == 'MP4'),
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]
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with gr.Row():
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gr.HTML('<a href="https://huggingface.co/tencent/HunyuanVideo">  Hunyuan Video</a><br>')
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with gr.Row():
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num_frames = gr.Slider(label='Frames', minimum=9, maximum=257, step=1, value=45)
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tile_frames = gr.Slider(label='Tile frames', minimum=1, maximum=64, step=1, value=16)
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with gr.Row():
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override_scheduler = gr.Checkbox(label='Override scheduler', value=True)
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with gr.Row():
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template = gr.TextArea(label='Prompt processor', lines=3, value=default_template)
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with gr.Row():
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video_type = gr.Dropdown(label='Video file', choices=['None', 'GIF', 'PNG', 'MP4'], value='None')
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duration = gr.Slider(label='Duration', minimum=0.25, maximum=10, step=0.25, value=2, visible=False)
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with gr.Row():
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gif_loop = gr.Checkbox(label='Loop', value=True, visible=False)
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mp4_pad = gr.Slider(label='Pad frames', minimum=0, maximum=24, step=1, value=1, visible=False)
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mp4_interpolate = gr.Slider(label='Interpolate frames', minimum=0, maximum=24, step=1, value=0, visible=False)
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video_type.change(fn=video_type_change, inputs=[video_type], outputs=[duration, gif_loop, mp4_pad, mp4_interpolate])
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return [num_frames, tile_frames, override_scheduler, template, video_type, duration, gif_loop, mp4_pad, mp4_interpolate]
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def run(self, p: processing.StableDiffusionProcessing, num_frames, tile_frames, override_scheduler, template, video_type, duration, gif_loop, mp4_pad, mp4_interpolate): # pylint: disable=arguments-differ, unused-argument
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# set params
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num_frames = int(num_frames)
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p.width = 16 * int(p.width // 16)
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p.height = 16 * int(p.height // 16)
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p.do_not_save_grid = True
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p.ops.append('video')
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# load model
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if shared.sd_model.__class__ != diffusers.HunyuanVideoPipeline:
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sd_models.unload_model_weights()
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t0 = time.time()
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kwargs = {}
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kwargs = model_quant.create_bnb_config(kwargs)
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kwargs = model_quant.create_ao_config(kwargs)
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transformer = diffusers.HunyuanVideoTransformer3DModel.from_pretrained(
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repo_id,
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subfolder="transformer",
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torch_dtype=devices.dtype,
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revision="refs/pr/18",
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cache_dir = shared.opts.hfcache_dir,
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**kwargs
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)
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shared.log.debug(f'Video: module={transformer.__class__.__name__}')
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text_encoder = transformers.LlamaModel.from_pretrained(
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repo_id,
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subfolder="text_encoder",
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revision="refs/pr/18",
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cache_dir = shared.opts.hfcache_dir,
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torch_dtype=devices.dtype,
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**kwargs
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)
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shared.log.debug(f'Video: module={text_encoder.__class__.__name__}')
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shared.sd_model = diffusers.HunyuanVideoPipeline.from_pretrained(
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repo_id,
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transformer=transformer,
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text_encoder=text_encoder,
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revision="refs/pr/18",
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cache_dir = shared.opts.hfcache_dir,
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torch_dtype=devices.dtype,
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**kwargs
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)
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t1 = time.time()
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shared.log.debug(f'Video: load cls={shared.sd_model.__class__.__name__} repo="{repo_id}" dtype={devices.dtype} time={t1-t0:.2f}')
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sd_models.set_diffuser_options(shared.sd_model)
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shared.sd_model.sd_checkpoint_info = sd_checkpoint.CheckpointInfo(repo_id)
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shared.sd_model.sd_model_hash = None
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shared.sd_model.vae.orig_decode = shared.sd_model.vae.decode
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shared.sd_model.vae.orig_encode_prompt = shared.sd_model.encode_prompt
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shared.sd_model.vae.decode = hijack_decode
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shared.sd_model.encode_prompt = hijack_encode_prompt
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shared.sd_model.vae.enable_slicing()
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shared.sd_model.vae.enable_tiling()
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shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
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devices.torch_gc(force=True)
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if override_scheduler:
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p.sampler_name = 'Default'
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shared.sd_model.scheduler._shift = 7.0 # pylint: disable=protected-access
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# encode prompt
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processing.fix_seed(p)
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p.task_args['num_frames'] = num_frames
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p.task_args['output_type'] = 'pil'
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p.task_args['generator'] = torch.manual_seed(p.seed)
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# p.task_args['prompt'] = None
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# p.task_args['prompt_embeds'], p.task_args['pooled_prompt_embeds'], p.task_args['prompt_attention_mask'] = shared.sd_model.encode_prompt(prompt=p.prompt, prompt_template=get_template(template), device=devices.device)
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# run processing
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t0 = time.time()
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shared.sd_model.vae.tile_sample_min_num_frames = tile_frames
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shared.state.disable_preview = True
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shared.log.debug(f'Video: cls={shared.sd_model.__class__.__name__} width={p.width} height={p.height} frames={num_frames}')
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processed = processing.process_images(p)
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shared.state.disable_preview = False
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t1 = time.time()
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if processed is not None and len(processed.images) > 0:
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shared.log.info(f'Video: frames={len(processed.images)} time={t1-t0:.2f}')
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if video_type != 'None':
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images.save_video(p, filename=None, images=processed.images, video_type=video_type, duration=duration, loop=gif_loop, pad=mp4_pad, interpolate=mp4_interpolate)
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return processed
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