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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,73 @@ | ||
| model: | ||
| base_learning_rate: 1.0e-04 | ||
| 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 # Note: different from the one we trained before | ||
| conditioning_key: crossattn | ||
| monitor: val/loss_simple_ema | ||
| scale_factor: 0.18215 | ||
| use_ema: False | ||
|
|
||
| scheduler_config: # 10000 warmup steps | ||
| target: ldm.lr_scheduler.LambdaLinearScheduler | ||
| params: | ||
| warm_up_steps: [ 10000 ] | ||
| cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases | ||
| f_start: [ 1.e-6 ] | ||
| f_max: [ 1. ] | ||
| f_min: [ 1. ] | ||
|
|
||
| unet_config: | ||
| target: ldm.modules.diffusionmodules.openaimodel.UNetModel | ||
| params: | ||
| 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 | ||
| use_spatial_transformer: True | ||
| use_linear_in_transformer: True | ||
| transformer_depth: 1 | ||
| context_dim: 1024 | ||
| use_checkpoint: True | ||
| legacy: False | ||
|
|
||
| first_stage_config: | ||
| target: ldm.models.autoencoder.AutoencoderKL | ||
| params: | ||
| embed_dim: 4 | ||
| monitor: val/rec_loss | ||
| ddconfig: | ||
| 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: modules.xlmr_m18.BertSeriesModelWithTransformation | ||
| params: | ||
| name: "XLMR-Large" |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,33 @@ | ||
| import sys | ||
| import copy | ||
| import logging | ||
|
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|
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| class ColoredFormatter(logging.Formatter): | ||
| COLORS = { | ||
| "DEBUG": "\033[0;36m", # CYAN | ||
| "INFO": "\033[0;32m", # GREEN | ||
| "WARNING": "\033[0;33m", # YELLOW | ||
| "ERROR": "\033[0;31m", # RED | ||
| "CRITICAL": "\033[0;37;41m", # WHITE ON RED | ||
| "RESET": "\033[0m", # RESET COLOR | ||
| } | ||
|
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| def format(self, record): | ||
| colored_record = copy.copy(record) | ||
| levelname = colored_record.levelname | ||
| seq = self.COLORS.get(levelname, self.COLORS["RESET"]) | ||
| colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}" | ||
| return super().format(colored_record) | ||
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| logger = logging.getLogger("lora") | ||
| logger.propagate = False | ||
|
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|
|
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| if not logger.handlers: | ||
| handler = logging.StreamHandler(sys.stdout) | ||
| handler.setFormatter( | ||
| ColoredFormatter("[%(name)s]-%(levelname)s: %(message)s") | ||
| ) | ||
| logger.addHandler(handler) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,33 @@ | ||
|
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||
| import network | ||
|
|
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| class ModuleTypeGLora(network.ModuleType): | ||
| def create_module(self, net: network.Network, weights: network.NetworkWeights): | ||
| if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]): | ||
| return NetworkModuleGLora(net, weights) | ||
|
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| return None | ||
|
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| # adapted from https://github.com/KohakuBlueleaf/LyCORIS | ||
| class NetworkModuleGLora(network.NetworkModule): | ||
| def __init__(self, net: network.Network, weights: network.NetworkWeights): | ||
| super().__init__(net, weights) | ||
|
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| if hasattr(self.sd_module, 'weight'): | ||
| self.shape = self.sd_module.weight.shape | ||
|
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| self.w1a = weights.w["a1.weight"] | ||
| self.w1b = weights.w["b1.weight"] | ||
| self.w2a = weights.w["a2.weight"] | ||
| self.w2b = weights.w["b2.weight"] | ||
|
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| def calc_updown(self, orig_weight): | ||
| w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype) | ||
| w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype) | ||
| w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype) | ||
| w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype) | ||
|
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| output_shape = [w1a.size(0), w1b.size(1)] | ||
| updown = ((w2b @ w1b) + ((orig_weight @ w2a) @ w1a)) | ||
|
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| return self.finalize_updown(updown, orig_weight, output_shape) |
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