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import torch
import comfy.model_management
import comfy.sample
import latent_preview
def prepare_mask(mask, shape):
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
mask = mask.expand((-1,shape[1],-1,-1))
if mask.shape[0] < shape[0]:
mask = mask.repeat((shape[0] -1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]]
return mask
def remap_range(value, minIn, MaxIn, minOut, maxOut):
if value > MaxIn: value = MaxIn;
if value < minIn: value = minIn;
finalValue = ((value - minIn) / (MaxIn - minIn)) * (maxOut - minOut) + minOut;
return finalValue;
class KSamplerSDXLAdvanced:
@classmethod
def INPUT_TYPES(s):
ui_widgets = {"required":
{
"model_model": ("MODEL",),
"model_refiner": ("MODEL",),
"CONDITIONING_model_pos": ("CONDITIONING", ),
"CONDITIONING_model_neg": ("CONDITIONING", ),
"CONDITIONING_refiner_pos": ("CONDITIONING", ),
"CONDITIONING_refiner_neg": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"cfg_scale": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 100.0}),
# "cfg_rescale_multiplier": ("FLOAT", {"default": 1, "min": -1.0, "max": 2.0, "step": 0.1}),
"sampler": (comfy.samplers.KSampler.SAMPLERS, {"default": "dpmpp_2m"}),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default": "karras"}),
"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
"base_steps": ("INT", {"default": 12, "min": 0, "max": 10000}),
"refiner_steps": ("INT", {"default": 4, "min": 0, "max": 10000}),
"detail_level": ("FLOAT", {"default": 1, "min": 0.0, "max": 2.0, "step": 0.1}),
"detail_from": (["penultimate_step","base_sample"], {"default": "penultimate_step"}),
"noise_source": (["CPU","GPU"], {"default": "CPU"}),
"auto_rescale_tonemap": (["enable","disable"], {"default": "enable"}),
"rescale_tonemap_to": ("FLOAT", {"default": 7.5, "min": 0, "max": 30.0, "step": 0.5}),
# "refiner_extra_noise": (["enable","disable"], {"default": "disable"}),
# "base_noise": ("FLOAT", {"default": 1, "min": 0.0, "max": 10.0, "step": 0.01}),
# "noise_shift_end_refiner": ("INT", {"default": -1, "min": -10000, "max": 0})
},
"optional":
{
"SD15VAE": ("VAE", ),
"SDXLVAE": ("VAE", ),
}
}
return ui_widgets
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample_sdxl"
CATEGORY = "sampling"
def patch_tonemap(self, model, multiplier):
def sampler_tonemap_reinhard(args):
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
noise_pred = (cond - uncond)
noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred, dim=(1)) + 0.0000000001)[:,None]
noise_pred /= noise_pred_vector_magnitude
mean = torch.mean(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True)
std = torch.std(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True)
top = (std * 3 + mean) * multiplier
#reinhard
noise_pred_vector_magnitude *= (1.0 / top)
new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0)
new_magnitude *= top
return uncond + noise_pred * new_magnitude * cond_scale
m = model.clone()
m.set_model_sampler_cfg_function(sampler_tonemap_reinhard)
return m
# def patch_model(self, model, multiplier):
# def rescale_cfg(args):
# cond = args["cond"]
# uncond = args["uncond"]
# cond_scale = args["cond_scale"]
# x_cfg = uncond + cond_scale * (cond - uncond)
# ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)
# ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)
# x_rescaled = x_cfg * (ro_pos / ro_cfg)
# x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg
# return x_final
# m = model.clone()
# m.set_model_sampler_cfg_function(rescale_cfg)
# return m
def common_ksampler(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
device = comfy.model_management.get_torch_device()
latent_image = latent["samples"]
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
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
def sample(self, model, 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):
force_full_denoise = True
if return_with_leftover_noise == "enable":
force_full_denoise = False
disable_noise = False
if add_noise == "disable":
disable_noise = True
return self.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)
def calc_sigma(self, model, sampler_name, scheduler, steps, start_at_step, end_at_step):
device = comfy.model_management.get_torch_device()
end_at_step = min(steps, end_at_step)
start_at_step = min(start_at_step, end_at_step)
real_model = None
comfy.model_management.load_model_gpu(model)
real_model = model.model
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=1.0, model_options=model.model_options)
sigmas = sampler.sigmas
sigma = sigmas[start_at_step] - sigmas[end_at_step]
sigma /= model.model.latent_format.scale_factor
sigma_output = sigma.cpu().numpy()
print("Calculated sigma:",sigma_output)
return sigma_output
def create_noisy_latents(self, source, seed, width, height, batch_size):
torch.manual_seed(seed)
if source == "CPU":
device = "cpu"
else:
device = comfy.model_management.get_torch_device()
noise = torch.randn((batch_size, 4, height // 8, width // 8), dtype=torch.float32, device=device).cpu()
return {"samples":noise}
def inject_noise(self, latents, strength, noise=None, mask=None):
s = latents.copy()
if noise is None:
return s
if latents["samples"].shape != noise["samples"].shape:
print("warning, shapes in InjectNoise not the same, ignoring")
return s
noised = s["samples"].clone() + noise["samples"].clone() * strength
if mask is not None:
mask = prepare_mask(mask, noised.shape)
noised = mask * noised + (1-mask) * latents["samples"]
s["samples"] = noised
return s
# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475
def slerp(self, val, low, high):
dims = low.shape
#flatten to batches
low = low.reshape(dims[0], -1)
high = high.reshape(dims[0], -1)
low_norm = low/torch.norm(low, dim=1, keepdim=True)
high_norm = high/torch.norm(high, dim=1, keepdim=True)
# in case we divide by zero
low_norm[low_norm != low_norm] = 0.0
high_norm[high_norm != high_norm] = 0.0
omega = torch.acos((low_norm*high_norm).sum(1))
so = torch.sin(omega)
res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
return res.reshape(dims)
def slerp_latents(self, latents1, factor, latents2=None, mask=None):
s = latents1.copy()
if latents2 is None:
return (s,)
if latents1["samples"].shape != latents2["samples"].shape:
print("warning, shapes in LatentSlerp not the same, ignoring")
return (s,)
slerped = self.slerp(factor, latents1["samples"].clone(), latents2["samples"].clone())
if mask is not None:
mask = prepare_mask(mask, slerped.shape)
slerped = mask * slerped + (1-mask) * latents1["samples"]
s["samples"] = slerped
return s
def compute_and_generate_noise(self,samples,seed,width,height,batch_size,model,sampler,scheduler,total_steps,start_at,end_at,source):
noisy_latent = self.create_noisy_latents(source,seed,width,height,batch_size)
sigma_balls = self.calc_sigma(model,sampler,scheduler,total_steps,start_at,end_at)
samples_output = self.inject_noise(samples,sigma_balls,noisy_latent)
return samples_output
def sample_sdxl(self, model_model, model_refiner, CONDITIONING_model_pos, CONDITIONING_model_neg, CONDITIONING_refiner_pos, CONDITIONING_refiner_neg, latent_image, seed, cfg_scale, sampler, scheduler, start_at_step, base_steps, refiner_steps,detail_level,detail_from,noise_source,auto_rescale_tonemap,rescale_tonemap_to,SD15VAE=None, SDXLVAE=None):
# if cfg_rescale_multiplier != 1:
# model_model = self.patch_model(model_model,cfg_rescale_multiplier)
# model_refiner = self.patch_model(model_refiner,cfg_rescale_multiplier)
if auto_rescale_tonemap == "enable" and cfg_scale!=rescale_tonemap_to:
scale_model = 1/cfg_scale*rescale_tonemap_to
model_model = self.patch_tonemap(model_model,scale_model)
if sampler == "uni_pc" or sampler == "uni_pc_bh2":
scale_model = 1/cfg_scale*7.5
model_refiner = self.patch_tonemap(model_refiner,scale_model)
for lat in latent_image['samples']:
d, y, x = lat.size()
break
batch_size = len(latent_image['samples'])
width = x*8
height = y*8
base_start_at = start_at_step
base_end_at = base_steps
base_total_steps = base_steps + refiner_steps
refiner_start_at = base_steps
refiner_end_at = base_steps + refiner_steps
refiner_total_steps = base_steps + refiner_steps
if sampler == "uni_pc" or sampler == "uni_pc_bh2":
noisy_base = self.compute_and_generate_noise(latent_image,seed,width,height,batch_size,model_model,sampler,scheduler,base_end_at-1,base_start_at,base_end_at-1,noise_source)
else:
noisy_base = self.compute_and_generate_noise(latent_image,seed,width,height,batch_size,model_model,sampler,scheduler,base_end_at,base_start_at,base_end_at,noise_source)
sample_model = self.sample(model_model,"disable",seed,base_total_steps,cfg_scale,sampler,scheduler,CONDITIONING_model_pos,CONDITIONING_model_neg,noisy_base,base_start_at,base_end_at,"disable")
if SD15VAE is not None and SDXLVAE is not None:
sample_model["samples"] = SD15VAE.decode(sample_model["samples"])
sample_model["samples"] = SDXLVAE.encode(sample_model["samples"])
if sampler == "uni_pc" or sampler == "uni_pc_bh2":
sampler = "dpmpp_2m"
scheduler = "karras"
if detail_level < 0.9999 or detail_level > 1:
if detail_from == "penultimate_step":
if detail_level > 1:
noisy_latent_1 = self.compute_and_generate_noise(sample_model,seed,width,height,batch_size,model_refiner,sampler,scheduler,refiner_total_steps+1,refiner_start_at,refiner_end_at+1,noise_source)
else:
noisy_latent_1 = self.compute_and_generate_noise(sample_model,seed,width,height,batch_size,model_refiner,sampler,scheduler,refiner_total_steps-1,refiner_start_at,refiner_end_at-1,noise_source)
else:
noisy_latent_1 = sample_model
noisy_latent_2 = self.compute_and_generate_noise(sample_model,seed,width,height,batch_size,model_refiner,sampler,scheduler,refiner_total_steps, refiner_start_at,refiner_end_at,noise_source)
if detail_level > 1:
noisy_latent_3 = self.slerp_latents(noisy_latent_1,remap_range(detail_level,1,2,1,0),noisy_latent_2)
else:
noisy_latent_3 = self.slerp_latents(noisy_latent_1,detail_level,noisy_latent_2)
else:
noisy_latent_3 = self.compute_and_generate_noise(sample_model,seed,width,height,batch_size,model_refiner,sampler,scheduler,refiner_total_steps, refiner_start_at,refiner_end_at,noise_source)
sample_refiner = self.sample(model_refiner,"disable",seed,refiner_total_steps,cfg_scale,sampler,scheduler,CONDITIONING_refiner_pos,CONDITIONING_refiner_neg,noisy_latent_3,refiner_start_at,refiner_end_at,"disable")
return (sample_refiner,)
NODE_CLASS_MAPPINGS = {
"KSamplerSDXLAdvanced": KSamplerSDXLAdvanced
}
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