[pull] master from Comfy-Org:master

comfy/ldm/ace/ace_step15.py

@@ -183,7 +183,7 @@ def forward(
             else:
                 attn_bias = window_bias
 
-        attn_output = optimized_attention(query_states, key_states, value_states, self.num_heads, attn_bias, skip_reshape=True)
+        attn_output = optimized_attention(query_states, key_states, value_states, self.num_heads, attn_bias, skip_reshape=True, low_precision_attention=False)
         attn_output = self.o_proj(attn_output)
 
         return attn_output
@@ -1035,8 +1035,7 @@ def prepare_condition(
                     audio_codes = torch.nn.functional.pad(audio_codes, (0, math.ceil(src_latents.shape[1] / 5) - audio_codes.shape[1]), "constant", 35847)
                 lm_hints_5Hz = self.tokenizer.quantizer.get_output_from_indices(audio_codes, dtype=text_hidden_states.dtype)
             else:
-                assert False
-                # TODO ?
+                lm_hints_5Hz, indices = self.tokenizer.tokenize(refer_audio_acoustic_hidden_states_packed)
 
             lm_hints = self.detokenizer(lm_hints_5Hz)
 

comfy/ldm/modules/attention.py

@@ -524,6 +524,9 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
 
 @wrap_attn
 def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if kwargs.get("low_precision_attention", True) is False:
+        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs)
+
     exception_fallback = False
     if skip_reshape:
         b, _, _, dim_head = q.shape

comfy/model_base.py

@@ -1548,6 +1548,7 @@ def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
         device = kwargs["device"]
+        noise = kwargs["noise"]
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
@@ -1571,15 +1572,19 @@ def extra_conds(self, **kwargs):
                                         1.4844e-01,  9.4727e-02,  3.8477e-01, -1.2578e+00, -3.3203e-01,
                                         -8.5547e-01,  4.3359e-01,  4.2383e-01, -8.9453e-01, -5.0391e-01,
                                         -5.6152e-02, -2.9219e+00, -2.4658e-02,  5.0391e-01,  9.8438e-01,
-                                        7.2754e-02, -2.1582e-01,  6.3672e-01,  1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, 750)
+                                        7.2754e-02, -2.1582e-01,  6.3672e-01,  1.0000e+00]]], device=device).movedim(-1, 1).repeat(1, 1, noise.shape[2])
+            pass_audio_codes = True
         else:
-            refer_audio = refer_audio[-1]
-        out['refer_audio'] = comfy.conds.CONDRegular(refer_audio)
+            refer_audio = refer_audio[-1][:, :, :noise.shape[2]]
+            pass_audio_codes = False
 
-        audio_codes = kwargs.get("audio_codes", None)
-        if audio_codes is not None:
-            out['audio_codes'] = comfy.conds.CONDRegular(torch.tensor(audio_codes, device=device))
+        if pass_audio_codes:
+            audio_codes = kwargs.get("audio_codes", None)
+            if audio_codes is not None:
+                out['audio_codes'] = comfy.conds.CONDRegular(torch.tensor(audio_codes, device=device))
+                refer_audio = refer_audio[:, :, :750]
 
+        out['refer_audio'] = comfy.conds.CONDRegular(refer_audio)
         return out
 
 class Omnigen2(BaseModel):

comfy/ops.py

@@ -54,6 +54,8 @@ def scaled_dot_product_attention(q, k, v, *args, **kwargs):
             SDPA_BACKEND_PRIORITY.insert(0, SDPBackend.CUDNN_ATTENTION)
 
             def scaled_dot_product_attention(q, k, v, *args, **kwargs):
+                if q.nelement() < 1024 * 128:  # arbitrary number, for small inputs cudnn attention seems slower
+                    return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
                 with sdpa_kernel(SDPA_BACKEND_PRIORITY, set_priority=True):
                     return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
         else:

comfy/text_encoders/ace15.py

@@ -101,9 +101,7 @@ def sample_manual_loop_no_classes(
     return output_audio_codes
 
 
-def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=1024, seed=0):
-    cfg_scale = 2.0
-
+def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=1024, seed=0, cfg_scale=2.0, temperature=0.85, top_p=0.9, top_k=0):
     positive = [[token for token, _ in inner_list] for inner_list in positive]
     negative = [[token for token, _ in inner_list] for inner_list in negative]
     positive = positive[0]
@@ -120,7 +118,7 @@ def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=102
         positive = [model.special_tokens["pad"]] * pos_pad + positive
 
     paddings = [pos_pad, neg_pad]
-    return sample_manual_loop_no_classes(model, [positive, negative], paddings, cfg_scale=cfg_scale, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)
+    return sample_manual_loop_no_classes(model, [positive, negative], paddings, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)
 
 
 class ACE15Tokenizer(sd1_clip.SD1Tokenizer):
@@ -137,6 +135,12 @@ def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
         language = kwargs.get("language", "en")
         seed = kwargs.get("seed", 0)
 
+        generate_audio_codes = kwargs.get("generate_audio_codes", True)
+        cfg_scale = kwargs.get("cfg_scale", 2.0)
+        temperature = kwargs.get("temperature", 0.85)
+        top_p = kwargs.get("top_p", 0.9)
+        top_k = kwargs.get("top_k", 0.0)
+
         duration = math.ceil(duration)
         meta_lm = 'bpm: {}\nduration: {}\nkeyscale: {}\ntimesignature: {}'.format(bpm, duration, keyscale, timesignature)
         lm_template = "<|im_start|>system\n# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n<|im_end|>\n<|im_start|>user\n# Caption\n{}\n{}\n<|im_end|>\n<|im_start|>assistant\n<think>\n{}\n</think>\n\n<|im_end|>\n"
@@ -147,7 +151,14 @@ def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
 
         out["lyrics"] = self.qwen3_06b.tokenize_with_weights("# Languages\n{}\n\n# Lyric{}<|endoftext|><|endoftext|>".format(language, lyrics), return_word_ids, disable_weights=True, **kwargs)
         out["qwen3_06b"] = self.qwen3_06b.tokenize_with_weights("# Instruction\nGenerate audio semantic tokens based on the given conditions:\n\n# Caption\n{}# Metas\n{}<|endoftext|>\n<|endoftext|>".format(text, meta_cap), return_word_ids, **kwargs)
-        out["lm_metadata"] = {"min_tokens": duration * 5, "seed": seed}
+        out["lm_metadata"] = {"min_tokens": duration * 5,
+                              "seed": seed,
+                              "generate_audio_codes": generate_audio_codes,
+                              "cfg_scale": cfg_scale,
+                              "temperature": temperature,
+                              "top_p": top_p,
+                              "top_k": top_k,
+                              }
         return out
 
 
@@ -203,10 +214,14 @@ def encode_token_weights(self, token_weight_pairs):
         self.qwen3_06b.set_clip_options({"layer": [0]})
         lyrics_embeds, _, extra_l = self.qwen3_06b.encode_token_weights(token_weight_pairs_lyrics)
 
+        out = {"conditioning_lyrics": lyrics_embeds[:, 0]}
+
         lm_metadata = token_weight_pairs["lm_metadata"]
-        audio_codes = generate_audio_codes(getattr(self, self.lm_model, self.qwen3_06b), token_weight_pairs["lm_prompt"], token_weight_pairs["lm_prompt_negative"], min_tokens=lm_metadata["min_tokens"], max_tokens=lm_metadata["min_tokens"], seed=lm_metadata["seed"])
+        if lm_metadata["generate_audio_codes"]:
+            audio_codes = generate_audio_codes(getattr(self, self.lm_model, self.qwen3_06b), token_weight_pairs["lm_prompt"], token_weight_pairs["lm_prompt_negative"], min_tokens=lm_metadata["min_tokens"], max_tokens=lm_metadata["min_tokens"], seed=lm_metadata["seed"], cfg_scale=lm_metadata["cfg_scale"], temperature=lm_metadata["temperature"], top_p=lm_metadata["top_p"], top_k=lm_metadata["top_k"])
+            out["audio_codes"] = [audio_codes]
 
-        return base_out, None, {"conditioning_lyrics": lyrics_embeds[:, 0], "audio_codes": [audio_codes]}
+        return base_out, None, out
 
     def set_clip_options(self, options):
         self.qwen3_06b.set_clip_options(options)

comfy/text_encoders/llama.py

@@ -651,10 +651,10 @@ def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermed
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, seq_len, attention_mask.shape[-1])
-            mask = mask.masked_fill(mask.to(torch.bool), torch.finfo(x.dtype).min)
+            mask = mask.masked_fill(mask.to(torch.bool), torch.finfo(x.dtype).min / 4)
 
         if seq_len > 1:
-            causal_mask = torch.empty(past_len + seq_len, past_len + seq_len, dtype=x.dtype, device=x.device).fill_(torch.finfo(x.dtype).min).triu_(1)
+            causal_mask = torch.empty(past_len + seq_len, past_len + seq_len, dtype=x.dtype, device=x.device).fill_(torch.finfo(x.dtype).min / 4).triu_(1)
             if mask is not None:
                 mask += causal_mask
             else:

comfy_extras/nodes_ace.py

@@ -44,13 +44,18 @@ def define_schema(cls):
                 io.Combo.Input("timesignature", options=['2', '3', '4', '6']),
                 io.Combo.Input("language", options=["en", "ja", "zh", "es", "de", "fr", "pt", "ru", "it", "nl", "pl", "tr", "vi", "cs", "fa", "id", "ko", "uk", "hu", "ar", "sv", "ro", "el"]),
                 io.Combo.Input("keyscale", options=[f"{root} {quality}" for quality in ["major", "minor"] for root in ["C", "C#", "Db", "D", "D#", "Eb", "E", "F", "F#", "Gb", "G", "G#", "Ab", "A", "A#", "Bb", "B"]]),
+                io.Boolean.Input("generate_audio_codes", default=True, tooltip="Enable the LLM that generates audio codes. This can be slow but will increase the quality of the generated audio. Turn this off if you are giving the model an audio reference.", advanced=True),
+                io.Float.Input("cfg_scale", default=2.0, min=0.0, max=100.0, step=0.1, advanced=True),
+                io.Float.Input("temperature", default=0.85, min=0.0, max=2.0, step=0.01, advanced=True),
+                io.Float.Input("top_p", default=0.9, min=0.0, max=2000.0, step=0.01, advanced=True),
+                io.Int.Input("top_k", default=0, min=0, max=100, advanced=True),
             ],
             outputs=[io.Conditioning.Output()],
         )
 
     @classmethod
-    def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale) -> io.NodeOutput:
-        tokens = clip.tokenize(tags, lyrics=lyrics, bpm=bpm, duration=duration, timesignature=int(timesignature), language=language, keyscale=keyscale, seed=seed)
+    def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale, generate_audio_codes, cfg_scale, temperature, top_p, top_k) -> io.NodeOutput:
+        tokens = clip.tokenize(tags, lyrics=lyrics, bpm=bpm, duration=duration, timesignature=int(timesignature), language=language, keyscale=keyscale, seed=seed, generate_audio_codes=generate_audio_codes, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k)
         conditioning = clip.encode_from_tokens_scheduled(tokens)
         return io.NodeOutput(conditioning)
 
@@ -100,14 +105,15 @@ def execute(cls, seconds, batch_size) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
         return io.NodeOutput({"samples": latent, "type": "audio"})
 
-class ReferenceTimbreAudio(io.ComfyNode):
+class ReferenceAudio(io.ComfyNode):
     @classmethod
     def define_schema(cls):
         return io.Schema(
             node_id="ReferenceTimbreAudio",
+            display_name="Reference Audio",
             category="advanced/conditioning/audio",
             is_experimental=True,
-            description="This node sets the reference audio for timbre (for ace step 1.5)",
+            description="This node sets the reference audio for ace step 1.5",
             inputs=[
                 io.Conditioning.Input("conditioning"),
                 io.Latent.Input("latent", optional=True),
@@ -131,7 +137,7 @@ async def get_node_list(self) -> list[type[io.ComfyNode]]:
             EmptyAceStepLatentAudio,
             TextEncodeAceStepAudio15,
             EmptyAceStep15LatentAudio,
-            ReferenceTimbreAudio,
+            ReferenceAudio,
         ]
 
 async def comfy_entrypoint() -> AceExtension: