Custom VAE
This commit is contained in:
City
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"""
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List of all VAE configs, with training parts stripped.
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"""
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vae_conf = {
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"kl-f4": {
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"type" : "AutoencoderKL",
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"embed_scale" : 4,
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"embed_dim" : 3,
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"z_channels" : 3,
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"double_z" : True,
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"resolution" : 256,
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"in_channels" : 3,
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"out_ch" : 3,
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"ch" : 128,
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"ch_mult" : [1,2,4],
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"num_res_blocks" : 2,
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"attn_resolutions" : [],
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},
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"kl-f8": { # Default SD1.5 VAE
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"type" : "AutoencoderKL",
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"embed_scale" : 8,
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"embed_dim" : 4,
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"z_channels" : 4,
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"double_z" : True,
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"resolution" : 256,
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"in_channels" : 3,
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"out_ch" : 3,
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"ch" : 128,
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"ch_mult" : [1,2,4,4],
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"num_res_blocks" : 2,
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"attn_resolutions" : [],
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},
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"kl-f16": {
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"type" : "AutoencoderKL",
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"embed_scale" : 16,
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"embed_dim" : 16,
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"z_channels" : 16,
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"double_z" : True,
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"resolution" : 256,
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"in_channels" : 3,
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"out_ch" : 3,
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"ch" : 128,
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"ch_mult" : [1,1,2,2,4],
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"num_res_blocks" : 2,
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"attn_resolutions" : [16],
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},
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"kl-f32": {
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"type" : "AutoencoderKL",
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"embed_scale" : 32,
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"embed_dim" : 64,
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"z_channels" : 64,
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"double_z" : True,
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"resolution" : 256,
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"in_channels" : 3,
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"out_ch" : 3,
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"ch" : 128,
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"ch_mult" : [1,1,2,2,4,4],
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"num_res_blocks" : 2,
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"attn_resolutions" : [16,8],
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},
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}
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+106
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import torch
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import comfy.sd
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import comfy.utils
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from comfy import model_management
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from comfy import diffusers_convert
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from .models.kl import AutoencoderKL
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vae_dtype_dict = {
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"auto" : model_management.vae_device(),
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"fp32" : torch.float32,
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"fp16" : torch.float16,
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"bf16" : torch.bfloat16,
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}
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class EXVAE(comfy.sd.VAE):
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def __init__(self, model_path, model_conf, dtype=None):
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sd = comfy.utils.load_torch_file(model_path)
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if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
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sd = diffusers_convert.convert_vae_state_dict(sd)
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self.latent_dim = model_conf["embed_dim"]
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self.latent_scale = model_conf["embed_scale"]
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if model_conf["type"] == "AutoencoderKL":
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model = AutoencoderKL(config=model_conf)
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self.first_stage_model = model.eval()
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m, u = self.first_stage_model.load_state_dict(sd, strict=False)
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if len(m) > 0: print("Missing VAE keys", m)
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if len(u) > 0: print("Leftover VAE keys", u)
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self.device = model_management.vae_device()
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self.offload_device = model_management.vae_offload_device()
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self.vae_dtype = vae_dtype_dict.get(dtype, "auto")
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self.first_stage_model.to(self.vae_dtype)
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### Encode/Decode functions below needed due to source repo having 4 VAE channels and a scale factor of 8 hardcoded
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def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
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steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
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steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
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steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
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pbar = comfy.utils.ProgressBar(steps)
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decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float()
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output = torch.clamp((
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(comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.latent_scale, pbar = pbar) +
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comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.latent_scale, pbar = pbar) +
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comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.latent_scale, pbar = pbar))
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/ 3.0) / 2.0, min=0.0, max=1.0)
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return output
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def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
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steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
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steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
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steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
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pbar = comfy.utils.ProgressBar(steps)
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encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float()
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samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.latent_scale), out_channels=self.latent_dim, pbar=pbar)
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samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.latent_scale), out_channels=self.latent_dim, pbar=pbar)
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samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.latent_scale), out_channels=self.latent_dim, pbar=pbar)
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samples /= 3.0
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return samples
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def decode(self, samples_in):
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self.first_stage_model = self.first_stage_model.to(self.device)
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try:
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memory_used = (2562 * samples_in.shape[2] * samples_in.shape[3] * 64) * 1.7
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model_management.free_memory(memory_used, self.device)
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free_memory = model_management.get_free_memory(self.device)
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batch_number = int(free_memory / memory_used)
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batch_number = max(1, batch_number)
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pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.latent_scale), round(samples_in.shape[3] * self.latent_scale)), device="cpu")
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for x in range(0, samples_in.shape[0], batch_number):
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samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
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pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).cpu().float() + 1.0) / 2.0, min=0.0, max=1.0)
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except model_management.OOM_EXCEPTION as e:
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print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
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pixel_samples = self.decode_tiled_(samples_in)
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self.first_stage_model = self.first_stage_model.to(self.offload_device)
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pixel_samples = pixel_samples.cpu().movedim(1,-1)
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return pixel_samples
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def encode(self, pixel_samples):
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self.first_stage_model = self.first_stage_model.to(self.device)
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pixel_samples = pixel_samples.movedim(-1,1)
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try:
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memory_used = (2078 * pixel_samples.shape[2] * pixel_samples.shape[3]) * 1.7 #NOTE: this constant along with the one in the decode above are estimated from the mem usage for the VAE and could change.
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model_management.free_memory(memory_used, self.device)
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free_memory = model_management.get_free_memory(self.device)
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batch_number = int(free_memory / memory_used)
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batch_number = max(1, batch_number)
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samples = torch.empty((pixel_samples.shape[0], self.first_stage_model.embed_dim, round(pixel_samples.shape[2] // self.latent_scale), round(pixel_samples.shape[3] // self.latent_scale)), device="cpu")
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for x in range(0, pixel_samples.shape[0], batch_number):
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pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device)
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samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).cpu().float()
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except model_management.OOM_EXCEPTION as e:
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print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
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samples = self.encode_tiled_(pixel_samples)
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self.first_stage_model = self.first_stage_model.to(self.offload_device)
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return samples
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MIT License
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Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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import torch
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import numpy as np
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from torch import nn
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def Normalize(in_channels, num_groups=32):
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return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
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def nonlinearity(x): # swish
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return x*torch.sigmoid(x)
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class AutoencoderKL(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.embed_dim = config["embed_dim"]
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self.encoder = Encoder(**config)
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self.decoder = Decoder(**config)
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assert config["double_z"]
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self.quant_conv = torch.nn.Conv2d(2*config["z_channels"], 2*self.embed_dim, 1)
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self.post_quant_conv = torch.nn.Conv2d(self.embed_dim, config["z_channels"], 1)
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def encode(self, x):
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h = self.encoder(x)
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moments = self.quant_conv(h)
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posterior = DiagonalGaussianDistribution(moments)
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return posterior.sample()
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def decode(self, z):
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z = self.post_quant_conv(z)
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dec = self.decoder(z)
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return dec
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def forward(self, input, sample_posterior=True):
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posterior = self.encode(input)
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if sample_posterior:
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z = posterior.sample()
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else:
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z = posterior.mode()
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dec = self.decode(z)
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return dec, posterior
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class Encoder(nn.Module):
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def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
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attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
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resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
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**ignore_kwargs):
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super().__init__()
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if use_linear_attn: attn_type = "linear"
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self.ch = ch
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self.temb_ch = 0
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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# downsampling
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self.conv_in = torch.nn.Conv2d(in_channels,
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self.ch,
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kernel_size=3,
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stride=1,
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padding=1)
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curr_res = resolution
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in_ch_mult = (1,)+tuple(ch_mult)
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self.in_ch_mult = in_ch_mult
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self.down = nn.ModuleList()
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for i_level in range(self.num_resolutions):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_in = ch*in_ch_mult[i_level]
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block_out = ch*ch_mult[i_level]
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for i_block in range(self.num_res_blocks):
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block.append(ResnetBlock(in_channels=block_in,
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out_channels=block_out,
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temb_channels=self.temb_ch,
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dropout=dropout))
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block_in = block_out
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if curr_res in attn_resolutions:
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attn.append(make_attn(block_in, attn_type=attn_type))
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down = nn.Module()
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down.block = block
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down.attn = attn
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if i_level != self.num_resolutions-1:
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down.downsample = Downsample(block_in, resamp_with_conv)
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curr_res = curr_res // 2
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self.down.append(down)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout)
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self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
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self.mid.block_2 = ResnetBlock(in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout)
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# end
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self.norm_out = Normalize(block_in)
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self.conv_out = torch.nn.Conv2d(block_in,
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2*z_channels if double_z else z_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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def forward(self, x):
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# timestep embedding
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temb = None
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# downsampling
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hs = [self.conv_in(x)]
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for i_level in range(self.num_resolutions):
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for i_block in range(self.num_res_blocks):
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h = self.down[i_level].block[i_block](hs[-1], temb)
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if len(self.down[i_level].attn) > 0:
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h = self.down[i_level].attn[i_block](h)
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hs.append(h)
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if i_level != self.num_resolutions-1:
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hs.append(self.down[i_level].downsample(hs[-1]))
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# middle
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h = hs[-1]
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h = self.mid.block_1(h, temb)
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h, temb)
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# end
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h = self.norm_out(h)
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h = nonlinearity(h)
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h = self.conv_out(h)
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return h
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class Decoder(nn.Module):
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def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
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attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
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resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
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attn_type="vanilla", post_quant_conv=None, **ignorekwargs):
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super().__init__()
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if use_linear_attn: attn_type = "linear"
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self.ch = ch
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self.temb_ch = 0
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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self.give_pre_end = give_pre_end
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self.tanh_out = tanh_out
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self.post_quant_conv = post_quant_conv
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# compute in_ch_mult, block_in and curr_res at lowest res
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in_ch_mult = (1,)+tuple(ch_mult)
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block_in = ch*ch_mult[self.num_resolutions-1]
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curr_res = resolution // 2**(self.num_resolutions-1)
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self.z_shape = (1,z_channels,curr_res,curr_res)
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print("Working with z of shape {} = {} dimensions.".format(
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self.z_shape, np.prod(self.z_shape)))
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# z to block_in
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self.conv_in = torch.nn.Conv2d(z_channels,
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block_in,
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kernel_size=3,
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stride=1,
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padding=1)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout)
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self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
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self.mid.block_2 = ResnetBlock(in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout)
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# upsampling
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self.up = nn.ModuleList()
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for i_level in reversed(range(self.num_resolutions)):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_out = ch*ch_mult[i_level]
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for i_block in range(self.num_res_blocks+1):
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block.append(ResnetBlock(in_channels=block_in,
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out_channels=block_out,
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temb_channels=self.temb_ch,
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dropout=dropout))
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block_in = block_out
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if curr_res in attn_resolutions:
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attn.append(make_attn(block_in, attn_type=attn_type))
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up = nn.Module()
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up.block = block
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up.attn = attn
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if i_level != 0:
|
||||
up.upsample = Upsample(block_in, resamp_with_conv)
|
||||
curr_res = curr_res * 2
|
||||
self.up.insert(0, up) # prepend to get consistent order
|
||||
|
||||
# end
|
||||
self.norm_out = Normalize(block_in)
|
||||
self.conv_out = torch.nn.Conv2d(block_in,
|
||||
out_ch,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
def forward(self, z):
|
||||
#assert z.shape[1:] == self.z_shape[1:]
|
||||
self.last_z_shape = z.shape
|
||||
|
||||
# timestep embedding
|
||||
temb = None
|
||||
|
||||
# z to block_in
|
||||
h = self.conv_in(z)
|
||||
|
||||
# middle
|
||||
h = self.mid.block_1(h, temb)
|
||||
h = self.mid.attn_1(h)
|
||||
h = self.mid.block_2(h, temb)
|
||||
|
||||
# upsampling
|
||||
for i_level in reversed(range(self.num_resolutions)):
|
||||
for i_block in range(self.num_res_blocks+1):
|
||||
h = self.up[i_level].block[i_block](h, temb)
|
||||
if len(self.up[i_level].attn) > 0:
|
||||
h = self.up[i_level].attn[i_block](h)
|
||||
if i_level != 0:
|
||||
h = self.up[i_level].upsample(h)
|
||||
|
||||
# end
|
||||
if self.give_pre_end:
|
||||
return h
|
||||
|
||||
h = self.norm_out(h)
|
||||
h = nonlinearity(h)
|
||||
h = self.conv_out(h)
|
||||
if self.tanh_out:
|
||||
h = torch.tanh(h)
|
||||
return h
|
||||
|
||||
|
||||
class DiagonalGaussianDistribution(object):
|
||||
def __init__(self, parameters, deterministic=False):
|
||||
self.parameters = parameters
|
||||
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
|
||||
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
|
||||
self.deterministic = deterministic
|
||||
self.std = torch.exp(0.5 * self.logvar)
|
||||
self.var = torch.exp(self.logvar)
|
||||
if self.deterministic:
|
||||
self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
|
||||
|
||||
def sample(self):
|
||||
x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
|
||||
return x
|
||||
|
||||
def kl(self, other=None):
|
||||
if self.deterministic:
|
||||
return torch.Tensor([0.])
|
||||
else:
|
||||
if other is None:
|
||||
return 0.5 * torch.sum(torch.pow(self.mean, 2)
|
||||
+ self.var - 1.0 - self.logvar,
|
||||
dim=[1, 2, 3])
|
||||
else:
|
||||
return 0.5 * torch.sum(
|
||||
torch.pow(self.mean - other.mean, 2) / other.var
|
||||
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
|
||||
dim=[1, 2, 3])
|
||||
|
||||
def nll(self, sample, dims=[1,2,3]):
|
||||
if self.deterministic:
|
||||
return torch.Tensor([0.])
|
||||
logtwopi = np.log(2.0 * np.pi)
|
||||
return 0.5 * torch.sum(
|
||||
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
|
||||
dim=dims)
|
||||
|
||||
def mode(self):
|
||||
return self.mean
|
||||
|
||||
|
||||
class Upsample(nn.Module):
|
||||
def __init__(self, in_channels, with_conv):
|
||||
super().__init__()
|
||||
self.with_conv = with_conv
|
||||
if self.with_conv:
|
||||
self.conv = torch.nn.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
# BF16 fix
|
||||
xh = x.to(torch.float32)
|
||||
xh = torch.nn.functional.interpolate(xh, scale_factor=2.0, mode="nearest")
|
||||
x = xh.to(x.dtype)
|
||||
|
||||
if self.with_conv:
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
|
||||
class Downsample(nn.Module):
|
||||
def __init__(self, in_channels, with_conv):
|
||||
super().__init__()
|
||||
self.with_conv = with_conv
|
||||
if self.with_conv:
|
||||
# no asymmetric padding in torch conv, must do it ourselves
|
||||
self.conv = torch.nn.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=0)
|
||||
|
||||
def forward(self, x):
|
||||
if self.with_conv:
|
||||
pad = (0,1,0,1)
|
||||
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
|
||||
x = self.conv(x)
|
||||
else:
|
||||
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
|
||||
return x
|
||||
|
||||
|
||||
class ResnetBlock(nn.Module):
|
||||
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
|
||||
dropout, temb_channels=512):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
out_channels = in_channels if out_channels is None else out_channels
|
||||
self.out_channels = out_channels
|
||||
self.use_conv_shortcut = conv_shortcut
|
||||
|
||||
self.norm1 = Normalize(in_channels)
|
||||
self.conv1 = torch.nn.Conv2d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
if temb_channels > 0:
|
||||
self.temb_proj = torch.nn.Linear(temb_channels,
|
||||
out_channels)
|
||||
self.norm2 = Normalize(out_channels)
|
||||
self.dropout = torch.nn.Dropout(dropout)
|
||||
self.conv2 = torch.nn.Conv2d(out_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
if self.in_channels != self.out_channels:
|
||||
if self.use_conv_shortcut:
|
||||
self.conv_shortcut = torch.nn.Conv2d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
else:
|
||||
self.nin_shortcut = torch.nn.Conv2d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
|
||||
def forward(self, x, temb):
|
||||
h = x
|
||||
h = self.norm1(h)
|
||||
h = nonlinearity(h)
|
||||
h = self.conv1(h)
|
||||
|
||||
if temb is not None:
|
||||
h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
|
||||
|
||||
h = self.norm2(h)
|
||||
h = nonlinearity(h)
|
||||
h = self.dropout(h)
|
||||
h = self.conv2(h)
|
||||
|
||||
if self.in_channels != self.out_channels:
|
||||
if self.use_conv_shortcut:
|
||||
x = self.conv_shortcut(x)
|
||||
else:
|
||||
x = self.nin_shortcut(x)
|
||||
|
||||
return x+h
|
||||
|
||||
|
||||
class AttnBlock(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = Normalize(in_channels)
|
||||
self.q = torch.nn.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.k = torch.nn.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.v = torch.nn.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.proj_out = torch.nn.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q = self.q(h_)
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
|
||||
# compute attention
|
||||
b,c,h,w = q.shape
|
||||
q = q.reshape(b,c,h*w)
|
||||
q = q.permute(0,2,1) # b,hw,c
|
||||
k = k.reshape(b,c,h*w) # b,c,hw
|
||||
w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
|
||||
# w_ = w_ * (int(c)**(-0.5))
|
||||
w_ = w_ * (c**(-0.5))
|
||||
w_ = torch.nn.functional.softmax(w_, dim=2)
|
||||
|
||||
# attend to values
|
||||
v = v.reshape(b,c,h*w)
|
||||
w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
|
||||
h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
|
||||
h_ = h_.reshape(b,c,h,w)
|
||||
|
||||
h_ = self.proj_out(h_)
|
||||
|
||||
return x+h_
|
||||
|
||||
def make_attn(in_channels, attn_type="vanilla"):
|
||||
assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
|
||||
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
|
||||
if attn_type == "vanilla":
|
||||
return AttnBlock(in_channels)
|
||||
elif attn_type == "none":
|
||||
return nn.Identity(in_channels)
|
||||
else:
|
||||
return LinAttnBlock(in_channels)
|
||||
@@ -0,0 +1,29 @@
|
||||
import folder_paths
|
||||
|
||||
from .conf import vae_conf
|
||||
from .loader import EXVAE
|
||||
|
||||
class ExtraVAELoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"vae_name": (folder_paths.get_filename_list("vae"),),
|
||||
"vae_type": (list(vae_conf.keys()), {"default":"kl-f8"}),
|
||||
"dtype" : (["auto","fp32","fp16","bf16"],),
|
||||
}
|
||||
}
|
||||
RETURN_TYPES = ("VAE",)
|
||||
FUNCTION = "load_vae"
|
||||
CATEGORY = "ExtraModels"
|
||||
TITLE = "ExtraVAELoader"
|
||||
|
||||
def load_vae(self, vae_name, vae_type, dtype):
|
||||
model_path = folder_paths.get_full_path("vae", vae_name)
|
||||
model_conf = vae_conf[vae_type]
|
||||
vae = EXVAE(model_path, model_conf, dtype)
|
||||
return (vae,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"ExtraVAELoader" : ExtraVAELoader,
|
||||
}
|
||||
@@ -14,5 +14,9 @@ else:
|
||||
from .DiT.nodes import NODE_CLASS_MAPPINGS as DiT_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(DiT_Nodes)
|
||||
|
||||
# VAE
|
||||
from .VAE.nodes import NODE_CLASS_MAPPINGS as VAE_Nodes
|
||||
NODE_CLASS_MAPPINGS.update(VAE_Nodes)
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()}
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
|
||||
|
||||
Reference in New Issue
Block a user