mirror of https://github.com/vladmandic/automatic
122 lines
5.5 KiB
Python
122 lines
5.5 KiB
Python
import time
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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_manager, processing, shared, images, devices, sd_models, sd_checkpoint, model_quant, timer, sd_hijack_te
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repo_id = 'rhymes-ai/Allegro'
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def hijack_decode(*args, **kwargs):
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t0 = time.time()
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vae: diffusers.AutoencoderKLAllegro = 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: vae={vae.__class__.__name__} time={t1-t0:.2f}')
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return res
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class Script(scripts_manager.Script):
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def title(self):
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return 'Video: Allegro (Legacy)'
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def show(self, is_img2img):
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return not is_img2img
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# return signature is array of gradio components
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def ui(self, is_img2img):
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with gr.Row():
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gr.HTML('<a href="https://huggingface.co/rhymes-ai/Allegro">  Allegro Video</a><br>')
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with gr.Row():
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num_frames = gr.Slider(label='Frames', minimum=4, maximum=88, step=1, value=22)
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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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from modules.ui_sections import create_video_inputs
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video_type, duration, gif_loop, mp4_pad, mp4_interpolate = create_video_inputs(tab='img2img' if is_img2img else 'txt2img')
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return [num_frames, override_scheduler, video_type, duration, gif_loop, mp4_pad, mp4_interpolate]
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def run(self, p: processing.StableDiffusionProcessing, num_frames, override_scheduler, 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 = 8 * int(p.width // 8)
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p.height = 8 * int(p.height // 8)
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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.AllegroPipeline:
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sd_models.unload_model_weights()
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t0 = time.time()
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quant_args = model_quant.create_config()
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transformer = diffusers.AllegroTransformer3DModel.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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cache_dir=shared.opts.hfcache_dir,
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**quant_args
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)
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shared.log.debug(f'Video: module={transformer.__class__.__name__}')
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text_encoder = transformers.T5EncoderModel.from_pretrained(
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repo_id,
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subfolder="text_encoder",
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cache_dir=shared.opts.hfcache_dir,
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torch_dtype=devices.dtype,
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**quant_args
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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.AllegroPipeline.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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cache_dir=shared.opts.hfcache_dir,
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torch_dtype=devices.dtype,
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**quant_args
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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.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.vae.enable_tiling()
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sd_hijack_te.init_hijack(shared.sd_model)
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# shared.sd_model.vae.enable_slicing()
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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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processing.fix_seed(p)
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if override_scheduler:
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p.sampler_name = 'Default'
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p.steps = 100
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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['clean_caption'] = False
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p.all_prompts, p.all_negative_prompts = shared.prompt_styles.apply_styles_to_prompts([p.prompt], [p.negative_prompt], p.styles, [p.seed])
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p.task_args['prompt'] = p.all_prompts[0]
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p.task_args['negative_prompt'] = p.all_negative_prompts[0]
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# w = shared.sd_model.transformer.config.sample_width * shared.sd_model.vae_scale_factor_spatial
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# h = shared.sd_model.transformer.config.sample_height * shared.sd_model.vae_scale_factor_spatial
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# n = shared.sd_model.transformer.config.sample_frames * shared.sd_model.vae_scale_factor_temporal
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# run processing
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t0 = time.time()
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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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