jaidaken
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
84 lines
3.3 KiB
Python
84 lines
3.3 KiB
Python
# Due to the current lack of maintenance for the `ComfyUI_Noise` extension,
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# I have copied the code from the applied PR.
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# https://github.com/BlenderNeko/ComfyUI_Noise/pull/13/files
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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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# technically doesn't normalize because unsampling is not guaranteed to end at a std given by the schedule
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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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