Ethanfel/ComfyUI-SelVA
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feat(sampler): add ti_strength blend for TI injection

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TI via text conditioning produces buzz because SelVA's text path is
mean-pooled into a global DiT bias — not rich per-token cross-attention
like SD. The optimizer learns a constant spectral artifact rather than
semantic style shift.

ti_strength=1.0 (default) = full injection as before.
ti_strength<1.0 = lerp between original and injected text_clip,
allowing the effect to be dialled back without retraining.
Applies to both text_clip and neg_text_clip symmetrically.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Ethanfel
2026-04-09 00:07:57 +02:00
parent b89167cfae
commit 28ee3db337
1 changed files with 11 additions and 4 deletions
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nodes/selva_sampler.py
+11 -4
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@@ -44,6 +44,11 @@ class SelvaSampler:
"tooltip": "Learned token embeddings from SelVA Textual Inversion Loader. " "tooltip": "Learned token embeddings from SelVA Textual Inversion Loader. "
"Injects style tokens into CLIP conditioning without modifying model weights.", "Injects style tokens into CLIP conditioning without modifying model weights.",
}), }),
"ti_strength": ("FLOAT", {
"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05,
"tooltip": "Blends between original CLIP conditioning (0.0) and full TI injection (1.0). "
"Reduce toward 0.3–0.5 if TI produces buzz artifacts.",
}),
}, },
} }
@@ -54,7 +59,7 @@ class SelvaSampler:
CATEGORY = SELVA_CATEGORY CATEGORY = SELVA_CATEGORY
DESCRIPTION = "Generates audio from video features using SelVA's flow matching ODE. Supports text prompts and negative prompts via classifier-free guidance." DESCRIPTION = "Generates audio from video features using SelVA's flow matching ODE. Supports text prompts and negative prompts via classifier-free guidance."
def generate(self, model, features, prompt, negative_prompt, duration, steps, cfg_strength, seed, normalize=True, target_lufs=-27.0, textual_inversion=None): def generate(self, model, features, prompt, negative_prompt, duration, steps, cfg_strength, seed, normalize=True, target_lufs=-27.0, textual_inversion=None, ti_strength=1.0):
import dataclasses import dataclasses
from selva_core.model.flow_matching import FlowMatching from selva_core.model.flow_matching import FlowMatching
@@ -124,10 +129,12 @@ class SelvaSampler:
emb = textual_inversion["embeddings"].to(device, dtype) # [K, 1024] emb = textual_inversion["embeddings"].to(device, dtype) # [K, 1024]
K = emb.shape[0] K = emb.shape[0]
inject_mode = textual_inversion.get("inject_mode", "suffix") inject_mode = textual_inversion.get("inject_mode", "suffix")
text_clip = _inject_tokens(text_clip, emb, K, inject_mode) ti_text = _inject_tokens(text_clip, emb, K, inject_mode)
text_clip = torch.lerp(text_clip, ti_text, ti_strength)
if neg_text_clip is not None: if neg_text_clip is not None:
neg_text_clip = _inject_tokens(neg_text_clip, emb, K, inject_mode) ti_neg = _inject_tokens(neg_text_clip, emb, K, inject_mode)
print(f"[SelVA] Textual inversion: {K} tokens mode={inject_mode}", neg_text_clip = torch.lerp(neg_text_clip, ti_neg, ti_strength)
print(f"[SelVA] Textual inversion: {K} tokens mode={inject_mode} strength={ti_strength}",
flush=True) flush=True)
conditions = net_generator.preprocess_conditions(clip_f, sync_f, text_clip) conditions = net_generator.preprocess_conditions(clip_f, sync_f, text_clip)