mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-02-03 02:00:29 +08:00
ae20354b69
Mark widgets as advanced across core, comfy_extras, and comfy_api_nodes to support the new collapsible advanced inputs section in the frontend. Changes: - 267 advanced markers in comfy_extras/ - 162 advanced markers in comfy_api_nodes/ - All files pass python3 -m py_compile verification Widgets marked advanced (hidden by default): - Scheduler internals: sigma_max, sigma_min, rho, mu, beta, alpha - Sampler internals: eta, s_noise, order, rtol, atol, h_init, pcoeff, etc. - Memory optimization: tile_size, overlap, temporal_size, temporal_overlap - Pipeline controls: add_noise, start_at_step, end_at_step - Timing controls: start_percent, end_percent - Layer selection: stop_at_clip_layer, layers, block_number - Video encoding: codec, crf, format - Device/dtype: device, noise_device, dtype, weight_dtype Widgets kept basic (always visible): - Core params: strength, steps, cfg, denoise, seed, width, height - Model selectors: ckpt_name, lora_name, vae_name, sampler_name - Common controls: upscale_method, crop, batch_size, fps, opacity Related: frontend PR #11939 Amp-Thread-ID: https://ampcode.com/threads/T-019c1734-6b61-702e-b333-f02c399963fc
139 lines
5.4 KiB
Python
139 lines
5.4 KiB
Python
#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License)
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import torch
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import logging
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, IO
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def Fourier_filter(x, threshold, scale):
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# FFT
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x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))
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x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1))
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B, C, H, W = x_freq.shape
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mask = torch.ones((B, C, H, W), device=x.device)
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crow, ccol = H // 2, W //2
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mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
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x_freq = x_freq * mask
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# IFFT
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x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1))
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x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real
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return x_filtered.to(x.dtype)
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class FreeU(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="FreeU",
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category="model_patches/unet",
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inputs=[
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IO.Model.Input("model"),
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IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01, advanced=True),
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IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01, advanced=True),
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IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
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IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
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],
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outputs=[
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IO.Model.Output(),
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],
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)
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@classmethod
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def execute(cls, model, b1, b2, s1, s2) -> IO.NodeOutput:
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model_channels = model.model.model_config.unet_config["model_channels"]
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scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
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on_cpu_devices = {}
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def output_block_patch(h, hsp, transformer_options):
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scale = scale_dict.get(int(h.shape[1]), None)
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if scale is not None:
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h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0]
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if hsp.device not in on_cpu_devices:
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try:
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hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
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except:
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logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
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on_cpu_devices[hsp.device] = True
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hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
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else:
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hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
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return h, hsp
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m = model.clone()
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m.set_model_output_block_patch(output_block_patch)
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return IO.NodeOutput(m)
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patch = execute # TODO: remove
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class FreeU_V2(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="FreeU_V2",
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category="model_patches/unet",
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inputs=[
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IO.Model.Input("model"),
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IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01, advanced=True),
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IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01, advanced=True),
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IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
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IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
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],
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outputs=[
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IO.Model.Output(),
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],
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)
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@classmethod
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def execute(cls, model, b1, b2, s1, s2) -> IO.NodeOutput:
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model_channels = model.model.model_config.unet_config["model_channels"]
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scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
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on_cpu_devices = {}
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def output_block_patch(h, hsp, transformer_options):
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scale = scale_dict.get(int(h.shape[1]), None)
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if scale is not None:
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hidden_mean = h.mean(1).unsqueeze(1)
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B = hidden_mean.shape[0]
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hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)
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hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)
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hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)
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h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1)
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if hsp.device not in on_cpu_devices:
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try:
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hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
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except:
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logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
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on_cpu_devices[hsp.device] = True
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hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
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else:
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hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
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return h, hsp
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m = model.clone()
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m.set_model_output_block_patch(output_block_patch)
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return IO.NodeOutput(m)
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patch = execute # TODO: remove
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class FreelunchExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[IO.ComfyNode]]:
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return [
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FreeU,
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FreeU_V2,
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]
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async def comfy_entrypoint() -> FreelunchExtension:
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return FreelunchExtension()
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