Merge remote-tracking branch 'origin/master' into content-disposition

2025-12-17 10:02:59 +08:00 · 2025-07-23 18:47:39 +01:00 · 2025-07-23 18:47:39 +01:00 · 2d3a8d523d
commit 2d3a8d523d
parent 3e53e199ac 255f139863
10 changed files with 129 additions and 20 deletions
--- a/.ci/windows_base_files/README_VERY_IMPORTANT.txt
+++ b/.ci/windows_base_files/README_VERY_IMPORTANT.txt
@ -4,6 +4,9 @@ if you have a NVIDIA gpu:

 run_nvidia_gpu.bat

+if you want to enable the fast fp16 accumulation (faster for fp16 models with slightly less quality):
+
+run_nvidia_gpu_fast_fp16_accumulation.bat


 To run it in slow CPU mode:
--- a/.github/workflows/check-line-endings.yml
+++ b/.github/workflows/check-line-endings.yml
@ -17,6 +17,7 @@ jobs:
      - name: Check for Windows line endings (CRLF)
        run: |
          # Get the list of changed files in the PR
+          git merge origin/${{ github.base_ref }} --no-edit
          CHANGED_FILES=$(git diff --name-only origin/${{ github.base_ref }}..HEAD)

          # Flag to track if CRLF is found
--- a/README.md
+++ b/README.md
@ -69,6 +69,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
 - Image Editing Models
   - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
   - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
+   - [HiDream E1.1](https://comfyanonymous.github.io/ComfyUI_examples/hidream/#hidream-e11)
 - Video Models
   - [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
   - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@ -101,7 +101,7 @@ if args.directml is not None:
    lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.

 try:
-    import intel_extension_for_pytorch as ipex
+    import intel_extension_for_pytorch as ipex  # noqa: F401
    _ = torch.xpu.device_count()
    xpu_available = xpu_available or torch.xpu.is_available()
 except:
@ -186,8 +186,12 @@ def get_total_memory(dev=None, torch_total_too=False):
        elif is_intel_xpu():
            stats = torch.xpu.memory_stats(dev)
            mem_reserved = stats['reserved_bytes.all.current']
+            if torch_version_numeric < (2, 6):
+                mem_total_xpu = torch.xpu.get_device_properties(dev).total_memory
+            else:
+                _, mem_total_xpu = torch.xpu.mem_get_info(dev)
            mem_total_torch = mem_reserved
-            mem_total = torch.xpu.get_device_properties(dev).total_memory
+            mem_total = mem_total_xpu
        elif is_ascend_npu():
            stats = torch.npu.memory_stats(dev)
            mem_reserved = stats['reserved_bytes.all.current']
@ -929,7 +933,7 @@ def device_supports_non_blocking(device):
    if is_device_mps(device):
        return False #pytorch bug? mps doesn't support non blocking
    if is_intel_xpu():
-        return False
+        return True
    if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews)
        return False
    if directml_enabled:
@ -968,6 +972,8 @@ def get_offload_stream(device):
        stream_counter = (stream_counter + 1) % len(ss)
        if is_device_cuda(device):
            ss[stream_counter].wait_stream(torch.cuda.current_stream())
+        elif is_device_xpu(device):
+            ss[stream_counter].wait_stream(torch.xpu.current_stream())
        stream_counters[device] = stream_counter
        return s
    elif is_device_cuda(device):
@ -979,6 +985,15 @@ def get_offload_stream(device):
        stream_counter = (stream_counter + 1) % len(ss)
        stream_counters[device] = stream_counter
        return s
+    elif is_device_xpu(device):
+        ss = []
+        for k in range(NUM_STREAMS):
+            ss.append(torch.xpu.Stream(device=device, priority=0))
+        STREAMS[device] = ss
+        s = ss[stream_counter]
+        stream_counter = (stream_counter + 1) % len(ss)
+        stream_counters[device] = stream_counter
+        return s
    return None

 def sync_stream(device, stream):
@ -986,6 +1001,8 @@ def sync_stream(device, stream):
        return
    if is_device_cuda(device):
        torch.cuda.current_stream().wait_stream(stream)
+    elif is_device_xpu(device):
+        torch.xpu.current_stream().wait_stream(stream)

 def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None):
    if device is None or weight.device == device:
@ -1092,8 +1109,11 @@ def get_free_memory(dev=None, torch_free_too=False):
            stats = torch.xpu.memory_stats(dev)
            mem_active = stats['active_bytes.all.current']
            mem_reserved = stats['reserved_bytes.all.current']
+            if torch_version_numeric < (2, 6):
+                mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved
+            else:
+                mem_free_xpu, _ = torch.xpu.mem_get_info(dev)
            mem_free_torch = mem_reserved - mem_active
-            mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved
            mem_free_total = mem_free_xpu + mem_free_torch
        elif is_ascend_npu():
            stats = torch.npu.memory_stats(dev)
@ -1142,6 +1162,9 @@ def is_device_cpu(device):
 def is_device_mps(device):
    return is_device_type(device, 'mps')

+def is_device_xpu(device):
+    return is_device_type(device, 'xpu')
+
 def is_device_cuda(device):
    return is_device_type(device, 'cuda')

@ -1173,7 +1196,10 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
        return False

    if is_intel_xpu():
-        return True
+        if torch_version_numeric < (2, 3):
+            return True
+        else:
+            return torch.xpu.get_device_properties(device).has_fp16

    if is_ascend_npu():
        return True
@ -1236,7 +1262,10 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
        return False

    if is_intel_xpu():
-        return True
+        if torch_version_numeric < (2, 6):
+            return True
+        else:
+            return torch.xpu.get_device_capability(device)['has_bfloat16_conversions']

    if is_ascend_npu():
        return True
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@ -278,6 +278,42 @@ class PreviewAudio(SaveAudio):
                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                }

+def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
+    """Convert audio to float 32 bits PCM format."""
+    if wav.dtype.is_floating_point:
+        return wav
+    elif wav.dtype == torch.int16:
+        return wav.float() / (2 ** 15)
+    elif wav.dtype == torch.int32:
+        return wav.float() / (2 ** 31)
+    raise ValueError(f"Unsupported wav dtype: {wav.dtype}")
+
+def load(filepath: str) -> tuple[torch.Tensor, int]:
+    with av.open(filepath) as af:
+        if not af.streams.audio:
+            raise ValueError("No audio stream found in the file.")
+
+        stream = af.streams.audio[0]
+        sr = stream.codec_context.sample_rate
+        n_channels = stream.channels
+
+        frames = []
+        length = 0
+        for frame in af.decode(streams=stream.index):
+            buf = torch.from_numpy(frame.to_ndarray())
+            if buf.shape[0] != n_channels:
+                buf = buf.view(-1, n_channels).t()
+
+            frames.append(buf)
+            length += buf.shape[1]
+
+        if not frames:
+            raise ValueError("No audio frames decoded.")
+
+        wav = torch.cat(frames, dim=1)
+        wav = f32_pcm(wav)
+        return wav, sr
+
 class LoadAudio:
    @classmethod
    def INPUT_TYPES(s):
@ -292,7 +328,7 @@ class LoadAudio:

    def load(self, audio):
        audio_path = folder_paths.get_annotated_filepath(audio)
-        waveform, sample_rate = torchaudio.load(audio_path)
+        waveform, sample_rate = load(audio_path)
        audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
        return (audio, )

--- a/comfy_extras/nodes_cfg.py
+++ b/comfy_extras/nodes_cfg.py
@ -50,6 +50,7 @@ class CFGNorm:
    RETURN_NAMES = ("patched_model",)
    FUNCTION = "patch"
    CATEGORY = "advanced/guidance"
+    EXPERIMENTAL = True

    def patch(self, model, strength):
        m = model.clone()
--- a/comfy_extras/nodes_custom_sampler.py
+++ b/comfy_extras/nodes_custom_sampler.py
@ -301,6 +301,35 @@ class ExtendIntermediateSigmas:

        return (extended_sigmas,)

+
+class SamplingPercentToSigma:
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "model": (IO.MODEL, {}),
+                "sampling_percent": (IO.FLOAT, {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.0001}),
+                "return_actual_sigma": (IO.BOOLEAN, {"default": False, "tooltip": "Return the actual sigma value instead of the value used for interval checks.\nThis only affects results at 0.0 and 1.0."}),
+            }
+        }
+
+    RETURN_TYPES = (IO.FLOAT,)
+    RETURN_NAMES = ("sigma_value",)
+    CATEGORY = "sampling/custom_sampling/sigmas"
+
+    FUNCTION = "get_sigma"
+
+    def get_sigma(self, model, sampling_percent, return_actual_sigma):
+        model_sampling = model.get_model_object("model_sampling")
+        sigma_val = model_sampling.percent_to_sigma(sampling_percent)
+        if return_actual_sigma:
+            if sampling_percent == 0.0:
+                sigma_val = model_sampling.sigma_max.item()
+            elif sampling_percent == 1.0:
+                sigma_val = model_sampling.sigma_min.item()
+        return (sigma_val,)
+
+
 class KSamplerSelect:
    @classmethod
    def INPUT_TYPES(s):
@ -683,9 +712,10 @@ class CFGGuider:
        return (guider,)

 class Guider_DualCFG(comfy.samplers.CFGGuider):
-    def set_cfg(self, cfg1, cfg2):
+    def set_cfg(self, cfg1, cfg2, nested=False):
        self.cfg1 = cfg1
        self.cfg2 = cfg2
+        self.nested = nested

    def set_conds(self, positive, middle, negative):
        middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"})
@ -695,14 +725,20 @@ class Guider_DualCFG(comfy.samplers.CFGGuider):
        negative_cond = self.conds.get("negative", None)
        middle_cond = self.conds.get("middle", None)
        positive_cond = self.conds.get("positive", None)
-        if model_options.get("disable_cfg1_optimization", False) == False:
-            if math.isclose(self.cfg2, 1.0):
-                negative_cond = None
-                if math.isclose(self.cfg1, 1.0):
-                    middle_cond = None

-        out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options)
-        return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1
+        if self.nested:
+            out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options)
+            pred_text = comfy.samplers.cfg_function(self.inner_model, out[2], out[1], self.cfg1, x, timestep, model_options=model_options, cond=positive_cond, uncond=middle_cond)
+            return out[0] + self.cfg2 * (pred_text - out[0])
+        else:
+            if model_options.get("disable_cfg1_optimization", False) == False:
+                if math.isclose(self.cfg2, 1.0):
+                    negative_cond = None
+                    if math.isclose(self.cfg1, 1.0):
+                        middle_cond = None
+
+            out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options)
+            return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1

 class DualCFGGuider:
    @classmethod
@ -714,6 +750,7 @@ class DualCFGGuider:
                    "negative": ("CONDITIONING", ),
                    "cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
                    "cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+                    "style": (["regular", "nested"],),
                     }
                }

@ -722,10 +759,10 @@ class DualCFGGuider:
    FUNCTION = "get_guider"
    CATEGORY = "sampling/custom_sampling/guiders"

-    def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative):
+    def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative, style):
        guider = Guider_DualCFG(model)
        guider.set_conds(cond1, cond2, negative)
-        guider.set_cfg(cfg_conds, cfg_cond2_negative)
+        guider.set_cfg(cfg_conds, cfg_cond2_negative, nested=(style == "nested"))
        return (guider,)

 class DisableNoise:
@ -879,6 +916,7 @@ NODE_CLASS_MAPPINGS = {
    "FlipSigmas": FlipSigmas,
    "SetFirstSigma": SetFirstSigma,
    "ExtendIntermediateSigmas": ExtendIntermediateSigmas,
+    "SamplingPercentToSigma": SamplingPercentToSigma,

    "CFGGuider": CFGGuider,
    "DualCFGGuider": DualCFGGuider,
--- a/comfyui_version.py
+++ b/comfyui_version.py
@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.44"
+__version__ = "0.3.45"
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.44"
+version = "0.3.45"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"
--- a/requirements.txt
+++ b/requirements.txt
@ -1,5 +1,5 @@
 comfyui-frontend-package==1.23.4
-comfyui-workflow-templates==0.1.36
+comfyui-workflow-templates==0.1.39
 comfyui-embedded-docs==0.2.4
 torch
 torchsde