Custom VAE

VAE/loader.py

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+import torch
+import comfy.sd
+import comfy.utils
+from comfy import model_management
+from comfy import diffusers_convert
+from .models.kl import AutoencoderKL
+
+vae_dtype_dict = {
+	"auto" : model_management.vae_device(),
+	"fp32" : torch.float32,
+	"fp16" : torch.float16,
+	"bf16" : torch.bfloat16,
+}
+
+class EXVAE(comfy.sd.VAE):
+	def __init__(self, model_path, model_conf, dtype=None):
+		sd = comfy.utils.load_torch_file(model_path)
+		if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
+			sd = diffusers_convert.convert_vae_state_dict(sd)
+
+		self.latent_dim   = model_conf["embed_dim"]
+		self.latent_scale = model_conf["embed_scale"]
+
+		if model_conf["type"] == "AutoencoderKL":
+			model = AutoencoderKL(config=model_conf)
+
+		self.first_stage_model = model.eval()
+		m, u = self.first_stage_model.load_state_dict(sd, strict=False)
+		if len(m) > 0: print("Missing VAE keys", m)
+		if len(u) > 0: print("Leftover VAE keys", u)
+
+		self.device = model_management.vae_device()
+		self.offload_device = model_management.vae_offload_device()
+		self.vae_dtype = vae_dtype_dict.get(dtype, "auto")
+		self.first_stage_model.to(self.vae_dtype)
+
+	### Encode/Decode functions below needed due to source repo having 4 VAE channels and a scale factor of 8 hardcoded
+	def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
+		steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
+		steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+		steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+		pbar = comfy.utils.ProgressBar(steps)
+
+		decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float()
+		output = torch.clamp((
+			(comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.latent_scale, pbar = pbar) +
+			comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.latent_scale, pbar = pbar) +
+			 comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.latent_scale, pbar = pbar))
+			/ 3.0) / 2.0, min=0.0, max=1.0)
+		return output
+
+	def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
+		steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
+		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)
+		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)
+		pbar = comfy.utils.ProgressBar(steps)
+
+		encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float()
+		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)
+		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)
+		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)
+		samples /= 3.0
+		return samples
+
+	def decode(self, samples_in):
+		self.first_stage_model = self.first_stage_model.to(self.device)
+		try:
+			memory_used = (2562 * samples_in.shape[2] * samples_in.shape[3] * 64) * 1.7
+			model_management.free_memory(memory_used, self.device)
+			free_memory = model_management.get_free_memory(self.device)
+			batch_number = int(free_memory / memory_used)
+			batch_number = max(1, batch_number)
+
+			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")
+			for x in range(0, samples_in.shape[0], batch_number):
+				samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
+				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)
+		except model_management.OOM_EXCEPTION as e:
+			print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
+			pixel_samples = self.decode_tiled_(samples_in)
+
+		self.first_stage_model = self.first_stage_model.to(self.offload_device)
+		pixel_samples = pixel_samples.cpu().movedim(1,-1)
+		return pixel_samples
+
+	def encode(self, pixel_samples):
+		self.first_stage_model = self.first_stage_model.to(self.device)
+		pixel_samples = pixel_samples.movedim(-1,1)
+		try:
+			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.
+			model_management.free_memory(memory_used, self.device)
+			free_memory = model_management.get_free_memory(self.device)
+			batch_number = int(free_memory / memory_used)
+			batch_number = max(1, batch_number)
+			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")
+			for x in range(0, pixel_samples.shape[0], batch_number):
+				pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device)
+				samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).cpu().float()
+
+		except model_management.OOM_EXCEPTION as e:
+			print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
+			samples = self.encode_tiled_(pixel_samples)
+
+		self.first_stage_model = self.first_stage_model.to(self.offload_device)
+		return samples
+