Ethanfel
e56ece9c1c
Learns K CLIP token embeddings ([K, 1024]) with all model weights frozen, keeping generated latents on the decoder's natural manifold — avoids the quality degradation that affects LoRA on BJ's audio dataset. - selva_textual_inversion_trainer.py: trains learned_tokens via AdamW, injects into last K positions of 77-token CLIP embedding, checkpoints with eval audio + spectral metrics - selva_textual_inversion_loader.py: loads .pt bundle, returns TEXTUAL_INVERSION dict for sampler - selva_sampler.py: optional textual_inversion input; injects into both text_clip and neg_text_clip before preprocess_conditions - __init__.py: registers both new nodes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
202 lines
10 KiB
Python
202 lines
10 KiB
Python
import torch
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import comfy.utils
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import comfy.model_management
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from .utils import SELVA_CATEGORY, get_device, get_offload_device, soft_empty_cache
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class SelvaSampler:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("SELVA_MODEL",),
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"features": ("SELVA_FEATURES",),
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"prompt": ("STRING", {
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"default": "", "multiline": True,
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"tooltip": "Sound description for CLIP text conditioning. Leave empty to reuse the prompt from the Feature Extractor (wire its prompt output here). Changing this without re-extracting features shifts CLIP conditioning but not sync features.",
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}),
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"negative_prompt": ("STRING", {
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"default": "", "multiline": False,
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"tooltip": "Sounds to suppress, e.g. 'speech, music, wind noise'. Steered away from via CFG. Leave empty for unconditional guidance baseline.",
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}),
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"duration": ("FLOAT", {
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"default": 0.0, "min": 0.0, "max": 30.0, "step": 0.1,
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"tooltip": "Output audio length in seconds. 0 = match the video duration stored in features.",
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}),
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"steps": ("INT", {"default": 25, "min": 1, "max": 200,
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"tooltip": "Euler steps for the flow matching ODE. 25 is the SelVA default. Diminishing returns above 50; below 10 may sound rough."}),
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"cfg_strength": ("FLOAT", {"default": 4.5, "min": 1.0, "max": 20.0, "step": 0.1,
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"tooltip": "Classifier-free guidance scale. Higher values follow the prompt more strictly but can introduce artifacts. SelVA default is 4.5; useful range is roughly 3–7."}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFF}),
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},
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"optional": {
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"normalize": ("BOOLEAN", {
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"default": True,
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"tooltip": "Normalize output level. Uses RMS normalization to target_lufs rather than peak normalization, so level matches typical audio content.",
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}),
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"target_lufs": ("FLOAT", {
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"default": -27.0, "min": -40.0, "max": -6.0, "step": 1.0,
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"tooltip": "Target RMS level in dBFS when normalize=True. -27 matches the measured RMS of LUFS-normalized training clips. Increase toward -20 for louder output.",
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}),
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"textual_inversion": ("TEXTUAL_INVERSION", {
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"tooltip": "Learned token embeddings from SelVA Textual Inversion Loader. "
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"Injects style tokens into CLIP conditioning without modifying model weights.",
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}),
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},
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}
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("audio",)
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OUTPUT_TOOLTIPS = ("Generated audio waveform — connect to VHS_VideoCombine or Save Audio.",)
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FUNCTION = "generate"
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CATEGORY = SELVA_CATEGORY
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DESCRIPTION = "Generates audio from video features using SelVA's flow matching ODE. Supports text prompts and negative prompts via classifier-free guidance."
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def generate(self, model, features, prompt, negative_prompt, duration, steps, cfg_strength, seed, normalize=True, target_lufs=-27.0, textual_inversion=None):
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import dataclasses
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from selva_core.model.flow_matching import FlowMatching
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device = get_device()
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dtype = model["dtype"]
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strategy = model["strategy"]
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net_generator = model["generator"]
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feature_utils = model["feature_utils"]
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# Validate that features were extracted with the same model variant
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feat_variant = features.get("variant")
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if feat_variant is not None and feat_variant != model["variant"]:
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raise ValueError(
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f"[SelVA] Variant mismatch: features were extracted with '{feat_variant}' "
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f"but model is '{model['variant']}'. Re-run the Feature Extractor with the current model."
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)
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# Resolve prompt: use override if given, otherwise fall back to features prompt
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if not prompt or not prompt.strip():
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prompt = features.get("prompt", "")
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if prompt:
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print(f"[SelVA] Using prompt from features: '{prompt[:60]}'", flush=True)
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else:
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print("[SelVA] Warning: no prompt in features or sampler — CLIP text conditioning will be empty.", flush=True)
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# Resolve duration
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if duration <= 0:
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if "duration" not in features:
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raise ValueError("[SelVA] duration=0 but features contain no duration field.")
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duration = features["duration"]
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print(f"[SelVA] Using video duration from features: {duration:.2f}s", flush=True)
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# Derive sequence config for this duration from the model's mode template
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seq_cfg = dataclasses.replace(model["seq_cfg"], duration=duration)
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sample_rate = seq_cfg.sampling_rate
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if strategy == "offload_to_cpu":
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net_generator.to(device)
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feature_utils.to(device)
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soft_empty_cache()
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try:
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clip_f = features["clip_features"].to(device, dtype) # [1, T_clip, 1024]
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sync_f = features["sync_features"].to(device, dtype) # [1, T_sync, 768]
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print(f"[SelVA] clip_f={tuple(clip_f.shape)} sync_f={tuple(sync_f.shape)}", flush=True)
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# Update model rotary position embeddings for actual feature shapes and duration.
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# Use actual feature dimensions (not seq_cfg) to avoid rounding assertion mismatches.
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net_generator.update_seq_lengths(
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latent_seq_len=seq_cfg.latent_seq_len,
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clip_seq_len=clip_f.shape[1],
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sync_seq_len=sync_f.shape[1],
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)
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print(f"[SelVA] seq: latent={seq_cfg.latent_seq_len} clip={clip_f.shape[1]} sync={sync_f.shape[1]}", flush=True)
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with torch.no_grad():
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# Encode text conditioning
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text_clip = feature_utils.encode_text_clip([prompt]) # [1, 77, D]
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# Encode negative prompt (or use empty conditions)
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neg_text_clip = feature_utils.encode_text_clip([negative_prompt]) \
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if negative_prompt.strip() else None
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# Inject textual inversion tokens into last K positions of CLIP embedding
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if textual_inversion is not None:
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emb = textual_inversion["embeddings"].to(device, dtype) # [K, 1024]
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K = emb.shape[0]
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text_clip = text_clip.clone()
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text_clip[:, -K:, :] = emb.unsqueeze(0)
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if neg_text_clip is not None:
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neg_text_clip = neg_text_clip.clone()
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neg_text_clip[:, -K:, :] = emb.unsqueeze(0)
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print(f"[SelVA] Textual inversion: injected {K} tokens into CLIP conditioning",
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flush=True)
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conditions = net_generator.preprocess_conditions(clip_f, sync_f, text_clip)
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empty_conditions = net_generator.get_empty_conditions(
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bs=1, negative_text_features=neg_text_clip
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)
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# Initial noise (MPS doesn't support torch.Generator on device)
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gen_device = "cpu" if device.type == "mps" else device
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rng = torch.Generator(device=gen_device).manual_seed(seed)
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x0 = torch.randn(
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1, seq_cfg.latent_seq_len, net_generator.latent_dim,
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device=gen_device, dtype=dtype, generator=rng,
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).to(device)
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# Flow matching ODE (Euler)
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fm = FlowMatching(min_sigma=0, inference_mode="euler", num_steps=steps)
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pbar = comfy.utils.ProgressBar(steps)
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def ode_wrapper_tracked(t, x):
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comfy.model_management.throw_exception_if_processing_interrupted()
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pbar.update(1)
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return net_generator.ode_wrapper(t, x, conditions, empty_conditions, cfg_strength)
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try:
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x1 = fm.to_data(ode_wrapper_tracked, x0)
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except torch.cuda.OutOfMemoryError:
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raise RuntimeError(
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"[SelVA] CUDA out of memory during generation. Try switching offload_strategy "
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"to 'offload_to_cpu', using a smaller variant, or reducing duration."
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)
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print(f"[SelVA] latent stats: mean={x1.float().mean():.4f} std={x1.float().std():.4f}", flush=True)
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# Decode: latent → mel → audio
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try:
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with torch.no_grad():
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x1_unnorm = net_generator.unnormalize(x1)
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spec = feature_utils.decode(x1_unnorm) # latent → mel spectrogram
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audio = feature_utils.vocode(spec) # mel → waveform
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except torch.cuda.OutOfMemoryError:
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raise RuntimeError(
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"[SelVA] CUDA out of memory during decode/vocode. Try switching offload_strategy "
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"to 'offload_to_cpu', using a smaller variant, or reducing duration."
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)
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finally:
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if strategy == "offload_to_cpu":
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net_generator.to(get_offload_device())
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feature_utils.to(get_offload_device())
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soft_empty_cache()
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# Ensure [1, 1, samples] and normalize to [-1,1]
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audio = audio.float()
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if audio.dim() == 2:
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audio = audio.unsqueeze(1)
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elif audio.dim() == 3 and audio.shape[1] != 1:
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audio = audio.mean(dim=1, keepdim=True) # stereo → mono
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if normalize:
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target_rms = 10 ** (target_lufs / 20.0)
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rms = audio.pow(2).mean().sqrt().clamp(min=1e-8)
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audio = audio * (target_rms / rms)
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# If RMS normalization pushes peaks into clipping, scale back to
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# preserve dynamics rather than hard-clipping (no saturation)
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peak = audio.abs().max().clamp(min=1e-8)
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if peak > 1.0:
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audio = audio / peak
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print(f"[SelVA] audio: shape={tuple(audio.shape)} sr={sample_rate}", flush=True)
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return ({"waveform": audio.cpu(), "sample_rate": sample_rate},)
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