141 lines
4.6 KiB
Python
141 lines
4.6 KiB
Python
"""
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This file is part of ComfyUI.
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Copyright (C) 2024 Stability AI
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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import torch
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import nodes
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import comfy.utils
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class StableCascade_EmptyLatentImage:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
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"compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})
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}}
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RETURN_TYPES = ("LATENT", "LATENT")
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RETURN_NAMES = ("stage_c", "stage_b")
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FUNCTION = "generate"
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CATEGORY = "latent/stable_cascade"
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def generate(self, width, height, compression, batch_size=1):
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c_latent = torch.zeros([batch_size, 16, height // compression, width // compression])
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b_latent = torch.zeros([batch_size, 4, height // 4, width // 4])
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return ({
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"samples": c_latent,
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}, {
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"samples": b_latent,
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})
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class StableCascade_StageC_VAEEncode:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"image": ("IMAGE",),
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"vae": ("VAE", ),
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"compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
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}}
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RETURN_TYPES = ("LATENT", "LATENT")
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RETURN_NAMES = ("stage_c", "stage_b")
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FUNCTION = "generate"
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CATEGORY = "latent/stable_cascade"
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def generate(self, image, vae, compression):
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width = image.shape[-2]
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height = image.shape[-3]
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out_width = (width // compression) * vae.downscale_ratio
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out_height = (height // compression) * vae.downscale_ratio
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s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1)
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c_latent = vae.encode(s[:,:,:,:3])
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b_latent = torch.zeros([c_latent.shape[0], 4, height // 4, width // 4])
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return ({
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"samples": c_latent,
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}, {
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"samples": b_latent,
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})
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class StableCascade_StageB_Conditioning:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "conditioning": ("CONDITIONING",),
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"stage_c": ("LATENT",),
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}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "set_prior"
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CATEGORY = "conditioning/stable_cascade"
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def set_prior(self, conditioning, stage_c):
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c = []
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for t in conditioning:
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d = t[1].copy()
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d['stable_cascade_prior'] = stage_c['samples']
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n = [t[0], d]
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c.append(n)
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return (c, )
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class StableCascade_SuperResolutionControlnet:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"image": ("IMAGE",),
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"vae": ("VAE", ),
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}}
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RETURN_TYPES = ("IMAGE", "LATENT", "LATENT")
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RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b")
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FUNCTION = "generate"
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CATEGORY = "_for_testing/stable_cascade"
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def generate(self, image, vae):
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width = image.shape[-2]
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height = image.shape[-3]
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batch_size = image.shape[0]
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controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1)
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c_latent = torch.zeros([batch_size, 16, height // 16, width // 16])
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b_latent = torch.zeros([batch_size, 4, height // 2, width // 2])
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return (controlnet_input, {
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"samples": c_latent,
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}, {
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"samples": b_latent,
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})
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NODE_CLASS_MAPPINGS = {
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"StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage,
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"StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning,
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"StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode,
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"StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet,
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}
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