diff --git a/comfy_extras/v3/nodes_cfg.py b/comfy_extras/v3/nodes_cfg.py index 5c06c271a..40957fe93 100644 --- a/comfy_extras/v3/nodes_cfg.py +++ b/comfy_extras/v3/nodes_cfg.py @@ -32,6 +32,7 @@ class CFGNorm(io.ComfyNodeV3): io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01), ], outputs=[io.Model.Output("patched_model", display_name="patched_model")], + is_experimental=True, ) @classmethod diff --git a/comfy_extras/v3/nodes_flux.py b/comfy_extras/v3/nodes_flux.py new file mode 100644 index 000000000..165cf8384 --- /dev/null +++ b/comfy_extras/v3/nodes_flux.py @@ -0,0 +1,125 @@ +from __future__ import annotations + +import comfy.utils +import node_helpers +from comfy_api.v3 import io + +PREFERED_KONTEXT_RESOLUTIONS = [ + (672, 1568), + (688, 1504), + (720, 1456), + (752, 1392), + (800, 1328), + (832, 1248), + (880, 1184), + (944, 1104), + (1024, 1024), + (1104, 944), + (1184, 880), + (1248, 832), + (1328, 800), + (1392, 752), + (1456, 720), + (1504, 688), + (1568, 672), +] + + +class CLIPTextEncodeFlux(io.ComfyNodeV3): + @classmethod + def define_schema(cls): + return io.SchemaV3( + node_id="CLIPTextEncodeFlux_V3", + category="advanced/conditioning/flux", + inputs=[ + io.Clip.Input(id="clip"), + io.String.Input(id="clip_l", multiline=True, dynamic_prompts=True), + io.String.Input(id="t5xxl", multiline=True, dynamic_prompts=True), + io.Float.Input(id="guidance", default=3.5, min=0.0, max=100.0, step=0.1), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) + + @classmethod + def execute(cls, clip, clip_l, t5xxl, guidance): + tokens = clip.tokenize(clip_l) + tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] + + return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance})) + + +class FluxDisableGuidance(io.ComfyNodeV3): + @classmethod + def define_schema(cls): + return io.SchemaV3( + node_id="FluxDisableGuidance_V3", + category="advanced/conditioning/flux", + description="This node completely disables the guidance embed on Flux and Flux like models", + inputs=[ + io.Conditioning.Input(id="conditioning"), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) + + @classmethod + def execute(cls, conditioning): + c = node_helpers.conditioning_set_values(conditioning, {"guidance": None}) + return io.NodeOutput(c) + + +class FluxGuidance(io.ComfyNodeV3): + @classmethod + def define_schema(cls): + return io.SchemaV3( + node_id="FluxGuidance_V3", + category="advanced/conditioning/flux", + inputs=[ + io.Conditioning.Input(id="conditioning"), + io.Float.Input(id="guidance", default=3.5, min=0.0, max=100.0, step=0.1), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) + + @classmethod + def execute(cls, conditioning, guidance): + c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance}) + return io.NodeOutput(c) + + +class FluxKontextImageScale(io.ComfyNodeV3): + @classmethod + def define_schema(cls): + return io.SchemaV3( + node_id="FluxKontextImageScale_V3", + category="advanced/conditioning/flux", + description="This node resizes the image to one that is more optimal for flux kontext.", + inputs=[ + io.Image.Input(id="image"), + ], + outputs=[ + io.Image.Output(), + ], + ) + + @classmethod + def execute(cls, image): + width = image.shape[2] + height = image.shape[1] + aspect_ratio = width / height + _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS) + image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1) + return io.NodeOutput(image) + + +NODES_LIST = [ + CLIPTextEncodeFlux, + FluxDisableGuidance, + FluxGuidance, + FluxKontextImageScale, +] diff --git a/comfy_extras/v3/nodes_freelunch.py b/comfy_extras/v3/nodes_freelunch.py new file mode 100644 index 000000000..9e856ec1e --- /dev/null +++ b/comfy_extras/v3/nodes_freelunch.py @@ -0,0 +1,131 @@ +#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License) + +from __future__ import annotations + +import logging + +import torch + +from comfy_api.v3 import io + + +def Fourier_filter(x, threshold, scale): + # FFT + x_freq = torch.fft.fftn(x.float(), dim=(-2, -1)) + x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1)) + + B, C, H, W = x_freq.shape + mask = torch.ones((B, C, H, W), device=x.device) + + crow, ccol = H // 2, W //2 + mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale + x_freq = x_freq * mask + + # IFFT + x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1)) + x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real + + return x_filtered.to(x.dtype) + + +class FreeU(io.ComfyNodeV3): + @classmethod + def define_schema(cls): + return io.SchemaV3( + node_id="FreeU_V3", + category="model_patches/unet", + inputs=[ + io.Model.Input(id="model"), + io.Float.Input(id="b1", default=1.1, min=0.0, max=10.0, step=0.01), + io.Float.Input(id="b2", default=1.2, min=0.0, max=10.0, step=0.01), + io.Float.Input(id="s1", default=0.9, min=0.0, max=10.0, step=0.01), + io.Float.Input(id="s2", default=0.2, min=0.0, max=10.0, step=0.01), + ], + outputs=[ + io.Model.Output(), + ], + ) + + @classmethod + def execute(cls, model, b1, b2, s1, s2): + model_channels = model.model.model_config.unet_config["model_channels"] + scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} + on_cpu_devices = {} + + def output_block_patch(h, hsp, transformer_options): + scale = scale_dict.get(int(h.shape[1]), None) + if scale is not None: + h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0] + if hsp.device not in on_cpu_devices: + try: + hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) + except Exception: + logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) + on_cpu_devices[hsp.device] = True + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + else: + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + + return h, hsp + + m = model.clone() + m.set_model_output_block_patch(output_block_patch) + return io.NodeOutput(m) + + +class FreeU_V2(io.ComfyNodeV3): + @classmethod + def define_schema(cls): + return io.SchemaV3( + node_id="FreeU_V2_V3", + category="model_patches/unet", + inputs=[ + io.Model.Input(id="model"), + io.Float.Input(id="b1", default=1.3, min=0.0, max=10.0, step=0.01), + io.Float.Input(id="b2", default=1.4, min=0.0, max=10.0, step=0.01), + io.Float.Input(id="s1", default=0.9, min=0.0, max=10.0, step=0.01), + io.Float.Input(id="s2", default=0.2, min=0.0, max=10.0, step=0.01), + ], + outputs=[ + io.Model.Output(), + ], + ) + + @classmethod + def execute(cls, model, b1, b2, s1, s2): + model_channels = model.model.model_config.unet_config["model_channels"] + scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} + on_cpu_devices = {} + + def output_block_patch(h, hsp, transformer_options): + scale = scale_dict.get(int(h.shape[1]), None) + if scale is not None: + hidden_mean = h.mean(1).unsqueeze(1) + B = hidden_mean.shape[0] + hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) + hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) + hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) + + h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1) + + if hsp.device not in on_cpu_devices: + try: + hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) + except Exception: + logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) + on_cpu_devices[hsp.device] = True + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + else: + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + + return h, hsp + + m = model.clone() + m.set_model_output_block_patch(output_block_patch) + return io.NodeOutput(m) + + +NODES_LIST = [ + FreeU, + FreeU_V2, +] diff --git a/comfy_extras/v3/nodes_fresca.py b/comfy_extras/v3/nodes_fresca.py new file mode 100644 index 000000000..f2c9c3dee --- /dev/null +++ b/comfy_extras/v3/nodes_fresca.py @@ -0,0 +1,110 @@ +# Code based on https://github.com/WikiChao/FreSca (MIT License) + +from __future__ import annotations + +import torch +import torch.fft as fft + +from comfy_api.v3 import io + + +def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20): + """ + Apply frequency-dependent scaling to an image tensor using Fourier transforms. + + Parameters: + x: Input tensor of shape (B, C, H, W) + scale_low: Scaling factor for low-frequency components (default: 1.0) + scale_high: Scaling factor for high-frequency components (default: 1.5) + freq_cutoff: Number of frequency indices around center to consider as low-frequency (default: 20) + + Returns: + x_filtered: Filtered version of x in spatial domain with frequency-specific scaling applied. + """ + # Preserve input dtype and device + dtype, device = x.dtype, x.device + + # Convert to float32 for FFT computations + x = x.to(torch.float32) + + # 1) Apply FFT and shift low frequencies to center + x_freq = fft.fftn(x, dim=(-2, -1)) + x_freq = fft.fftshift(x_freq, dim=(-2, -1)) + + # Initialize mask with high-frequency scaling factor + mask = torch.ones(x_freq.shape, device=device) * scale_high + m = mask + for d in range(len(x_freq.shape) - 2): + dim = d + 2 + cc = x_freq.shape[dim] // 2 + f_c = min(freq_cutoff, cc) + m = m.narrow(dim, cc - f_c, f_c * 2) + + # Apply low-frequency scaling factor to center region + m[:] = scale_low + + # 3) Apply frequency-specific scaling + x_freq = x_freq * mask + + # 4) Convert back to spatial domain + x_freq = fft.ifftshift(x_freq, dim=(-2, -1)) + x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real + + # 5) Restore original dtype + x_filtered = x_filtered.to(dtype) + + return x_filtered + + +class FreSca(io.ComfyNodeV3): + @classmethod + def define_schema(cls): + return io.SchemaV3( + node_id="FreSca_V3", + display_name="FreSca _V3", + category="_for_testing", + description="Applies frequency-dependent scaling to the guidance", + inputs=[ + io.Model.Input(id="model"), + io.Float.Input(id="scale_low", default=1.0, min=0, max=10, step=0.01, + tooltip="Scaling factor for low-frequency components"), + io.Float.Input(id="scale_high", default=1.25, min=0, max=10, step=0.01, + tooltip="Scaling factor for high-frequency components"), + io.Int.Input(id="freq_cutoff", default=20, min=1, max=10000, step=1, + tooltip="Number of frequency indices around center to consider as low-frequency"), + ], + outputs=[ + io.Model.Output(), + ], + is_experimental=True, + ) + + @classmethod + def execute(cls, model, scale_low, scale_high, freq_cutoff): + def custom_cfg_function(args): + conds_out = args["conds_out"] + if len(conds_out) <= 1 or None in args["conds"][:2]: + return conds_out + cond = conds_out[0] + uncond = conds_out[1] + + guidance = cond - uncond + filtered_guidance = Fourier_filter( + guidance, + scale_low=scale_low, + scale_high=scale_high, + freq_cutoff=freq_cutoff, + ) + filtered_cond = filtered_guidance + uncond + + return [filtered_cond, uncond] + conds_out[2:] + + m = model.clone() + m.set_model_sampler_pre_cfg_function(custom_cfg_function) + + return io.NodeOutput(m) + + +NODES_LIST = [ + FreSca, +] diff --git a/comfy_extras/v3/nodes_gits.py b/comfy_extras/v3/nodes_gits.py new file mode 100644 index 000000000..a849be038 --- /dev/null +++ b/comfy_extras/v3/nodes_gits.py @@ -0,0 +1,376 @@ +from __future__ import annotations + +import numpy as np +import torch + +from comfy_api.v3 import io + + +def loglinear_interp(t_steps, num_steps): + """Performs log-linear interpolation of a given array of decreasing numbers.""" + xs = np.linspace(0, 1, len(t_steps)) + ys = np.log(t_steps[::-1]) + + new_xs = np.linspace(0, 1, num_steps) + new_ys = np.interp(new_xs, xs, ys) + + return np.exp(new_ys)[::-1].copy() + + +NOISE_LEVELS = { + 0.80: [ + [14.61464119, 7.49001646, 0.02916753], + [14.61464119, 11.54541874, 6.77309084, 0.02916753], + [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753], + [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753], + [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753], + [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], + [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 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+ @classmethod + def define_schema(cls): + return io.SchemaV3( + node_id="GITSScheduler_V3", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Float.Input(id="coeff", default=1.20, min=0.80, max=1.50, step=0.05), + io.Int.Input(id="steps", default=10, min=2, max=1000), + io.Float.Input(id="denoise", default=1.0, min=0.0, max=1.0, step=0.01), + ], + outputs=[ + io.Sigmas.Output(), + ], + ) + + @classmethod + def execute(cls, coeff, steps, denoise): + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return io.NodeOutput(torch.FloatTensor([])) + total_steps = round(steps * denoise) + + if steps <= 20: + sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:] + else: + sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:] + sigmas = loglinear_interp(sigmas, steps + 1) + + sigmas = sigmas[-(total_steps + 1):] + sigmas[-1] = 0 + return io.NodeOutput(torch.FloatTensor(sigmas)) + + +NODES_LIST = [ + GITSScheduler, +] diff --git a/nodes.py b/nodes.py index fc813f3a2..bbe5167f8 100644 --- a/nodes.py +++ b/nodes.py @@ -2313,6 +2313,10 @@ def init_builtin_extra_nodes(): "v3/nodes_cond.py", "v3/nodes_controlnet.py", "v3/nodes_cosmos.py", + "v3/nodes_flux.py", + "v3/nodes_freelunch.py", + "v3/nodes_fresca.py", + "v3/nodes_gits.py", "v3/nodes_images.py", "v3/nodes_mask.py", "v3/nodes_preview_any.py", diff --git a/pyproject.toml b/pyproject.toml index 10d1f3533..55bc29518 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,9 +23,8 @@ lint.select = [ # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f "F", ] +ignore = ["E501"] # disable line-length checking exclude = ["*.ipynb"] -line-length = 144 -lint.pycodestyle.ignore-overlong-task-comments = true [tool.ruff.lint.per-file-ignores] "!comfy_extras/v3/*" = ["E", "I"] # enable these rules only for V3 nodes