Merge 5905513e32 into c6238047ee

README.md

@@ -183,7 +183,7 @@ Simply download, extract with [7-Zip](https://7-zip.org) or with the windows exp
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
-Update your Nvidia drivers if it doesn't start.
+The portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start.
 
 #### Alternative Downloads:
 
@@ -212,7 +212,7 @@ Python 3.14 works but you may encounter issues with the torch compile node. The
 
 Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
 
-torch 2.4 and above is supported but some features might only work on newer versions. We generally recommend using the latest major version of pytorch unless it is less than 2 weeks old.
+torch 2.4 and above is supported but some features might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.
 
 ### Instructions:
 

comfy/ldm/lumina/model.py

@@ -119,6 +119,9 @@ class JointAttention(nn.Module):
             xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
         output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True, transformer_options=transformer_options)
 
+        if output.dtype == torch.float16:
+            output.div_(4)
+
         return self.out(output)
 
 
@@ -175,8 +178,12 @@ class FeedForward(nn.Module):
     def _forward_silu_gating(self, x1, x3):
         return clamp_fp16(F.silu(x1) * x3)
 
-    def forward(self, x):
-        return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
+    def forward(self, x, apply_fp16_downscale=False):
+        x3 = self.w3(x)
+        if x.dtype == torch.float16 and apply_fp16_downscale:
+            x3.div_(32)
+
+        return self.w2(self._forward_silu_gating(self.w1(x), x3))
 
 
 class JointTransformerBlock(nn.Module):
@@ -287,6 +294,7 @@ class JointTransformerBlock(nn.Module):
             x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
                 clamp_fp16(self.feed_forward(
                     modulate(self.ffn_norm1(x), scale_mlp),
+                    apply_fp16_downscale=True,
                 ))
             )
         else:

comfy_api_nodes/nodes_kling.py

@@ -567,7 +567,7 @@ async def execute_lipsync(
     # Upload the audio file to Comfy API and get download URL
     if audio:
         audio_url = await upload_audio_to_comfyapi(
-            cls, audio, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg", filename="output.mp3"
+            cls, audio, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg"
         )
         logging.info("Uploaded audio to Comfy API. URL: %s", audio_url)
     else:

comfy_api_nodes/util/conversions.py

@@ -55,7 +55,7 @@ def image_tensor_pair_to_batch(image1: torch.Tensor, image2: torch.Tensor) -> to
 
 def tensor_to_bytesio(
     image: torch.Tensor,
-    name: str | None = None,
+    *,
     total_pixels: int = 2048 * 2048,
     mime_type: str = "image/png",
 ) -> BytesIO:
@@ -75,7 +75,7 @@ def tensor_to_bytesio(
 
     pil_image = tensor_to_pil(image, total_pixels=total_pixels)
     img_binary = pil_to_bytesio(pil_image, mime_type=mime_type)
-    img_binary.name = f"{name if name else uuid.uuid4()}.{mimetype_to_extension(mime_type)}"
+    img_binary.name = f"{uuid.uuid4()}.{mimetype_to_extension(mime_type)}"
     return img_binary
 
 

comfy_api_nodes/util/upload_helpers.py

@@ -82,7 +82,6 @@ async def upload_audio_to_comfyapi(
     container_format: str = "mp4",
     codec_name: str = "aac",
     mime_type: str = "audio/mp4",
-    filename: str = "uploaded_audio.mp4",
 ) -> str:
     """
     Uploads a single audio input to ComfyUI API and returns its download URL.
@@ -92,7 +91,7 @@ async def upload_audio_to_comfyapi(
     waveform: torch.Tensor = audio["waveform"]
     audio_data_np = audio_tensor_to_contiguous_ndarray(waveform)
     audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, sample_rate, container_format, codec_name)
-    return await upload_file_to_comfyapi(cls, audio_bytes_io, filename, mime_type)
+    return await upload_file_to_comfyapi(cls, audio_bytes_io, f"{uuid.uuid4()}.{container_format}", mime_type)
 
 
 async def upload_video_to_comfyapi(