42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
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import comfy.diffusers_convert
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import comfy.samplers
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import comfy.sd
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import comfy.utils
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import comfy.clip_vision
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import torch
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import nodes
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class Tiled_KSampler:
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@classmethod
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def INPUT_TYPES(cls):
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return {"required":
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{"model": ("MODEL", ),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"tiling": ("INT", {"default": 1, "min": 0, "max": 1}),
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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}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"latent_image": ("LATENT", ),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "sample"
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CATEGORY = "Sampling/Tiled"
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def apply_circular(self, model, enable):
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for layer in [layer for layer in model.modules() if isinstance(layer, torch.nn.Conv2d)]:
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layer.padding_mode = 'circular' if enable else 'zeros'
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def sample(self, model, seed, tiling, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
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self.apply_circular(model.model, tiling == 1)
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return nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
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NODE_CLASS_MAPPINGS = {
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"Tiled KSampler": Tiled_KSampler,
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} |