mirror of https://github.com/vladmandic/automatic
205 lines
11 KiB
Python
205 lines
11 KiB
Python
"""
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Lightweight AnimateDiff implementation in Diffusers
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Docs: <https://huggingface.co/docs/diffusers/api/pipelines/animatediff>
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TODO animatediff items:
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- SDXL
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- Custom models
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- Custom LORAs
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- Enable second pass
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- TemporalDiff: https://huggingface.co/CiaraRowles/TemporalDiff/tree/main
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- AnimateFace: https://huggingface.co/nlper2022/animatediff_face_512/tree/main
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"""
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import os
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import gradio as gr
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import diffusers
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from modules import scripts, processing, shared, devices, sd_models
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# config
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ADAPTERS = {
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'None': None,
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'Motion 1.5 v3' :'vladmandic/animatediff-v3',
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'Motion 1.5 v2' :'guoyww/animatediff-motion-adapter-v1-5-2',
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'Motion 1.5 v1': 'guoyww/animatediff-motion-adapter-v1-5',
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'Motion 1.4': 'guoyww/animatediff-motion-adapter-v1-4',
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'TemporalDiff': 'vladmandic/temporaldiff',
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'AnimateFace': 'vladmandic/animateface',
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# 'LongAnimateDiff 32': 'vladmandic/longanimatediff-32',
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# 'LongAnimateDiff 64': 'vladmandic/longanimatediff-64',
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# 'Motion SD-XL Beta v1' :'vladmandic/animatediff-sdxl',
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}
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LORAS = {
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'None': None,
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'Zoom-in': 'guoyww/animatediff-motion-lora-zoom-in',
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'Zoom-out': 'guoyww/animatediff-motion-lora-zoom-out',
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'Pan-left': 'guoyww/animatediff-motion-lora-pan-left',
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'Pan-right': 'guoyww/animatediff-motion-lora-pan-right',
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'Tilt-up': 'guoyww/animatediff-motion-lora-tilt-up',
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'Tilt-down': 'guoyww/animatediff-motion-lora-tilt-down',
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'Roll-left': 'guoyww/animatediff-motion-lora-rolling-anticlockwise',
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'Roll-right': 'guoyww/animatediff-motion-lora-rolling-clockwise',
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}
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# state
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motion_adapter = None # instance of diffusers.MotionAdapter
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loaded_adapter = None # name of loaded adapter
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orig_pipe = None # original sd_model pipeline
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def set_adapter(adapter_name: str = 'None'):
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if shared.sd_model is None:
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return
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if shared.backend != shared.Backend.DIFFUSERS:
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shared.log.warning('AnimateDiff: not in diffusers mode')
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return
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global motion_adapter, loaded_adapter, orig_pipe # pylint: disable=global-statement
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# adapter_name = name if name is not None and isinstance(name, str) else loaded_adapter
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if adapter_name is None or adapter_name == 'None' or shared.sd_model is None:
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motion_adapter = None
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loaded_adapter = None
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if orig_pipe is not None:
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shared.log.debug(f'AnimateDiff restore pipeline: adapter="{loaded_adapter}"')
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shared.sd_model = orig_pipe
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orig_pipe = None
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return
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if shared.sd_model_type != 'sd' and shared.sd_model_type != 'sdxl':
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shared.log.warning(f'AnimateDiff: unsupported model type: {shared.sd_model.__class__.__name__}')
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return
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if motion_adapter is not None and loaded_adapter == adapter_name and shared.sd_model.__class__.__name__ == 'AnimateDiffPipeline':
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shared.log.debug(f'AnimateDiff cache: adapter="{adapter_name}"')
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return
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if getattr(shared.sd_model, 'image_encoder', None) is not None:
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shared.log.debug('AnimateDiff: unloading IP adapter')
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# shared.sd_model.image_encoder = None
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# shared.sd_model.unet.set_default_attn_processor()
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shared.sd_model.unet.config.encoder_hid_dim_type = None
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if adapter_name.endswith('.ckpt') or adapter_name.endswith('.safetensors'):
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import huggingface_hub as hf
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folder, filename = os.path.split(adapter_name)
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adapter_name = hf.hf_hub_download(repo_id=folder, filename=filename, cache_dir=shared.opts.diffusers_dir)
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try:
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shared.log.info(f'AnimateDiff load: adapter="{adapter_name}"')
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motion_adapter = None
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motion_adapter = diffusers.MotionAdapter.from_pretrained(adapter_name, cache_dir=shared.opts.diffusers_dir, torch_dtype=devices.dtype, low_cpu_mem_usage=False, device_map=None)
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motion_adapter.to(shared.device)
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sd_models.set_diffuser_options(motion_adapter, vae=None, op='adapter')
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loaded_adapter = adapter_name
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new_pipe = diffusers.AnimateDiffPipeline(
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vae=shared.sd_model.vae,
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text_encoder=shared.sd_model.text_encoder,
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tokenizer=shared.sd_model.tokenizer,
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unet=shared.sd_model.unet,
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scheduler=shared.sd_model.scheduler,
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feature_extractor=getattr(shared.sd_model, 'feature_extractor', None),
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image_encoder=getattr(shared.sd_model, 'image_encoder', None),
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motion_adapter=motion_adapter,
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)
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orig_pipe = shared.sd_model
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shared.sd_model = new_pipe
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sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
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sd_models.copy_diffuser_options(new_pipe, orig_pipe)
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sd_models.set_diffuser_options(shared.sd_model, vae=None, op='model')
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shared.log.debug(f'AnimateDiff create pipeline: adapter="{loaded_adapter}"')
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except Exception as e:
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motion_adapter = None
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loaded_adapter = None
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shared.log.error(f'AnimateDiff load error: adapter="{adapter_name}" {e}')
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class Script(scripts.Script):
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def title(self):
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return 'AnimateDiff'
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def show(self, _is_img2img):
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return scripts.AlwaysVisible if shared.backend == shared.Backend.DIFFUSERS else False
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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.Accordion('AnimateDiff', open=False, elem_id='animatediff'):
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with gr.Row():
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adapter_index = gr.Dropdown(label='Adapter', choices=list(ADAPTERS), value='None')
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frames = gr.Slider(label='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 sampler', value=True)
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with gr.Row():
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lora_index = gr.Dropdown(label='Lora', choices=list(LORAS), value='None')
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strength = gr.Slider(label='Strength', minimum=0.0, maximum=2.0, step=0.05, value=1.0)
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with gr.Row():
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latent_mode = gr.Checkbox(label='Latent mode', value=True, visible=False)
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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.Accordion('FreeInit', open=False):
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with gr.Row():
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fi_method = gr.Dropdown(label='Method', choices=['none', 'butterworth', 'ideal', 'gaussian'], value='none')
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with gr.Row():
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# fi_fast = gr.Checkbox(label='Fast sampling', value=False)
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fi_iters = gr.Slider(label='Iterations', minimum=1, maximum=10, step=1, value=3)
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fi_order = gr.Slider(label='Order', minimum=1, maximum=10, step=1, value=4)
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with gr.Row():
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fi_spatial = gr.Slider(label='Spatial frequency', minimum=0.0, maximum=1.0, step=0.05, value=0.25)
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fi_temporal = gr.Slider(label='Temporal frequency', minimum=0.0, maximum=1.0, step=0.05, value=0.25)
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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 [adapter_index, frames, lora_index, strength, latent_mode, video_type, duration, gif_loop, mp4_pad, mp4_interpolate, override_scheduler, fi_method, fi_iters, fi_order, fi_spatial, fi_temporal]
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def process(self, p: processing.StableDiffusionProcessing, adapter_index, frames, lora_index, strength, latent_mode, video_type, duration, gif_loop, mp4_pad, mp4_interpolate, override_scheduler, fi_method, fi_iters, fi_order, fi_spatial, fi_temporal): # pylint: disable=arguments-differ, unused-argument
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adapter = ADAPTERS[adapter_index]
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lora = LORAS[lora_index]
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set_adapter(adapter)
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if motion_adapter is None:
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return
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if override_scheduler:
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p.sampler_name = 'Default'
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shared.sd_model.scheduler = diffusers.DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="linear",
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clip_sample=False,
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num_train_timesteps=1000,
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rescale_betas_zero_snr=False,
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set_alpha_to_one=True,
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steps_offset=0,
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timestep_spacing="linspace",
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trained_betas=None,
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)
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shared.log.debug(f'AnimateDiff: adapter="{adapter}" lora="{lora}" strength={strength} video={video_type} scheduler={shared.sd_model.scheduler.__class__.__name__ if override_scheduler else p.sampler_name}')
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if lora is not None and lora != 'None':
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shared.sd_model.load_lora_weights(lora, adapter_name=lora)
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shared.sd_model.set_adapters([lora], adapter_weights=[strength])
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p.extra_generation_params['AnimateDiff Lora'] = f'{lora}:{strength}'
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if hasattr(shared.sd_model, 'enable_free_init') and fi_method != 'none':
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shared.sd_model.enable_free_init(
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num_iters=fi_iters,
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use_fast_sampling=False,
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method=fi_method,
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order=fi_order,
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spatial_stop_frequency=fi_spatial,
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temporal_stop_frequency=fi_temporal,
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)
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p.extra_generation_params['AnimateDiff'] = loaded_adapter
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p.do_not_save_grid = True
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if 'animatediff' not in p.ops:
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p.ops.append('animatediff')
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p.task_args['num_frames'] = frames
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p.task_args['num_inference_steps'] = p.steps
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if not latent_mode:
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p.task_args['output_type'] = 'np'
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def postprocess(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, adapter_index, frames, lora_index, strength, latent_mode, video_type, duration, gif_loop, mp4_pad, mp4_interpolate, override_scheduler, fi_method, fi_iters, fi_order, fi_spatial, fi_temporal): # pylint: disable=arguments-differ, unused-argument
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from modules.images import save_video
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if video_type != 'None':
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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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