Merge pull request #292 from bmaltais/dev
Fix again the custom model config load
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bmaltais
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@ -272,7 +272,7 @@ def save_inference_file(output_dir, v2, v_parameterization, output_name):
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)
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def set_pretrained_model_name_or_path_input(value, pretrained_model_name_or_path, v2, v_parameterization):
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def set_pretrained_model_name_or_path_input(model_list, pretrained_model_name_or_path, v2, v_parameterization):
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# define a list of substrings to search for
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substrings_v2 = [
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'stabilityai/stable-diffusion-2-1-base',
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@ -280,12 +280,12 @@ def set_pretrained_model_name_or_path_input(value, pretrained_model_name_or_path
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]
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# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
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if str(value) in substrings_v2:
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if str(model_list) in substrings_v2:
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print('SD v2 model detected. Setting --v2 parameter')
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v2 = True
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v_parameterization = False
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return value, v2, v_parameterization
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return model_list, v2, v_parameterization
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# define a list of substrings to search for v-objective
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substrings_v_parameterization = [
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@ -294,14 +294,14 @@ def set_pretrained_model_name_or_path_input(value, pretrained_model_name_or_path
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]
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# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_parameterization list
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if str(value) in substrings_v_parameterization:
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if str(model_list) in substrings_v_parameterization:
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print(
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'SD v2 v_parameterization detected. Setting --v2 parameter and --v_parameterization'
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)
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v2 = True
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v_parameterization = True
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return value, v2, v_parameterization
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return model_list, v2, v_parameterization
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# define a list of substrings to v1.x
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substrings_v1_model = [
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@ -309,19 +309,18 @@ def set_pretrained_model_name_or_path_input(value, pretrained_model_name_or_path
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'runwayml/stable-diffusion-v1-5',
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]
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if str(value) in substrings_v1_model:
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if str(model_list) in substrings_v1_model:
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v2 = False
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v_parameterization = False
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return value, v2, v_parameterization
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return model_list, v2, v_parameterization
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if value == 'custom':
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if model_list == 'custom':
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if str(pretrained_model_name_or_path) in substrings_v1_model or str(pretrained_model_name_or_path) in substrings_v2 or str(pretrained_model_name_or_path) in substrings_v_parameterization:
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value = ''
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pretrained_model_name_or_path = ''
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v2 = False
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v_parameterization = False
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return value, v2, v_parameterization
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return pretrained_model_name_or_path, v2, v_parameterization
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###
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### Gradio common GUI section
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@ -3,11 +3,11 @@
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# Thanks to cloneofsimo and kohya
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import argparse
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import os
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import torch
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from safetensors.torch import load_file, save_file, safe_open
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from tqdm import tqdm
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from library import train_util, model_util
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import numpy as np
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def load_state_dict(file_name, dtype):
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@ -38,13 +38,34 @@ def save_to_file(file_name, model, state_dict, dtype, metadata):
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torch.save(model, file_name)
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def resize_lora_model(lora_sd, new_rank, save_dtype, device, sv_ratio, verbose):
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def index_sv_cumulative(S, target):
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original_sum = float(torch.sum(S))
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cumulative_sums = torch.cumsum(S, dim=0)/original_sum
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index = int(torch.searchsorted(cumulative_sums, target)) + 1
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if index >= len(S):
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index = len(S) - 1
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return index
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def index_sv_fro(S, target):
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S_squared = S.pow(2)
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s_fro_sq = float(torch.sum(S_squared))
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sum_S_squared = torch.cumsum(S_squared, dim=0)/s_fro_sq
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index = int(torch.searchsorted(sum_S_squared, target**2)) + 1
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if index >= len(S):
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index = len(S) - 1
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return index
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def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose):
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network_alpha = None
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network_dim = None
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verbose_str = "\n"
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ratio_flag = False
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fro_list = []
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CLAMP_QUANTILE = 0.99
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CLAMP_QUANTILE = 1 # 0.99
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# Extract loaded lora dim and alpha
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for key, value in lora_sd.items():
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@ -58,12 +79,12 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, sv_ratio, verbose):
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network_alpha = network_dim
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scale = network_alpha/network_dim
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if not sv_ratio:
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if dynamic_method:
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print(f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}")
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else:
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new_alpha = float(scale*new_rank) # calculate new alpha from scale
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print(f"old dimension: {network_dim}, old alpha: {network_alpha}, new dim: {new_rank}, new alpha: {new_alpha}")
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else:
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print(f"Dynamically determining new alphas and dims based off sv ratio: {sv_ratio}")
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ratio_flag = True
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lora_down_weight = None
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lora_up_weight = None
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@ -101,22 +122,43 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, sv_ratio, verbose):
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U, S, Vh = torch.linalg.svd(full_weight_matrix)
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if ratio_flag:
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# Calculate new dim and alpha for dynamic sizing
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if dynamic_method=="sv_ratio":
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# Calculate new dim and alpha based off ratio
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max_sv = S[0]
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min_sv = max_sv/sv_ratio
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min_sv = max_sv/dynamic_param
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new_rank = torch.sum(S > min_sv).item()
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new_rank = max(new_rank, 1)
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new_alpha = float(scale*new_rank)
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elif dynamic_method=="sv_cumulative":
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# Calculate new dim and alpha based off cumulative sum
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new_rank = index_sv_cumulative(S, dynamic_param)
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new_rank = max(new_rank, 1)
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new_alpha = float(scale*new_rank)
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elif dynamic_method=="sv_fro":
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# Calculate new dim and alpha based off sqrt sum of squares
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new_rank = index_sv_fro(S, dynamic_param)
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new_rank = max(new_rank, 1)
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new_alpha = float(scale*new_rank)
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if verbose:
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s_sum = torch.sum(torch.abs(S))
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s_rank = torch.sum(torch.abs(S[:new_rank]))
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verbose_str+=f"{block_down_name:75} | "
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verbose_str+=f"sum(S) retained: {(s_rank)/s_sum:.1%}, max(S) ratio: {S[0]/S[new_rank]:0.1f}"
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S_squared = S.pow(2)
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s_fro = torch.sqrt(torch.sum(S_squared))
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s_red_fro = torch.sqrt(torch.sum(S_squared[:new_rank]))
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fro_percent = float(s_red_fro/s_fro)
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if not np.isnan(fro_percent):
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fro_list.append(float(fro_percent))
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if verbose and ratio_flag:
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verbose_str+=f", dynamic| dim: {new_rank}, alpha: {new_alpha}\n"
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verbose_str+=f"{block_down_name:75} | "
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verbose_str+=f"sum(S) retained: {(s_rank)/s_sum:.1%}, fro retained: {fro_percent:.1%}, max(S) ratio: {S[0]/S[new_rank]:0.1f}"
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if verbose and dynamic_method:
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verbose_str+=f", dynamic | dim: {new_rank}, alpha: {new_alpha}\n"
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else:
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verbose_str+=f"\n"
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@ -153,6 +195,8 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, sv_ratio, verbose):
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if verbose:
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print(verbose_str)
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print(f"Average Frobenius norm retention: {np.mean(fro_list):.2%} | std: {np.std(fro_list):0.3f}")
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print("resizing complete")
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return o_lora_sd, network_dim, new_alpha
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@ -168,6 +212,9 @@ def resize(args):
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return torch.bfloat16
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return None
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if args.dynamic_method and not args.dynamic_param:
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raise Exception("If using dynamic_method, then dynamic_param is required")
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merge_dtype = str_to_dtype('float') # matmul method above only seems to work in float32
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save_dtype = str_to_dtype(args.save_precision)
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if save_dtype is None:
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@ -177,19 +224,20 @@ def resize(args):
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lora_sd, metadata = load_state_dict(args.model, merge_dtype)
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print("resizing rank...")
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state_dict, old_dim, new_alpha = resize_lora_model(lora_sd, args.new_rank, save_dtype, args.device, args.sv_ratio, args.verbose)
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state_dict, old_dim, new_alpha = resize_lora_model(lora_sd, args.new_rank, save_dtype, args.device, args.dynamic_method, args.dynamic_param, args.verbose)
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# update metadata
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if metadata is None:
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metadata = {}
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comment = metadata.get("ss_training_comment", "")
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if not args.sv_ratio:
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if not args.dynamic_method:
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metadata["ss_training_comment"] = f"dimension is resized from {old_dim} to {args.new_rank}; {comment}"
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metadata["ss_network_dim"] = str(args.new_rank)
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metadata["ss_network_alpha"] = str(new_alpha)
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else:
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metadata["ss_training_comment"] = f"Dynamic resize from {old_dim} with ratio {args.sv_ratio}; {comment}"
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metadata["ss_training_comment"] = f"Dynamic resize with {args.dynamic_method}: {args.dynamic_param} from {old_dim}; {comment}"
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metadata["ss_network_dim"] = 'Dynamic'
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metadata["ss_network_alpha"] = 'Dynamic'
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@ -215,8 +263,11 @@ if __name__ == '__main__':
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parser.add_argument("--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う")
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parser.add_argument("--verbose", action="store_true",
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help="Display verbose resizing information / rank変更時の詳細情報を出力する")
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parser.add_argument("--sv_ratio", type=float, default=None,
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help="Specify svd ratio for dim calcs. Will override --new_rank")
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parser.add_argument("--dynamic_method", type=str, default=None, choices=[None, "sv_ratio", "sv_fro", "sv_cumulative"],
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help="Specify dynamic resizing method, will override --new_rank")
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parser.add_argument("--dynamic_param", type=float, default=None,
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help="Specify target for dynamic reduction")
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args = parser.parse_args()
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resize(args)
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