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import comfy
import torch
import numpy as np
import latent_preview
from .libs import utils
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0,
noise_mode="CPU", disable_noise=False, start_step=None, last_step=None, force_full_denoise=False,
incremental_seed_mode="comfy", variation_seed=None, variation_strength=None, noise=None):
device = comfy.model_management.get_torch_device()
noise_device = "cpu" if noise_mode == "CPU" else device
latent_image = latent["samples"]
if noise is None:
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device=noise_device)
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = utils.prepare_noise(latent_image, seed, batch_inds, noise_device, incremental_seed_mode, variation_seed=variation_seed, variation_strength=variation_strength)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
preview_format = "JPEG"
if preview_format not in ["JPEG", "PNG"]:
preview_format = "JPEG"
previewer = latent_preview.get_previewer(device, model.model.latent_format)
pbar = comfy.utils.ProgressBar(steps)
def callback(step, x0, x, total_steps):
preview_bytes = None
if previewer:
preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
pbar.update_absolute(step + 1, total_steps, preview_bytes)
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, seed=seed)
out = latent.copy()
out["samples"] = samples
return (out, )
class KSampler_inspire:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"noise_mode": (["GPU(=A1111)", "CPU"],),
"batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
"variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "InspirePack/a1111_compat"
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, batch_seed_mode="comfy", variation_seed=None, variation_strength=None):
return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, incremental_seed_mode=batch_seed_mode, variation_seed=variation_seed, variation_strength=variation_strength)
class KSamplerAdvanced_inspire:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"model": ("MODEL",),
"add_noise": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"}),
"noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
"noise_mode": (["GPU(=A1111)", "CPU"],),
"return_with_leftover_noise": ("BOOLEAN", {"default": False, "label_on": "enable", "label_off": "disable"}),
"batch_seed_mode": (["incremental", "comfy", "variation str inc:0.01", "variation str inc:0.05"],),
"variation_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"variation_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional":
{
"noise_opt": ("NOISE",),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "InspirePack/a1111_compat"
def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, noise_mode, return_with_leftover_noise, denoise=1.0, batch_seed_mode="comfy", variation_seed=None, variation_strength=None, noise_opt=None):
force_full_denoise = True
if return_with_leftover_noise:
force_full_denoise = False
disable_noise = False
if not add_noise:
disable_noise = True
return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step,
force_full_denoise=force_full_denoise, noise_mode=noise_mode, incremental_seed_mode=batch_seed_mode,
variation_seed=variation_seed, variation_strength=variation_strength, noise=noise_opt)
NODE_CLASS_MAPPINGS = {
"KSampler //Inspire": KSampler_inspire,
"KSamplerAdvanced //Inspire": KSamplerAdvanced_inspire,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"KSampler //Inspire": "KSampler (inspire)",
"KSamplerAdvanced //Inspire": "KSamplerAdvanced (inspire)"
}
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