mirror of
https://github.com/vladmandic/automatic
synced 2026-09-02 02:50:47 +02:00
improve state management
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
+1
-1
@@ -584,7 +584,7 @@ def check_diffusers():
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t_start = time.time()
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if args.skip_all:
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return
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target_commit = "7564fb016dabda0c943416190fc92398c50b1b20" # diffusers commit hash == 0.40.0.dev0 == 08-11-2026
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target_commit = "d5baa4fb548294f47dbca49890abd4b291204c60" # diffusers commit hash == 0.40.0.dev0 == 08-15-2026
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# if args.use_rocm or args.use_zluda:
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# sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
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pkg = package_spec('diffusers')
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+1
-1
@@ -548,7 +548,7 @@ def set_sdpa_params():
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log.debug(f'Torch attention installed: flashattn={flash} sageattention={sage}')
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from diffusers.models import attention_dispatch as a
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log.debug(f'Torch attention available: flash={a._CAN_USE_FLASH_ATTN} flash3={a._CAN_USE_FLASH_ATTN_3} aiter={a._CAN_USE_AITER_ATTN} sage={a._CAN_USE_SAGE_ATTN} flex={a._CAN_USE_FLEX_ATTN} npu={a._CAN_USE_NPU_ATTN} xla={a._CAN_USE_XLA_ATTN} xformers={a._CAN_USE_XFORMERS_ATTN} kernels={a.is_kernels_available()} sdnq=True') # pylint: disable=protected-access
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log.debug(f'Torch attention available: flash={a._CAN_USE_FLASH_ATTN} flash3={a._CAN_USE_FLASH_ATTN_3} sage={a._CAN_USE_SAGE_ATTN} flex={a._CAN_USE_FLEX_ATTN} npu={a._CAN_USE_NPU_ATTN} xla={a._CAN_USE_XLA_ATTN} xformers={a._CAN_USE_XFORMERS_ATTN} kernels={a.is_kernels_available()} sdnq=True') # pylint: disable=protected-access
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except Exception as e:
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log.warning(f'Torch SDPA: {e}')
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@@ -11,7 +11,6 @@ from modules import timer, errors
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from modules.logger import log
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log.info('Initializing: packages')
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initialized = False
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errors.install()
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logging.getLogger("DeepSpeed").disabled = True
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@@ -698,5 +698,5 @@ def run_ltx(task_id,
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progress.finish_task(task_id)
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p.close()
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log.info(f'Processed: fn="{video_file}" frames={num_frames} fps={fps} its={its} resolution={resolution} time={t_end-t0:.2f} timers={timer.process.dct()} memory={memstats.memory_stats()}')
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yield video_file, f'LTX: Generation completed | File {video_file} | Frames {num_frames} | Resolution {resolution} | f/s {fps} | it/s {its} ' + f"<div class='performance'><p>{summary} {memory}</p></div>"
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log.info(f'Processed: fn="{video_file}" frames={num_frames} fps={fps} its={its} resolution={resolution} time={t_end-t0:.2f} timers={timer.process.dct(no_total=True)} memory={memstats.memory_stats()}')
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yield video_file, f'Video | File {video_file} | Frames {num_frames} | Resolution {resolution} | f/s {fps} | it/s {its} ' + f"<div class='performance'><p>{summary} {memory}</p></div>"
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@@ -3,7 +3,7 @@ import time
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from PIL import Image
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import numpy as np
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from modules.logger import log
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from modules import shared, devices, processing, timer, progress, paths, sd_models, scripts_manager, call_queue, memstats, processing_video
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from modules import shared, devices, errors, processing, timer, progress, paths, sd_models, scripts_manager, call_queue, memstats, processing_video
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from modules.video_models import models_def, video_save, video_utils
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@@ -98,42 +98,6 @@ def generate(task_id, _ui_state,
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progress.start_task(task_id)
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memstats.reset_stats()
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timer.process.reset()
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workflow = load_model(model) # override workflow based on loaded model
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if not workflow:
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progress.finish_task(task_id)
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log.error('Video: model not loaded')
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return None, 'Model not loaded'
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p = processing.StableDiffusionProcessingVideo(
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sd_model=shared.sd_model,
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video_engine=engine,
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video_model=model,
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prompt=prompt,
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styles=styles,
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seed=int(seed) if seed is not None else -1,
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steps=int(steps),
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width=width,
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height=height,
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frames=frames,
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do_not_save_grid=True,
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do_not_save_samples=not mp4_frames,
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outpath_samples=paths.resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_video),
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ops=['video'],
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)
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video_minimax.apply_overrides(p, shared.sd_model, still=False, audio=audio_enable)
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video_minimax.set_sampler_shift(shared.sd_model, video_shift=video_shift, audio_shift=audio_shift)
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log.debug(f'Video: engine="{engine}" model="{model}" workflow={workflow} cls={shared.sd_model.__class__.__name__} shift={video_shift}:{audio_shift} kwargs={p.task_args}')
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processing.fix_seed(p)
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p.ops.append('video')
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p.scripts = scripts_manager.scripts_video
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p.script_args = args
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prepare_inputs(workflow, p, init_image, last_image, reference_media)
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_processed: processing.Processed = scripts_manager.scripts_video.run(p, *args)
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processed = processing.process_images(p)
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sd_models.offload_ondemand(shared.sd_model, reason='finish', force=True) # force offload all loaded modules to cpu
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devices.torch_gc(force=True) # free gpu memory before saving video
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# init vars
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pixels = None
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@@ -141,65 +105,109 @@ def generate(task_id, _ui_state,
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video_file = None
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aac_sample_rate = 32000
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audio = getattr(processed, 'audio', None) if audio_enable else None
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if audio is not None:
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audio = audio[0].float().cpu() if audio.ndim == 3 else audio.float().cpu()
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aac_sample_rate = getattr(shared.sd_model, 'audio_sampling_rate', 32000)
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try:
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workflow = load_model(model) # override workflow based on loaded model
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if not workflow:
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progress.finish_task(task_id)
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log.error('Video: model not loaded')
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return None, 'Model not loaded'
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p = processing.StableDiffusionProcessingVideo(
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sd_model=shared.sd_model,
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video_engine=engine,
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video_model=model,
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prompt=prompt,
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styles=styles,
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seed=int(seed) if seed is not None else -1,
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steps=int(steps),
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width=width,
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height=height,
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frames=frames,
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do_not_save_grid=True,
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do_not_save_samples=not mp4_frames,
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outpath_samples=paths.resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_video),
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ops=['video'],
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)
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video_minimax.apply_overrides(p, shared.sd_model, still=False, audio=audio_enable)
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video_minimax.set_sampler_shift(shared.sd_model, video_shift=video_shift, audio_shift=audio_shift)
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log.debug(f'Video: engine="{engine}" model="{model}" workflow={workflow} cls={shared.sd_model.__class__.__name__} shift={video_shift}:{audio_shift} kwargs={p.task_args}')
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processing.fix_seed(p)
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p.ops.append('video')
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p.scripts = scripts_manager.scripts_video
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p.script_args = args
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images = getattr(processed, 'images', [])
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if isinstance(images, list):
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pixels = video_save.images_to_tensor(images)
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elif isinstance(images, np.ndarray):
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pixels = video_save.numpy_to_tensor(images)
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else:
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log.error(f'Video: images={images} type={type(images)} unsupported')
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prepare_inputs(workflow, p, init_image, last_image, reference_media)
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if pixels is None:
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return None, "MiniMax: No frames generated"
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_processed: processing.Processed = scripts_manager.scripts_video.run(p, *args)
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processed = processing.process_images(p)
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if mp4_interpolate > 0:
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p.video_interpolate = mp4_interpolate
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from modules.processing_video import apply_video_interpolation
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# pixels is 5-D (N,C,T,H,W) in [-1,1]; RIFE needs 4-D (T,C,H,W) in [0,1]
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x = pixels.squeeze(0).permute(1, 0, 2, 3)
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x = (x.clamp(-1., 1.) + 1.0) * 0.5
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x = apply_video_interpolation(p, x, count=mp4_interpolate) # sets p.video_interpolated otherwise main save_video would do it also
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x = x * 2.0 - 1.0
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pixels = x.permute(1, 0, 2, 3).unsqueeze(0)
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sd_models.offload_ondemand(shared.sd_model, reason='finish', force=True) # force offload all loaded modules to cpu
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devices.torch_gc(force=True) # free gpu memory before saving video
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save_fps = mp4_fps * processing_video.interpolation_factor(p)
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num_frames, video_file, _thumb = video_save.save_video(
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p=p,
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pixels=pixels,
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audio=audio,
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mp4_fps=save_fps,
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mp4_codec=mp4_codec,
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mp4_opt=mp4_opt,
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mp4_ext=mp4_ext,
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mp4_sf=mp4_sf,
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mp4_video=mp4_video,
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mp4_frames=mp4_frames,
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mp4_thumb=mp4_thumb,
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mp4_interpolate=mp4_interpolate,
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aac_sample_rate=aac_sample_rate,
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metadata={},
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)
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_n, _c, _t, h, w = pixels.shape
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del pixels
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if audio is not None:
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del audio
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audio = getattr(processed, 'audio', None) if audio_enable else None
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if audio is not None:
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audio = audio[0].float().cpu() if audio.ndim == 3 else audio.float().cpu()
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aac_sample_rate = getattr(shared.sd_model, 'audio_sampling_rate', 32000)
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images = getattr(processed, 'images', [])
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if isinstance(images, list):
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pixels = video_save.images_to_tensor(images)
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elif isinstance(images, np.ndarray):
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pixels = video_save.numpy_to_tensor(images)
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else:
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log.error(f'Video: images={images} type={type(images)} unsupported')
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if pixels is None:
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return None, "MiniMax: No frames generated"
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if mp4_interpolate > 0:
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p.video_interpolate = mp4_interpolate
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from modules.processing_video import apply_video_interpolation
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# pixels is 5-D (N,C,T,H,W) in [-1,1]; RIFE needs 4-D (T,C,H,W) in [0,1]
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x = pixels.squeeze(0).permute(1, 0, 2, 3)
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x = (x.clamp(-1., 1.) + 1.0) * 0.5
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x = apply_video_interpolation(p, x, count=mp4_interpolate) # sets p.video_interpolated otherwise main save_video would do it also
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x = x * 2.0 - 1.0
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pixels = x.permute(1, 0, 2, 3).unsqueeze(0)
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save_fps = mp4_fps * processing_video.interpolation_factor(p)
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num_frames, video_file, _thumb = video_save.save_video(
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p=p,
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pixels=pixels,
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audio=audio,
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mp4_fps=save_fps,
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mp4_codec=mp4_codec,
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mp4_opt=mp4_opt,
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mp4_ext=mp4_ext,
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mp4_sf=mp4_sf,
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mp4_video=mp4_video,
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mp4_frames=mp4_frames,
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mp4_thumb=mp4_thumb,
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mp4_interpolate=mp4_interpolate,
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aac_sample_rate=aac_sample_rate,
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metadata={},
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)
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_n, _c, _t, h, w = pixels.shape
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del pixels
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if audio is not None:
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del audio
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except Exception as e:
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log.error(f'Video: engine="{engine}" model="{model}" workflow={workflow} {e}')
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errors.display(e, 'Video')
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finally:
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jobid = getattr(shared.sd_model, 'sdnext_phaseid', None) # previous jobid if any
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shared.state.end(jobid) # clear the previous job if exists
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progress.finish_task(task_id)
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p.close()
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t1 = time.time()
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progress.finish_task(task_id)
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p.close()
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resolution = f'{w}x{h}' if num_frames > 0 else None
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summary = timer.process.summary(min_time=0.25, total=False).replace('=', ' ')
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memory = shared.mem_mon.summary()
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total_time = max(t1 - t0, 1e-6)
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fps = f'{num_frames/total_time:.2f}'
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its = f'{(steps)/total_time:.3f}'
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log.info(f'Processed: fn="{video_file}" frames={num_frames} fps={fps} its={its} resolution={resolution} time={total_time:.2f} timers={timer.process.dct()} memory={memstats.memory_stats()}')
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log.info(f'Processed: fn="{video_file}" frames={num_frames} fps={fps} its={its} resolution={resolution} time={total_time:.2f} timers={timer.process.dct(no_total=True)} memory={memstats.memory_stats()}')
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ui_text = f'MiniMax: Generation completed | File {video_file} | Frames {num_frames} | Resolution {resolution} | f/s {fps} | it/s {its} ' + f"<div class='performance'><p>{summary} {memory}</p></div>"
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ui_text = f'Video | File {video_file} | Frames {num_frames} | Resolution {resolution} | f/s {fps} | it/s {its} ' + f"<div class='performance'><p>{summary} {memory}</p></div>"
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return video_file, ui_text
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+5
-2
@@ -20,6 +20,7 @@ def start_task(id_task):
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global current_task # pylint: disable=global-statement
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current_task = id_task
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pending_tasks.pop(id_task, None)
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log.debug(f'State: start id={id_task} pending={len(pending_tasks)} finished={len(finished_tasks)}')
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def record_results(id_task, res):
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@@ -30,10 +31,12 @@ def record_results(id_task, res):
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def finish_task(id_task):
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global current_task # pylint: disable=global-statement
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log.debug(f'State: end id={id_task}')
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if current_task == id_task:
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current_task = None
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finished_tasks.append(id_task)
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if len(finished_tasks) > 16:
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if id_task not in finished_tasks:
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finished_tasks.append(id_task)
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if len(finished_tasks) > 1024*1024:
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finished_tasks.pop(0)
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