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import torch
from . import a1111_compat
import comfy
class KSampler_progress(a1111_compat.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"],),
"interval": ("INT", {"default": 1, "min": 1, "max": 10000}),
"omit_start_latent": ("BOOLEAN", {"default": True, "label_on": "True", "label_off": "False"}),
}
}
CATEGORY = "InspirePack/analysis"
RETURN_TYPES = ("LATENT", "LATENT")
RETURN_NAMES = ("latent", "progress_latent")
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise, noise_mode, interval, omit_start_latent):
adv_steps = int(steps / denoise)
sampler = a1111_compat.KSamplerAdvanced_inspire()
if omit_start_latent:
result = []
else:
result = [latent_image['samples']]
for i in range(0, adv_steps+1):
add_noise = i == 0
return_with_leftover_noise = i != adv_steps
latent_image = sampler.sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, i, i+1, noise_mode, return_with_leftover_noise)[0]
if i % interval == 0 or i == adv_steps:
result.append(latent_image['samples'])
if len(result) > 0:
result = torch.cat(result)
result = {'samples': result}
else:
result = latent_image
return (latent_image, result)
class KSamplerAdvanced_progress(a1111_compat.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"}),
"interval": ("INT", {"default": 1, "min": 1, "max": 10000}),
"omit_start_latent": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
},
"optional": {"prev_progress_latent_opt": ("LATENT",), }
}
FUNCTION = "sample"
CATEGORY = "InspirePack/analysis"
RETURN_TYPES = ("LATENT", "LATENT")
RETURN_NAMES = ("latent", "progress_latent")
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, interval, omit_start_latent, prev_progress_latent_opt=None):
sampler = a1111_compat.KSamplerAdvanced_inspire()
if omit_start_latent:
result = []
else:
result = [latent_image['samples']]
for i in range(start_at_step, end_at_step+1):
cur_add_noise = i == start_at_step and add_noise
cur_return_with_leftover_noise = i != steps or return_with_leftover_noise
latent_image = sampler.sample(model, cur_add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, i, i+1, noise_mode, cur_return_with_leftover_noise)[0]
print(f"{i}, {i+1}")
if i % interval == 0 or i == steps:
result.append(latent_image['samples'])
if len(result) > 0:
result = torch.cat(result)
result = {'samples': result}
else:
result = latent_image
if prev_progress_latent_opt is not None:
result['samples'] = torch.cat((prev_progress_latent_opt['samples'], result['samples']), dim=0)
return (latent_image, result)
NODE_CLASS_MAPPINGS = {
"KSamplerProgress //Inspire": KSampler_progress,
"KSamplerAdvancedProgress //Inspire": KSamplerAdvanced_progress,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"KSamplerProgress //Inspire": "KSampler Progress (Inspire)",
"KSamplerAdvancedProgress //Inspire": "KSampler Advanced Progress (Inspire)",
}
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