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https://github.com/comfyanonymous/ComfyUI.git
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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
116 lines
3.9 KiB
Python
116 lines
3.9 KiB
Python
# Code based on https://github.com/WikiChao/FreSca (MIT License)
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import torch
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import torch.fft as fft
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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, scale_low=1.0, scale_high=1.5, freq_cutoff=20):
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"""
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Apply frequency-dependent scaling to an image tensor using Fourier transforms.
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Parameters:
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x: Input tensor of shape (B, C, H, W)
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scale_low: Scaling factor for low-frequency components (default: 1.0)
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scale_high: Scaling factor for high-frequency components (default: 1.5)
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freq_cutoff: Number of frequency indices around center to consider as low-frequency (default: 20)
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Returns:
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x_filtered: Filtered version of x in spatial domain with frequency-specific scaling applied.
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"""
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# Preserve input dtype and device
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dtype, device = x.dtype, x.device
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# Convert to float32 for FFT computations
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x = x.to(torch.float32)
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# 1) Apply FFT and shift low frequencies to center
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x_freq = fft.fftn(x, dim=(-2, -1))
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x_freq = fft.fftshift(x_freq, dim=(-2, -1))
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# Initialize mask with high-frequency scaling factor
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mask = torch.ones(x_freq.shape, device=device) * scale_high
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m = mask
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for d in range(len(x_freq.shape) - 2):
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dim = d + 2
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cc = x_freq.shape[dim] // 2
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f_c = min(freq_cutoff, cc)
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m = m.narrow(dim, cc - f_c, f_c * 2)
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# Apply low-frequency scaling factor to center region
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m[:] = scale_low
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# 3) Apply frequency-specific scaling
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x_freq = x_freq * mask
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# 4) Convert back to spatial domain
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x_freq = fft.ifftshift(x_freq, dim=(-2, -1))
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x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real
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# 5) Restore original dtype
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x_filtered = x_filtered.to(dtype)
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return x_filtered
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class FreSca(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="FreSca",
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search_aliases=["frequency guidance"],
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display_name="FreSca",
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category="_for_testing",
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description="Applies frequency-dependent scaling to the guidance",
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inputs=[
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io.Model.Input("model"),
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io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01,
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tooltip="Scaling factor for low-frequency components", advanced=True),
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io.Float.Input("scale_high", default=1.25, min=0, max=10, step=0.01,
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tooltip="Scaling factor for high-frequency components", advanced=True),
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io.Int.Input("freq_cutoff", default=20, min=1, max=10000, step=1,
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tooltip="Number of frequency indices around center to consider as low-frequency", 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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is_experimental=True,
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)
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@classmethod
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def execute(cls, model, scale_low, scale_high, freq_cutoff):
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def custom_cfg_function(args):
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conds_out = args["conds_out"]
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if len(conds_out) <= 1 or None in args["conds"][:2]:
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return conds_out
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cond = conds_out[0]
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uncond = conds_out[1]
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guidance = cond - uncond
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filtered_guidance = Fourier_filter(
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guidance,
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scale_low=scale_low,
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scale_high=scale_high,
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freq_cutoff=freq_cutoff,
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)
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filtered_cond = filtered_guidance + uncond
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return [filtered_cond, uncond] + conds_out[2:]
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m = model.clone()
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m.set_model_sampler_pre_cfg_function(custom_cfg_function)
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return io.NodeOutput(m)
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class FreScaExtension(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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FreSca,
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]
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async def comfy_entrypoint() -> FreScaExtension:
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return FreScaExtension()
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