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| import comfy
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| import torch
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| class Unsampler:
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| @classmethod
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| def INPUT_TYPES(s):
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| return {"required":
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| {"model": ("MODEL",),
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| "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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| "end_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
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| "cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
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| "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
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| "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
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| "normalize": (["disable", "enable"],),
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| "positive": ("CONDITIONING",),
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| "negative": ("CONDITIONING",),
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| "latent_image": ("LATENT",),
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| }}
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| RETURN_TYPES = ("LATENT",)
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| FUNCTION = "unsampler"
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| CATEGORY = "sampling"
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| def unsampler(self, model, cfg, sampler_name, steps, end_at_step, scheduler, normalize, positive, negative,
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| latent_image):
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| normalize = normalize == "enable"
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| device = comfy.model_management.get_torch_device()
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| latent = latent_image
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| latent_image = latent["samples"]
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| end_at_step = min(end_at_step, steps - 1)
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| end_at_step = steps - end_at_step
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| noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
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| noise_mask = None
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| if "noise_mask" in latent:
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| noise_mask = comfy.sampler_helpers.prepare_mask(latent["noise_mask"], noise.shape, device)
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| noise = noise.to(device)
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| latent_image = latent_image.to(device)
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| conds0 = \
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| {"positive": comfy.sampler_helpers.convert_cond(positive),
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| "negative": comfy.sampler_helpers.convert_cond(negative)}
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| conds = {}
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| for k in conds0:
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| conds[k] = list(map(lambda a: a.copy(), conds0[k]))
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| models, inference_memory = comfy.sampler_helpers.get_additional_models(conds, model.model_dtype())
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| comfy.model_management.load_models_gpu([model] + models, model.memory_required(noise.shape) + inference_memory)
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| sampler = comfy.samplers.KSampler(model, steps=steps, device=device, sampler=sampler_name,
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| scheduler=scheduler, denoise=1.0, model_options=model.model_options)
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| sigmas = sampler.sigmas.flip(0) + 0.0001
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| pbar = comfy.utils.ProgressBar(steps)
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| def callback(step, x0, x, total_steps):
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| pbar.update_absolute(step + 1, total_steps)
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| samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image,
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| force_full_denoise=False, denoise_mask=noise_mask, sigmas=sigmas, start_step=0,
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| last_step=end_at_step, callback=callback)
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| if normalize:
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| samples -= samples.mean()
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| samples /= samples.std()
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| samples = samples.cpu()
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| comfy.sampler_helpers.cleanup_additional_models(models)
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| out = latent.copy()
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| out["samples"] = samples
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| return (out,)
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