89 lines
4.8 KiB
Markdown
89 lines
4.8 KiB
Markdown
# Documentation
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- Class name: MikeySampler
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- Category: Mikey/Sampling
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- Output node: False
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- Repo Ref: https://github.com/bash-j/mikey_nodes
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MikeySampler节点旨在从给定的基础模型和细化模型生成高质量的样本，同时利用VAE进行潜在空间操作。它执行复杂的采样技术，并结合条件输入来优化输出。该节点还根据指定的方法和强度对生成的图像进行放大，确保最终输出满足所需的分辨率和质量标准。
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# Input types
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## Required
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- seed
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- 种子参数对于采样过程的可重复性至关重要，确保在不同运行中生成的样本是一致的。它在采样算法的随机性控制中起着重要作用。
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- Comfy dtype: INT
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- Python dtype: int
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- base_model
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- 基础模型是用于潜在空间初始采样的基础神经网络。它是一个关键组件，决定了生成样本的质量和多样性。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- refiner_model
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- 细化模型用于进一步提高基础模型生成的样本质量。它细化潜在空间表示，以实现最终输出的更高保真度。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- samples
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- 样本参数表示要从基础模型中采样的潜在向量的数量。它直接影响生成输出的多样性。
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- Comfy dtype: LATENT
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- Python dtype: torch.Tensor
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## Optional
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- upscale_by
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- upscale_by参数确定生成图像的缩放因子。它是控制最终输出分辨率的重要因素。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- hires_strength
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- hires_strength参数调整高分辨率放大过程的强度。它影响放大图像的清晰度和细节水平。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- latent
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- latent参数表示采样过程中得到的编码潜在向量。它很重要，因为它作为模型和最终放大图像之间的中间表示。
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- Comfy dtype: LATENT
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- Python dtype: torch.Tensor
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# Usage tips
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- Infra type: GPU
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# Source code
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```
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class MikeySampler:
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'base_model': ('MODEL',), 'refiner_model': ('MODEL',), 'samples': ('LATENT',), 'vae': ('VAE',), 'positive_cond_base': ('CONDITIONING',), 'negative_cond_base': ('CONDITIONING',), 'positive_cond_refiner': ('CONDITIONING',), 'negative_cond_refiner': ('CONDITIONING',), 'model_name': (folder_paths.get_filename_list('upscale_models'),), 'seed': ('INT', {'default': 0, 'min': 0, 'max': 18446744073709551615}), 'upscale_by': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 10.0, 'step': 0.1}), 'hires_strength': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 2.0, 'step': 0.1})}}
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RETURN_TYPES = ('LATENT',)
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FUNCTION = 'run'
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CATEGORY = 'Mikey/Sampling'
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def adjust_start_step(self, image_complexity, hires_strength=1.0):
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image_complexity /= 24
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if image_complexity > 1:
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image_complexity = 1
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image_complexity = min([0.55, image_complexity]) * hires_strength
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return min([16, 16 - int(round(image_complexity * 16, 0))])
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def run(self, seed, base_model, refiner_model, vae, samples, positive_cond_base, negative_cond_base, positive_cond_refiner, negative_cond_refiner, model_name, upscale_by=1.0, hires_strength=1.0, upscale_method='normal'):
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image_scaler = ImageScale()
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vaeencoder = VAEEncode()
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vaedecoder = VAEDecode()
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uml = UpscaleModelLoader()
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upscale_model = uml.load_model(model_name)[0]
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iuwm = ImageUpscaleWithModel()
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sample1 = common_ksampler(base_model, seed, 25, 6.5, 'dpmpp_2s_ancestral', 'simple', positive_cond_base, negative_cond_base, samples, start_step=0, last_step=18, force_full_denoise=False)[0]
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sample2 = common_ksampler(refiner_model, seed, 30, 3.5, 'dpmpp_2m', 'simple', positive_cond_refiner, negative_cond_refiner, sample1, disable_noise=True, start_step=21, force_full_denoise=True)
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if upscale_by == 0:
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return sample2
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else:
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sample2 = sample2[0]
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pixels = vaedecoder.decode(vae, sample2)[0]
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(org_width, org_height) = (pixels.shape[2], pixels.shape[1])
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img = iuwm.upscale(upscale_model, image=pixels)[0]
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(upscaled_width, upscaled_height) = (int(org_width * upscale_by // 8 * 8), int(org_height * upscale_by // 8 * 8))
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img = image_scaler.upscale(img, 'nearest-exact', upscaled_width, upscaled_height, 'center')[0]
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if hires_strength == 0:
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return (vaeencoder.encode(vae, img)[0],)
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image_complexity = calculate_image_complexity(img)
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start_step = self.adjust_start_step(image_complexity, hires_strength)
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latent = vaeencoder.encode(vae, img)[0]
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out = common_ksampler(base_model, seed, 16, 9.5, 'dpmpp_2m_sde', 'karras', positive_cond_base, negative_cond_base, latent, start_step=start_step, force_full_denoise=True)
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return out
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``` |