separate cfg scales + include sigma shift
This commit is contained in:
Stéphane du Hamel
54
nodes.py
54
nodes.py
@ -3,10 +3,12 @@ import torch
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import comfy.sample
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import comfy.samplers
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import comfy.utils
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import comfy.model_sampling
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import latent_preview
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def wan_ksampler(model_high_noise, model_low_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, boundary = 0.875, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
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def wan_ksampler(model_high_noise, model_low_noise, seed, steps, cfgs, sampler_name, scheduler, positive, negative, latent, boundary = 0.875, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
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# boundary is .9 for i2v, .875 for t2v
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latent_image = latent["samples"]
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@ -32,9 +34,12 @@ def wan_ksampler(model_high_noise, model_low_noise, seed, steps, cfg, sampler_na
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sampler = comfy.samplers.KSampler(model_high_noise, steps=steps, device=model_high_noise.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model_high_noise.model_options)
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sigmas = sampler.sigmas
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# why are timesteps 0-1000?
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timesteps = [model_high_noise.get_model_object("model_sampling").timestep(sigma)/1000 for sigma in sigmas.tolist()]
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sampling = model_high_noise.get_model_object("model_sampling")
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timesteps = [sampling.timestep(sigma)/1000 for sigma in sigmas.tolist()]
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print(sigmas.tolist())
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print(timesteps)
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switching_step = steps
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for (i,t) in enumerate(timesteps):
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for (i,t) in enumerate(timesteps[1:]):
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if t < boundary:
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switching_step = i
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break
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@ -47,7 +52,7 @@ def wan_ksampler(model_high_noise, model_low_noise, seed, steps, cfg, sampler_na
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callback = latent_preview.prepare_callback(model_high_noise, steps)
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end_step = min(last_step,switching_step)
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latent_image = comfy.sample.fix_empty_latent_channels(model_high_noise, latent_image)
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latent_image = comfy.sample.sample(model_high_noise, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
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latent_image = comfy.sample.sample(model_high_noise, noise, steps, cfgs[0], sampler_name, scheduler, positive, negative, latent_image,
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denoise=denoise, disable_noise=end_wth_low or disable_noise, start_step=start_step, last_step=end_step,
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force_full_denoise=end_wth_low or force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
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@ -57,7 +62,7 @@ def wan_ksampler(model_high_noise, model_low_noise, seed, steps, cfg, sampler_na
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callback = latent_preview.prepare_callback(model_low_noise, steps)
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begin_step = max(start_step, switching_step)
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latent_image = comfy.sample.fix_empty_latent_channels(model_low_noise, latent_image)
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latent_image = comfy.sample.sample(model_low_noise, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
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latent_image = comfy.sample.sample(model_low_noise, noise, steps, cfgs[1], sampler_name, scheduler, positive, negative, latent_image,
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denoise=denoise, disable_noise=disable_noise, start_step=begin_step, last_step=last_step,
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force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
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@ -65,6 +70,20 @@ def wan_ksampler(model_high_noise, model_low_noise, seed, steps, cfg, sampler_na
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out["samples"] = latent_image
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return (out, )
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def set_shift(model,sigma_shift):
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sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
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sampling_type = comfy.model_sampling.CONST
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class ModelSamplingAdvanced(sampling_base, sampling_type):
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pass
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model_sampling = ModelSamplingAdvanced(model.model.model_config)
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model_sampling.set_parameters(shift=sigma_shift, multiplier=1000)
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model.add_object_patch("model_sampling", model_sampling)
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model.add_object_patch("model_sampling", model_sampling)
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return model
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class WanMoeKSampler:
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@classmethod
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def INPUT_TYPES(s):
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@ -72,12 +91,14 @@ class WanMoeKSampler:
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"required": {
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"model_high_noise": ("MODEL", {"tooltip": "The first expert of the model used for denoising the input latent."}),
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"model_low_noise": ("MODEL", {"tooltip": "The second expert of the model used for denoising the input latent."}),
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"boundary": ("FLOAT", {"default:": 0.875, "min": 0.0, "max": 1.0, "step": 0.001, "round": 0.001,"tooltip": "Boundary (or t_moe): Timestep (not to be confused with denoising step) at which models should be swapped. Recommended values: 0.875 for t2v, 0.9 for i2v"}),
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"boundary": ("FLOAT", {"default": 0.875, "min": 0.0, "max": 1.0, "step": 0.001, "round": 0.001,"tooltip": "Boundary (or t_moe): Timestep (not to be confused with denoising step) at which models should be switched. Recommended values: 0.875 for t2v, 0.9 for i2v"}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
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"cfg_high_noise": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
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"cfg_low_noise": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "The scheduler controls how noise is gradually removed to form the image."}),
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"sigma_shift": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.01, "tooltip": "Same purpose as the a shift parameter in the ModelSamplingSD3 node (same value applied to both models)"}),
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"positive": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to include in the image."}),
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"negative": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to exclude from the image."}),
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"latent_image": ("LATENT", {"tooltip": "The latent image to denoise."}),
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@ -92,8 +113,11 @@ class WanMoeKSampler:
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CATEGORY = "sampling"
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DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image."
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def sample(self, model_high_noise, model_low_noise, boundary, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
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return wan_ksampler(model_high_noise, model_low_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,boundary=boundary, denoise=denoise)
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def sample(self, model_high_noise, model_low_noise, boundary, seed, steps, cfg_high_noise, cfg_low_noise, sampler_name, scheduler, sigma_shift, positive, negative, latent_image, denoise=1.0):
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model_high_noise = set_shift(model_high_noise, sigma_shift)
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model_low_noise = set_shift(model_low_noise, sigma_shift)
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return wan_ksampler(model_high_noise, model_low_noise, seed, steps, (cfg_high_noise, cfg_low_noise), sampler_name, scheduler, positive, negative, latent_image,boundary=boundary, denoise=denoise)
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class WanMoeKSamplerAdvanced:
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@classmethod
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@ -101,13 +125,15 @@ class WanMoeKSamplerAdvanced:
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return {"required":
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{"model_high_noise": ("MODEL", {"tooltip": "The first expert of the model used for denoising the input latent."}),
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"model_low_noise": ("MODEL", {"tooltip": "The second expert of the model used for denoising the input latent."}),
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"boundary":("FLOAT", {"default:": 0.875, "min": 0.0, "max": 1.0, "step": 0.001, "round":0.001,"tooltip": "Boundary (or t_moe): Timestep (not to be confused with denoising step) at which models should be swapped. Recommended values: 0.875 for t2v, 0.9 for i2v"}),
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"boundary": ("FLOAT", {"default": 0.875, "min": 0.0, "max": 1.0, "step": 0.001, "round":0.001}),
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"add_noise": (["enable", "disable"], ),
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"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
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"cfg_high_noise": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
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"cfg_low_noise": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
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"sigma_shift": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.01, "tooltip": "Same purpose as the a shift parameter in the ModelSamplingSD3 node (same value applied to both models)"}),
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"latent_image": ("LATENT", ),
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@ -122,11 +148,13 @@ class WanMoeKSamplerAdvanced:
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CATEGORY = "sampling"
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def sample(self, model_high_noise, model_low_noise, boundary, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):
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def sample(self, model_high_noise, model_low_noise, boundary, add_noise, noise_seed, steps, cfg_high_noise, cfg_low_noise, sampler_name, scheduler, sigma_shift, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):
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model_high_noise = set_shift(model_high_noise, sigma_shift)
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model_low_noise = set_shift(model_low_noise, sigma_shift)
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force_full_denoise = True
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if return_with_leftover_noise == "enable":
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force_full_denoise = False
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disable_noise = False
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if add_noise == "disable":
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disable_noise = True
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return wan_ksampler(model_high_noise, model_low_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, boundary=boundary, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
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return wan_ksampler(model_high_noise, model_low_noise, noise_seed, steps, (cfg_high_noise, cfg_low_noise), sampler_name, scheduler, positive, negative, latent_image, boundary=boundary, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
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