Fine then.
I'm sick of submitting useful PRs only to have them squashed for no reason. This is basically an intermediary step to just writing my own goddam*ed UI that will actually get updated on a regular basis...main
d8ahazard
parent
8701a7b7cc
commit
56f05dd638
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model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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image_size: 64
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channels: 4
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cond_stage_trainable: false # Note: different from the one we trained before
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 10000 ]
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ddconfig:
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double_z: true
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z_channels: 4
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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:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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@ -0,0 +1,68 @@
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model:
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base_learning_rate: 1.0e-4
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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parameterization: "v"
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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image_size: 64
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channels: 4
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cond_stage_trainable: false
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False # we set this to false because this is an inference only config
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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use_checkpoint: True
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use_fp16: True
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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_head_channels: 64 # need to fix for flash-attn
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use_spatial_transformer: True
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use_linear_in_transformer: True
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transformer_depth: 1
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context_dim: 1024
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ddconfig:
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#attn_type: "vanilla-xformers"
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double_z: true
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z_channels: 4
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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:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
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params:
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freeze: True
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layer: "penultimate"
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model:
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base_learning_rate: 1.0e-4
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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image_size: 64
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channels: 4
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cond_stage_trainable: false
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False # we set this to false because this is an inference only config
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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use_checkpoint: True
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use_fp16: True
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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_head_channels: 64 # need to fix for flash-attn
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use_spatial_transformer: True
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use_linear_in_transformer: True
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transformer_depth: 1
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context_dim: 1024
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ddconfig:
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#attn_type: "vanilla-xformers"
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double_z: true
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z_channels: 4
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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:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
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params:
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freeze: True
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layer: "penultimate"
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@ -0,0 +1,140 @@
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import collections
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import gc
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import os.path
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import traceback
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from collections import namedtuple
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import torch
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from ldm.util import instantiate_from_config
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from omegaconf import OmegaConf
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from modules import shared, devices, script_callbacks, sd_models
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from modules.paths import models_path
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from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
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from modules.sd_models import select_checkpoint, load_model_weights
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print("Fixing all the things that could have just been a pull request!")
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model_dir = "Stable-diffusion"
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model_path = os.path.abspath(os.path.join(models_path, model_dir))
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CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
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checkpoints_list = {}
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checkpoints_loaded = collections.OrderedDict()
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def get_config(checkpoint_info):
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path = checkpoint_info[0]
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model_config = checkpoint_info.config
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checkpoint_dir = os.path.join(shared.script_path, "extensions", "sd_auto_fix", "configs")
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if model_config == shared.cmd_opts.config:
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try:
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checkpoint = torch.load(path)
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c_dict = checkpoint["state_dict"] if "state_dict" in checkpoint else checkpoint
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v2_key = "cond_stage_model.model.ln_final.weight"
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if v2_key in c_dict:
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if "global_step" in checkpoint and checkpoint_info.config == shared.cmd_opts.config:
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if checkpoint["global_step"] == 875000 or checkpoint["global_step"] == 220000:
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model_config = os.path.join(checkpoint_dir, "v2-inference.yaml")
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else:
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model_config = os.path.join(checkpoint_dir, "v2-inference-v.yaml")
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print(f"V2 Model detected, selecting model config: {model_config}")
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del checkpoint
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except Exception as e:
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print(f"Exception: {e}")
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traceback.print_exc()
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pass
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return model_config
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def load_model(checkpoint_info=None):
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from modules import lowvram, sd_hijack
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checkpoint_info = checkpoint_info or select_checkpoint()
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model_config = get_config(checkpoint_info)
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if model_config != shared.cmd_opts.config:
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print(f"Loading config from: {model_config}")
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if shared.sd_model:
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sd_hijack.model_hijack.undo_hijack(shared.sd_model)
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shared.sd_model = None
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gc.collect()
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devices.torch_gc()
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sd_config = OmegaConf.load(model_config)
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if should_hijack_inpainting(checkpoint_info):
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# Hardcoded config for now...
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sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
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sd_config.model.params.use_ema = False
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sd_config.model.params.conditioning_key = "hybrid"
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sd_config.model.params.unet_config.params.in_channels = 9
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# Create a "fake" config with a different name so that we know to unload it when switching models.
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checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
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do_inpainting_hijack()
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if shared.cmd_opts.no_half:
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sd_config.model.params.unet_config.params.use_fp16 = False
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sd_model = instantiate_from_config(sd_config.model)
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load_model_weights(sd_model, checkpoint_info)
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
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else:
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sd_model.to(shared.device)
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sd_hijack.model_hijack.hijack(sd_model)
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sd_model.eval()
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shared.sd_model = sd_model
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script_callbacks.model_loaded_callback(sd_model)
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print(f"Model loaded.")
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return sd_model
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def reload_model_weights(sd_model=None, info=None):
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from modules import lowvram, devices, sd_hijack
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checkpoint_info = info or select_checkpoint()
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if not sd_model:
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sd_model = shared.sd_model
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if sd_model.sd_model_checkpoint == checkpoint_info.filename:
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return
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model_config = get_config(checkpoint_info)
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checkpoint_info = checkpoint_info._replace(config=model_config)
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if sd_model.sd_checkpoint_info.config != model_config or should_hijack_inpainting(
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checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
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del sd_model
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checkpoints_loaded.clear()
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load_model(checkpoint_info)
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return shared.sd_model
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.send_everything_to_cpu()
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else:
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sd_model.to(devices.cpu)
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sd_hijack.model_hijack.undo_hijack(sd_model)
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load_model_weights(sd_model, checkpoint_info)
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sd_hijack.model_hijack.hijack(sd_model)
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script_callbacks.model_loaded_callback(sd_model)
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_model.to(devices.device)
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print(f"Weights loaded.")
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return sd_model
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# This is so effing ridiculous that we have to do this $hit.
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sd_models.load_model = load_model
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sd_models.reload_model_weights = reload_model_weights
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Loading…
Reference in New Issue