mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-08-20 03:23:30 +08:00
88 lines
4.2 KiB
Python
88 lines
4.2 KiB
Python
import latent_preview
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import comfy
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import einops
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import torch
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def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
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device = comfy.model_management.get_torch_device()
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latent_image = latent["samples"]
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if disable_noise:
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noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
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else:
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batch_inds = latent["batch_index"] if "batch_index" in latent else None
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noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
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noise_mask = None
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if "noise_mask" in latent:
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noise_mask = latent["noise_mask"]
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preview_format = "JPEG"
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if preview_format not in ["JPEG", "PNG"]:
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preview_format = "JPEG"
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previewer = latent_preview.get_previewer(device, model.model.latent_format)
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pbar = comfy.utils.ProgressBar(steps)
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def callback(step, x0, x, total_steps):
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preview_bytes = None
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if previewer:
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preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
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pbar.update_absolute(step + 1, total_steps, preview_bytes)
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samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
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denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
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force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, seed=seed)
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out = latent.copy()
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out["samples"] = samples
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return (out, )
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class Gradually_More_Denoise_KSampler:
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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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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"latent_image": ("LATENT", ),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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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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"start_denoise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"denoise_increment": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.1}),
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"denoise_increment_steps": ("INT", {"default": 20, "min": 1, "max": 10000})
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},
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"optional": { "optional_vae": ("VAE",) }
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}
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RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", )
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RETURN_NAMES = ("MODEL", "CONDITIONING+", "CONDITIONING-", "LATENT", "VAE", )
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OUTPUT_NODE = True
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FUNCTION = "sample"
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CATEGORY = "ComfyUI-Frame-Interpolation/others"
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def sample(self, model, positive, negative, latent_image, optional_vae,
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seed, steps, cfg, sampler_name, scheduler,start_denoise, denoise_increment, denoise_increment_steps):
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if start_denoise + denoise_increment * denoise_increment_steps > 1.0:
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raise Exception(f"Max denoise strength can't over 1.0 (start_denoise={start_denoise}, denoise_increment={denoise_increment}, denoise_increment_steps={denoise_increment_steps}")
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copied_latent = latent_image.copy()
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out_samples = []
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for latent_sample in copied_latent["samples"]:
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latent = {"samples": einops.rearrange(latent_sample, "c h w -> 1 c h w")}
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#Latent's shape is NCHW
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gradually_denoising_samples = [
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common_ksampler(
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model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=start_denoise + denoise_increment * i
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)[0]["samples"]
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for i in range(denoise_increment_steps)
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]
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out_samples.extend(gradually_denoising_samples)
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copied_latent["samples"] = torch.cat(out_samples, dim=0)
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return (model, positive, negative, copied_latent, optional_vae) |