import os import time import torch from PIL import Image from modules import shared, errors, timer, memstats, progress, processing, sd_models, sd_samplers, devices, extra_networks, call_queue from modules.logger import log from modules.ltx import ltx_capabilities from modules.ltx.ltx_diffusers_patch import apply_patch as apply_ltx_diffusers_patch from modules.ltx.ltx_util import get_bucket, get_frames, load_model, load_upsample, load_upsample_2x, get_conditions, get_generator, get_prompts, ltx_scheduler_opts, vae_decode apply_ltx_diffusers_patch() from modules.processing_callbacks import diffusers_callback from modules.video_models.video_vae import set_vae_params from modules.video_models.video_save import save_video from modules.video_models.video_utils import check_av debug = log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None else lambda *args, **kwargs: None upsample_repo_id_09 = 'a-r-r-o-w/LTX-Video-0.9.7-Latent-Spatial-Upsampler-diffusers' # Upsampler weights are tied to the family VAE; using the wrong one preserves structure # but drifts per-channel latent statistics (decodes desaturated / crushed contrast). upsample_repo_id_20 = 'Lightricks/LTX-2' upsample_repo_id_23 = 'CalamitousFelicitousness/LTX-2.3-Spatial-Upsampler-x2-1.1-Diffusers' upsample_pipe = None upsample_pipe_2x = None STAGE2_DEV_LORA_ADAPTER = 'ltx2_stage2_distilled' def _prompt_tensors_to_device(*tensors): return tuple(t.to(device=devices.device) if torch.is_tensor(t) else t for t in tensors) def _canonical_ltx2_guidance(caps) -> dict: # Four-way composition (cfg + stg + modality + rescale) from huggingface/diffusers#13217. # Distilled bakes these into its sigma schedule; skip or we double-apply. if caps.family != '2.x' or caps.is_distilled: return {} return { 'stg_scale': caps.stg_default_scale, 'modality_scale': caps.modality_default_scale, 'guidance_rescale': caps.guidance_rescale_default, 'spatio_temporal_guidance_blocks': list(caps.stg_default_blocks), 'audio_guidance_scale': 7.0, 'audio_stg_scale': 1.0, 'audio_modality_scale': 3.0, 'audio_guidance_rescale': 0.7, } def _canonical_stage2_kwargs() -> dict: # Stage 2 identity guidance from huggingface/diffusers#13217. Applied to both Dev (with # distilled LoRA on top) and Distilled. Distilled was trained at identity; Dev's four-way # composition on top of the LoRA double-dips and produces striping/flicker. from diffusers.pipelines.ltx2.utils import STAGE_2_DISTILLED_SIGMA_VALUES return { 'sigmas': list(STAGE_2_DISTILLED_SIGMA_VALUES), 'noise_scale': float(STAGE_2_DISTILLED_SIGMA_VALUES[0]), 'guidance_scale': 1.0, 'stg_scale': 0.0, 'modality_scale': 1.0, 'guidance_rescale': 0.0, 'audio_guidance_scale': 1.0, 'audio_stg_scale': 0.0, 'audio_modality_scale': 1.0, 'audio_guidance_rescale': 0.0, 'spatio_temporal_guidance_blocks': None, } def _latent_pass(caps, prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask, width, height, frames, steps, guidance_scale, mp4_fps, conditions, image_cond_noise_scale, seed, image=None): prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask = _prompt_tensors_to_device( prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask, ) base_args = { 'prompt_embeds': prompt_embeds, 'prompt_attention_mask': prompt_attention_mask, 'negative_prompt_embeds': negative_prompt_embeds, 'negative_prompt_attention_mask': negative_prompt_attention_mask, 'width': get_bucket(width), 'height': get_bucket(height), 'num_frames': get_frames(frames), 'num_inference_steps': steps, 'generator': get_generator(seed), 'callback_on_step_end': diffusers_callback, 'output_type': 'latent', } if guidance_scale is not None and guidance_scale > 0: base_args['guidance_scale'] = guidance_scale if caps.supports_frame_rate_kwarg: base_args['frame_rate'] = float(mp4_fps) if caps.supports_image_cond_noise_scale and image_cond_noise_scale is not None: base_args['image_cond_noise_scale'] = image_cond_noise_scale if caps.supports_multi_condition and conditions: base_args['conditions'] = conditions if caps.is_i2v and caps.repo_cls_name in ('LTXImageToVideoPipeline', 'LTX2ImageToVideoPipeline') and image is not None: base_args['image'] = image if caps.family == '2.x' and caps.is_distilled: from diffusers.pipelines.ltx2.utils import DISTILLED_SIGMA_VALUES base_args['sigmas'] = list(DISTILLED_SIGMA_VALUES) base_args.pop('num_inference_steps', None) base_args.update(_canonical_ltx2_guidance(caps)) if caps.use_cross_timestep: base_args['use_cross_timestep'] = True log.debug(f'Video: cls={shared.sd_model.__class__.__name__} op=latent_pass args_keys={list(base_args.keys())}') result = shared.sd_model(**base_args) latents = result.frames[0] if hasattr(result, 'frames') else None return latents def run_ltx(task_id, _ui_state, model: str, prompt: str, negative: str, styles: list, width: int, height: int, frames: int, steps: int, sampler_index: int, guidance_scale: float, sampler_shift: float, dynamic_shift: bool, seed: int, upsample_enable: bool, upsample_ratio: float, refine_enable: bool, refine_strength: float, condition_strength: float, ltx_init_image, condition_last, condition_files, condition_video, condition_video_frames: int, condition_video_skip: int, decode_timestep: float, image_cond_noise_scale: float, mp4_fps: int, mp4_interpolate: int, mp4_codec: str, mp4_ext: str, mp4_opt: str, mp4_video: bool, mp4_frames: bool, mp4_sf: bool, mp4_thumb: bool, audio_enable: bool, _overrides, ): def abort(e, ok: bool = False, p=None): if ok: log.info(e) else: log.error(f'Video: cls={shared.sd_model.__class__.__name__} op=base {e}') errors.display(e, 'LTX') if p is not None: extra_networks.deactivate(p) shared.state.end() progress.finish_task(task_id) yield None, f'LTX Error: {str(e)}' if model is None or len(model) == 0 or model == 'None': yield from abort('Video: no model selected', ok=True) return if model.startswith('─'): yield from abort('Video: dropdown separator selected, pick an actual model below', ok=True) return check_av() progress.add_task_to_queue(task_id) with call_queue.get_lock(): progress.start_task(task_id) memstats.reset_stats() timer.process.reset() yield None, 'LTX: Loading...' engine = 'LTX Video' load_model(engine, model) caps = ltx_capabilities.get_caps(model) if caps is None or not shared.sd_model.__class__.__name__.startswith('LTX'): yield from abort(f'Video: cls={shared.sd_model.__class__.__name__} selected model is not LTX', ok=True) return # Lightricks TI2VidTwoStagesPipeline: Stage 1 at half-res, 2x upsample, Stage 2 refine at target. # Auto-couple when the user picks Refine but not Upsample. Both Dev and Distilled refine paths # expect upsampled latents; same-res refine on Distilled produces oversaturation. Condition # variants still need per-stage conditioning rebuild and are excluded by supports_two_stage_refine. auto_refine_upsample = ( refine_enable and caps.supports_two_stage_refine and not upsample_enable ) effective_upsample_enable = upsample_enable or auto_refine_upsample effective_upsample_ratio = upsample_ratio if upsample_enable else 2.0 target_w = get_bucket(width) target_h = get_bucket(height) if auto_refine_upsample: # Stage 1 at target/2 needs multiple-of-32; 2x upsample then forces final divisible by 64. # Derive final from base, otherwise Stage 2 silently falls to base*2 != target. base_w = get_bucket(target_w // 2) base_h = get_bucket(target_h // 2) final_w = base_w * 2 final_h = base_h * 2 if (final_w, final_h) != (target_w, target_h): log.warning(f'LTX: resolution={target_w}x{target_h} adjusted={final_w}x{final_h} two-stage refine needs resolution divisible by 64') elif effective_upsample_enable: base_w = target_w base_h = target_h final_w = get_bucket(effective_upsample_ratio * target_w) final_h = get_bucket(effective_upsample_ratio * target_h) else: base_w = target_w base_h = target_h final_w = target_w final_h = target_h log.debug(f'LTX: resolution planning target={target_w}x{target_h} base={base_w}x{base_h} final={final_w}x{final_h} upsample={auto_refine_upsample}') videojob = shared.state.begin('Video', task_id=task_id) shared.state.job_count = 1 from modules.video_models import models_def, video_overrides selected = next((m for m in models_def.models.get(engine, []) if m.name == model), None) if caps.is_i2v and caps.repo_cls_name in ('LTXImageToVideoPipeline', 'LTX2ImageToVideoPipeline') and ltx_init_image is None: yield from abort('No input image provided. Please upload or select an image.', ok=True) return condition_images = [] if ltx_init_image is not None: condition_images.append(ltx_init_image) conditions = [] conditions_stage2 = [] if caps.supports_multi_condition: # Stage 1 conditions match base latent dims; Stage 2 rebuilds at final dims so frame # indices and spatial sizes survive the 2x upsample. Same source PIL/file refs feed # both calls; get_conditions handles the resize. conditions = get_conditions( base_w, base_h, condition_strength, condition_images, condition_files, condition_video, condition_video_frames, condition_video_skip, family=caps.family, num_frames=get_frames(frames), condition_last=condition_last, ) if (final_w, final_h) != (base_w, base_h): conditions_stage2 = get_conditions( final_w, final_h, condition_strength, condition_images, condition_files, condition_video, condition_video_frames, condition_video_skip, family=caps.family, num_frames=get_frames(frames), condition_last=condition_last, ) else: conditions_stage2 = conditions sampler_name = processing.get_sampler_name(sampler_index) sd_samplers.create_sampler(sampler_name, shared.sd_model) log.debug(f'Video: cls={shared.sd_model.__class__.__name__} op=init caps={caps.family} styles={styles} sampler={shared.sd_model.scheduler.__class__.__name__}') from modules.paths import resolve_output_path p = processing.StableDiffusionProcessingVideo( sd_model=shared.sd_model, video_engine=engine, video_model=model, prompt=prompt, negative_prompt=negative, styles=styles, seed=int(seed) if seed is not None else -1, sampler_name=sampler_name, sampler_shift=float(sampler_shift), steps=int(steps), width=base_w, height=base_h, frames=get_frames(frames), cfg_scale=float(guidance_scale) if guidance_scale is not None and guidance_scale > 0 else caps.default_cfg, denoising_strength=float(condition_strength) if condition_strength is not None else 1.0, init_image=ltx_init_image, vae_type='Default', vae_tile_frames=16, ) processing.fix_seed(p) p.scripts = None p.script_args = None p.do_not_save_grid = True p.do_not_save_samples = not mp4_frames p.outpath_samples = resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_video) p.ops.append('video') p.task_args['num_inference_steps'] = p.steps p.task_args['width'] = p.width p.task_args['height'] = p.height # force pil: 'latent' output triggers frame collapse in process_samples p.task_args['output_type'] = 'pil' if caps.supports_frame_rate_kwarg: p.task_args['frame_rate'] = float(mp4_fps) if caps.supports_image_cond_noise_scale and image_cond_noise_scale is not None: p.task_args['image_cond_noise_scale'] = image_cond_noise_scale if caps.supports_decode_timestep and decode_timestep is not None: p.task_args['decode_timestep'] = decode_timestep if caps.supports_multi_condition and conditions: p.task_args['conditions'] = conditions if caps.is_i2v and caps.repo_cls_name in ('LTXImageToVideoPipeline', 'LTX2ImageToVideoPipeline') and ltx_init_image is not None: from modules import images p.task_args['image'] = images.resize_image(resize_mode=2, im=ltx_init_image, width=p.width, height=p.height, upscaler_name=None, output_type='pil') if caps.family == '2.x' and caps.is_distilled: from diffusers.pipelines.ltx2.utils import DISTILLED_SIGMA_VALUES p.task_args['sigmas'] = list(DISTILLED_SIGMA_VALUES) p.task_args.pop('num_inference_steps', None) p.task_args.update(_canonical_ltx2_guidance(caps)) framewise = caps.family == '0.9' set_vae_params(p, framewise=framewise) # Scheduler + shared.opts mutation is wrapped in ltx_scheduler_opts so restore runs on # every exit path (normal return, abort, interrupt, Stage 2 scheduler swap). See the # helper's docstring for the five pieces of state it snapshots. with ltx_scheduler_opts(shared.sd_model, dynamic_shift=dynamic_shift, sampler_shift=sampler_shift): if selected is not None: video_overrides.set_overrides(p, selected) t0 = time.time() shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, silent=True) t1 = time.time() samplejob = shared.state.begin('Sample') yield None, 'LTX: Generate in progress...' audio = None pixels = None frames_out = None needs_latent_path = upsample_enable or refine_enable try: if needs_latent_path: prompt_final, negative_final, networks = get_prompts(prompt, negative, styles) extra_networks.activate(p, networks) # Encode once and reuse across stages; encode_prompt short-circuits when # embeds are passed to __call__. CPU park keeps them off GPU between stages. with devices.inference_context(): prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask = shared.sd_model.encode_prompt( prompt=prompt_final, negative_prompt=negative_final, do_classifier_free_guidance=True, device=devices.device, ) prompt_embeds = prompt_embeds.cpu() prompt_attention_mask = prompt_attention_mask.cpu() if prompt_attention_mask is not None else None negative_prompt_embeds = negative_prompt_embeds.cpu() if negative_prompt_embeds is not None else None negative_prompt_attention_mask = negative_prompt_attention_mask.cpu() if negative_prompt_attention_mask is not None else None # encode_prompt outside pipe.__call__ bypasses the post-forward offload hook; # re-anchor so the text encoder doesn't stay pinned through Stage 1 forward. shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, force=True, silent=True) devices.torch_gc(force=True, reason='ltx:encode') latents = _latent_pass( caps=caps, prompt_embeds=prompt_embeds, prompt_attention_mask=prompt_attention_mask, negative_prompt_embeds=negative_prompt_embeds, negative_prompt_attention_mask=negative_prompt_attention_mask, width=base_w, height=base_h, frames=frames, steps=steps, guidance_scale=p.cfg_scale, mp4_fps=mp4_fps, conditions=conditions, image_cond_noise_scale=image_cond_noise_scale if caps.supports_image_cond_noise_scale else None, seed=p.seed, image=p.task_args.get('image'), ) else: processed = processing.process_images(p) if processed is None or processed.images is None or len(processed.images) == 0: yield from abort('Video: process_images returned no frames', ok=True, p=p) return pixels = processed.images raw_audio = getattr(processed, 'audio', None) if raw_audio is not None: # Strip batch dim from (B, 2, N); write_audio expects (2, N) for the # transpose-to-interleaved path used by AAC s16. audio = raw_audio[0].float().cpu() if raw_audio.ndim == 3 else raw_audio.float().cpu() latents = None except AssertionError as e: yield from abort(e, ok=True, p=p) return except Exception as e: yield from abort(e, ok=False, p=p) return t2 = time.time() # silent=True everywhere in run_ltx: per-module stats were already dumped during the # load-time balanced_offload pass. Upsample/refine boundaries force a rebuild because # the global offload_hook_instance is keyed on checkpoint_name (sd_offload.py:488), # but re-logging the same inventory adds noise without information. shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, silent=True) devices.torch_gc(force=True, reason='ltx:base') t3 = time.time() timer.process.add('offload', t1 - t0) timer.process.add('base', t2 - t1) timer.process.add('offload', t3 - t2) shared.state.end(samplejob) if effective_upsample_enable and latents is not None: t4 = time.time() upsamplejob = shared.state.begin('Upsample') try: # Shared-VAE exclude: both upsample pipes receive shared.sd_model.vae as a # constructor formality (pure latent -> latent forward). The main pipe already # owns the VAE's hook lifecycle, so walking it again here hits meta tensors # from the prior offload pass. Excluding also shortens the walk to the one # module that actually belongs to this pipe: latent_upsampler. upsample_exclude = ['vae'] if caps.family == '0.9': global upsample_pipe # pylint: disable=global-statement upsample_pipe = load_upsample(upsample_pipe, upsample_repo_id_09) upsample_pipe = sd_models.apply_balanced_offload(upsample_pipe, exclude=upsample_exclude, silent=True) up_args = { 'width': final_w, 'height': final_h, 'generator': get_generator(p.seed), 'output_type': 'latent', } if latents.ndim == 4: latents = latents.unsqueeze(0) log.debug(f'Video: op=upsample family=0.9 latents={latents.shape} {up_args}') yield None, 'LTX: Upsample in progress...' latents = upsample_pipe(latents=latents, **up_args).frames[0] upsample_pipe = sd_models.apply_balanced_offload(upsample_pipe, exclude=upsample_exclude, silent=True) else: global upsample_pipe_2x # pylint: disable=global-statement upsample_repo = upsample_repo_id_23 if caps.variant == '2.3' else upsample_repo_id_20 upsample_pipe_2x = load_upsample_2x(upsample_pipe_2x, upsample_repo) upsample_pipe_2x = sd_models.apply_balanced_offload(upsample_pipe_2x, exclude=upsample_exclude, silent=True) # 2.x base pass returns denormalized latents; latents_normalized=False tells the # upsampler "already raw, do not denormalize again". up_args = { 'width': final_w, 'height': final_h, 'num_frames': get_frames(frames), 'latents_normalized': False, 'generator': get_generator(p.seed), 'output_type': 'latent', } if latents.ndim == 4: latents = latents.unsqueeze(0) log.debug(f'Video: op=upsample family=2.x latents={latents.shape} auto={auto_refine_upsample} {up_args}') yield None, 'LTX: Upsample in progress...' latents = upsample_pipe_2x(latents=latents, **up_args).frames[0] upsample_pipe_2x = sd_models.apply_balanced_offload(upsample_pipe_2x, exclude=upsample_exclude, silent=True) except AssertionError as e: yield from abort(e, ok=True, p=p) return except Exception as e: yield from abort(e, ok=False, p=p) return t5 = time.time() timer.process.add('upsample', t5 - t4) shared.state.end(upsamplejob) if refine_enable and latents is not None: t7 = time.time() refinejob = shared.state.begin('Refine') shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, silent=True) devices.torch_gc(force=True, reason='ltx:refine') # Refine is terminal: let the pipe decode internally so the final VAE pass runs inside # the same offload/cudnn context as a normal generation (matches Generic Video tab). refine_args = { 'prompt_embeds': prompt_embeds, 'prompt_attention_mask': prompt_attention_mask, 'negative_prompt_embeds': negative_prompt_embeds, 'negative_prompt_attention_mask': negative_prompt_attention_mask, 'width': final_w, 'height': final_h, 'num_frames': get_frames(frames), 'num_inference_steps': steps, 'generator': get_generator(p.seed), 'callback_on_step_end': diffusers_callback, 'output_type': 'pil', } if p.cfg_scale is not None and p.cfg_scale > -1: refine_args['guidance_scale'] = p.cfg_scale if caps.supports_frame_rate_kwarg: refine_args['frame_rate'] = float(mp4_fps) if caps.supports_image_cond_noise_scale and image_cond_noise_scale is not None: refine_args['image_cond_noise_scale'] = image_cond_noise_scale if caps.supports_multi_condition and conditions_stage2: refine_args['conditions'] = conditions_stage2 # Thread Stage-1 I2V init image through Stage 2 so first-frame identity survives refine. if caps.is_i2v and caps.repo_cls_name in ('LTXImageToVideoPipeline', 'LTX2ImageToVideoPipeline') and p.task_args.get('image') is not None: refine_args['image'] = p.task_args['image'] if caps.family == '2.x' and caps.use_cross_timestep: refine_args['use_cross_timestep'] = True # output_type='latent' skips the post-loop audio_vae + vocoder pass when audio # is unwanted; per-step audio cross-attention still runs for video conditioning. # Internal video decode is also skipped; vae_decode below picks it up. want_audio = caps.supports_audio and audio_enable if not want_audio: refine_args['output_type'] = 'latent' saved_scheduler_stage2 = None try: if caps.family == '2.x': # Stage 2 recipe (huggingface/diffusers#13217): fresh scheduler with shifting # disabled, 3 steps on STAGE_2_DISTILLED_SIGMA_VALUES, identity guidance. # Dev runs Distilled-on-Dev via the LoRA; Distilled is already at identity. from diffusers import FlowMatchEulerDiscreteScheduler saved_scheduler_stage2 = shared.sd_model.scheduler shared.sd_model.scheduler = FlowMatchEulerDiscreteScheduler.from_config( saved_scheduler_stage2.config, use_dynamic_shifting=False, shift_terminal=None, ) if caps.supports_canonical_stage2: log.debug(f'LTX: stage=2 distilled=LoRA repo={caps.stage2_dev_lora_repo}') offline_args = {'local_files_only': True} if shared.opts.offline_mode else {} shared.sd_model.load_lora_weights( caps.stage2_dev_lora_repo, adapter_name=STAGE2_DEV_LORA_ADAPTER, cache_dir=shared.opts.hfcache_dir, **offline_args, ) shared.sd_model.set_adapters([STAGE2_DEV_LORA_ADAPTER], [1.0]) else: log.debug('LTX: stage=2 distilled=native') # Identity kwargs override any guidance left from earlier in refine_args. refine_args.update(_canonical_stage2_kwargs()) refine_args.pop('num_inference_steps', None) elif caps.repo_cls_name == 'LTXConditionPipeline': refine_args['denoise_strength'] = refine_strength if latents.ndim == 4: latents = latents.unsqueeze(0) ( refine_args['prompt_embeds'], refine_args['prompt_attention_mask'], refine_args['negative_prompt_embeds'], refine_args['negative_prompt_attention_mask'], ) = _prompt_tensors_to_device( refine_args['prompt_embeds'], refine_args['prompt_attention_mask'], refine_args['negative_prompt_embeds'], refine_args['negative_prompt_attention_mask'], ) log.debug(f'Video: op=refine cls={caps.repo_cls_name} latents={latents.shape} canonical_stage2={caps.supports_canonical_stage2}') yield None, 'LTX: Refine in progress...' try: result = shared.sd_model(latents=latents, **refine_args) out = result.frames[0] if hasattr(result, 'frames') else None if want_audio: pixels = out if hasattr(result, 'audio') and result.audio is not None: audio = result.audio[0].float().cpu() latents = None else: latents = out except AssertionError as e: yield from abort(e, ok=True, p=p) return except Exception as e: yield from abort(e, ok=False, p=p) return finally: if saved_scheduler_stage2 is not None: if caps.supports_canonical_stage2: try: from modules.lora.extra_networks_lora import unload_diffusers unload_diffusers() except Exception as e: log.warning(f'LTX: stage=2 distilled=LoRA unload failed: {e}') shared.sd_model.scheduler = saved_scheduler_stage2 # log.debug('LTX: stage=2 cleanup done') t8 = time.time() shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, silent=True) t9 = time.time() timer.process.add('refine', t8 - t7) timer.process.add('offload', t9 - t8) shared.state.end(refinejob) if needs_latent_path: extra_networks.deactivate(p) if needs_latent_path and latents is not None: # Decode any path that leaves latents intact: upsample-without-refine, or # refine with output_type='latent' (audio_enable=False). shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, exclude=['vae'], force=True, silent=True) devices.torch_gc(force=True, reason='ltx:vae') yield None, 'LTX: VAE decode in progress...' try: if torch.is_tensor(latents): # 0.9.x returns raw latents with output_type='latent'; 2.x pre-denormalizes. frames_out = vae_decode(latents, decode_timestep if caps.supports_decode_timestep else 0.0, p.seed, denormalize=caps.family == '0.9') else: frames_out = latents except AssertionError as e: yield from abort(e, ok=True, p=p) return except Exception as e: yield from abort(e, ok=False, p=p) return pixels = frames_out t10 = time.time() shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, silent=True) t11 = time.time() timer.process.add('offload', t11 - t10) if not audio_enable: audio = None try: aac_sample_rate = shared.sd_model.vocoder.config.output_sampling_rate except Exception: aac_sample_rate = 24000 if mp4_interpolate > 0 and pixels is not None: p.video_interpolate = mp4_interpolate from modules.processing_video import apply_video_interpolation # refine path returns PIL list (output_type='pil'); decode path returns 5-D tensor if isinstance(pixels, list) and len(pixels) > 0 and isinstance(pixels[0], Image.Image): from modules.video_models.video_save import images_to_tensor pixels = images_to_tensor(pixels) # pixels is 5-D (N,C,T,H,W) in [-1,1]; RIFE needs 4-D (T,C,H,W) in [0,1] x = pixels.squeeze(0).permute(1, 0, 2, 3) x = (x.clamp(-1., 1.) + 1.0) * 0.5 x = apply_video_interpolation(p, x, count=mp4_interpolate) x = x * 2.0 - 1.0 pixels = x.permute(1, 0, 2, 3).unsqueeze(0) # LTX is conditioned on mp4_fps as the source rate; scale saved fps to keep duration constant from modules.processing_video import interpolation_factor save_fps = mp4_fps * interpolation_factor(p) num_frames, video_file, _thumb = save_video( p=p, pixels=pixels, audio=audio, mp4_fps=save_fps, mp4_codec=mp4_codec, mp4_opt=mp4_opt, mp4_ext=mp4_ext, mp4_sf=mp4_sf, mp4_video=mp4_video, mp4_frames=mp4_frames, mp4_thumb=mp4_thumb, mp4_interpolate=mp4_interpolate, aac_sample_rate=aac_sample_rate, metadata={}, ) t_end = time.time() if isinstance(pixels, list) and len(pixels) > 0 and isinstance(pixels[0], Image.Image): w, h = pixels[0].size elif hasattr(pixels, 'ndim') and pixels.ndim == 5: _n, _c, _t, h, w = pixels.shape elif hasattr(pixels, 'ndim') and pixels.ndim == 4: _n, h, w, _c = pixels.shape elif hasattr(pixels, 'shape'): h, w = pixels.shape[-2], pixels.shape[-1] else: w, h = p.width, p.height resolution = f'{w}x{h}' if num_frames > 0 else None summary = timer.process.summary(min_time=0.25, total=False).replace('=', ' ') memory = shared.mem_mon.summary() total_time = max(t_end - t0, 1e-6) fps = f'{num_frames/total_time:.2f}' its = f'{(steps)/total_time:.2f}' shared.state.end(videojob) progress.finish_task(task_id) p.close() 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()}') yield video_file, f'LTX: Generation completed | File {video_file} | Frames {num_frames} | Resolution {resolution} | f/s {fps} | it/s {its} ' + f"

{summary} {memory}

"