Merge pull request #3 from 920232796/master (a25dfebe) · Commits · github_fork / Stable Diffusion Webui

modules/devices.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -38,8 +38,8 @@ def get_optimal_device():
		if torch.cuda.is_available():
		return torch.device(get_cuda_device_string())

		# if has_mps():
		# return torch.device("mps")
		if has_mps():
		return torch.device("mps")

		return cpu

+1 −1

Original line number	Diff line number	Diff line
		@@ -29,7 +29,7 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
		# new memory efficient cross attention blocks do not support hypernets and we already
		# have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
		ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
		# ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
		ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention

		# silence new console spam from SD2
		ldm.modules.attention.print = lambda *args: None

+5 −1

Original line number	Diff line number	Diff line
		@@ -110,8 +110,12 @@ restricted_opts = {
		from omegaconf import OmegaConf
		config = OmegaConf.load(f"{cmd_opts.config}")
		# XLMR-Large
		try:
		text_model_name = config.model.params.cond_stage_config.params.name

		except :
		text_model_name = "stable_diffusion"

		cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access

		devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \

0 → 100644

+67 −0

Original line number	Diff line number	Diff line
		model:
		base_learning_rate: 1.0e-4
		target: ldm.models.diffusion.ddpm.LatentDiffusion
		params:
		linear_start: 0.00085
		linear_end: 0.0120
		num_timesteps_cond: 1
		log_every_t: 200
		timesteps: 1000
		first_stage_key: "jpg"
		cond_stage_key: "txt"
		image_size: 64
		channels: 4
		cond_stage_trainable: false
		conditioning_key: crossattn
		monitor: val/loss_simple_ema
		scale_factor: 0.18215
		use_ema: False # we set this to false because this is an inference only config

		unet_config:
		target: ldm.modules.diffusionmodules.openaimodel.UNetModel
		params:
		use_checkpoint: True
		use_fp16: True
		image_size: 32 # unused
		in_channels: 4
		out_channels: 4
		model_channels: 320
		attention_resolutions: [ 4, 2, 1 ]
		num_res_blocks: 2
		channel_mult: [ 1, 2, 4, 4 ]
		num_head_channels: 64 # need to fix for flash-attn
		use_spatial_transformer: True
		use_linear_in_transformer: True
		transformer_depth: 1
		context_dim: 1024
		legacy: False

		first_stage_config:
		target: ldm.models.autoencoder.AutoencoderKL
		params:
		embed_dim: 4
		monitor: val/rec_loss
		ddconfig:
		#attn_type: "vanilla-xformers"
		double_z: true
		z_channels: 4
		resolution: 256
		in_channels: 3
		out_ch: 3
		ch: 128
		ch_mult:
		- 1
		- 2
		- 4
		- 4
		num_res_blocks: 2
		attn_resolutions: []
		dropout: 0.0
		lossconfig:
		target: torch.nn.Identity

		cond_stage_config:
		target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
		params:
		freeze: True
		layer: "penultimate"
		No newline at end of file