TenserTensor
ee88f9be6c
Implement sampling nodes: - ksampler.py: standard sampler with basic parameters - ksampler_advanced.py: advanced sampler with step control and noise settings - ksampler_context.py: context-based sampler extracting parameters from workflow context - ksampler_two_stage.py: automated two-stage sampling (draft + refinement) Add markdown documentation for all sampler nodes
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
from .ksampler_helpers import sample_latents
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class TT_KSamplerContext():
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"context": ("TT_CONTEXT",)
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}
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}
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RETURN_TYPES = ("TT_CONTEXT", "LATENT",)
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RETURN_NAMES = ("CONTEXT", "LATENT",)
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FUNCTION = "execute_sampling"
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CATEGORY = "TenserTensor/Sampling"
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def execute_sampling(self, context):
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model = context.get("model", None)
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latent = context.get("latent", None)
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config = context.get("workflow_config", None)
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if model is None:
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raise ValueError("Model is required for sampling")
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if latent is None:
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raise ValueError("Latent image is required for sampling")
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if config is None:
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raise ValueError("Workflow Config is required for sampling")
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args = {
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"model": model,
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"positive": context.get("positive", None),
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"negative": context.get("negative", None),
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"latent": latent,
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"seed": config.get("seed", 0),
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"steps": config.get("steps", 0),
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"cfg": config.get("cfg", 0),
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"sampler_name": config.get("sampler_name", None),
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"scheduler": config.get("scheduler", None),
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}
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samples = sample_latents(**args)
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return (context, samples)
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