🎨 完整的 IOPaint 项目更新
## 主要更新 - ✨ 更新所有依赖到最新稳定版本 - 📝 添加详细的项目文档和模型推荐 - 🔧 配置 VSCode Cloud Studio 预览功能 - 🐛 修复 PyTorch API 弃用警告 ## 依赖更新 - diffusers: 0.27.2 → 0.35.2 - gradio: 4.21.0 → 5.46.0 - peft: 0.7.1 → 0.18.0 - Pillow: 9.5.0 → 11.3.0 - fastapi: 0.108.0 → 0.116.2 ## 新增文件 - CLAUDE.md - 项目架构和开发指南 - UPGRADE_NOTES.md - 详细的升级说明 - .vscode/preview.yml - 预览配置 - .vscode/LAUNCH_GUIDE.md - 启动指南 - .gitignore - 更新的忽略规则 ## 代码修复 - 修复 iopaint/model/ldm.py 中的 torch.cuda.amp.autocast() 弃用警告 ## 文档更新 - README.md - 添加模型推荐和使用指南 - 完整的项目源码（iopaint/） - Web 前端源码（web_app/） 🤖 Generated with Claude Code
This commit is contained in:
let5sne
2
scripts/.gitignore
vendored
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2
scripts/.gitignore
vendored
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lama-cleaner/
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*.zip
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3
scripts/README.md
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3
scripts/README.md
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# IOPaint Windows 1-Click Installer
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https://www.iopaint.com/install/windows_1click_installer
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231
scripts/convert_vae_pt_to_diffusers.py
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231
scripts/convert_vae_pt_to_diffusers.py
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import argparse
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import io
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import requests
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import torch
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from omegaconf import OmegaConf
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from diffusers import AutoencoderKL
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from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
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assign_to_checkpoint,
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conv_attn_to_linear,
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create_vae_diffusers_config,
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renew_vae_attention_paths,
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renew_vae_resnet_paths,
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)
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def custom_convert_ldm_vae_checkpoint(checkpoint, config):
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vae_state_dict = checkpoint
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new_checkpoint = {}
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new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
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new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
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new_checkpoint["encoder.conv_out.weight"] = vae_state_dict[
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"encoder.conv_out.weight"
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]
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new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
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new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict[
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"encoder.norm_out.weight"
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]
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new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict[
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"encoder.norm_out.bias"
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]
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new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
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new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
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new_checkpoint["decoder.conv_out.weight"] = vae_state_dict[
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"decoder.conv_out.weight"
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]
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new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
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new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict[
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"decoder.norm_out.weight"
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]
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new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict[
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"decoder.norm_out.bias"
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]
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new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
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new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
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new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
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new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
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# Retrieves the keys for the encoder down blocks only
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num_down_blocks = len(
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{
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".".join(layer.split(".")[:3])
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for layer in vae_state_dict
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if "encoder.down" in layer
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}
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)
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down_blocks = {
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layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key]
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for layer_id in range(num_down_blocks)
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}
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# Retrieves the keys for the decoder up blocks only
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num_up_blocks = len(
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{
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".".join(layer.split(".")[:3])
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for layer in vae_state_dict
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if "decoder.up" in layer
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}
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)
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up_blocks = {
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layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key]
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for layer_id in range(num_up_blocks)
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}
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for i in range(num_down_blocks):
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resnets = [
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key
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for key in down_blocks[i]
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if f"down.{i}" in key and f"down.{i}.downsample" not in key
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]
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if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
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new_checkpoint[
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f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"
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] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.weight")
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new_checkpoint[
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f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"
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] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.bias")
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paths = renew_vae_resnet_paths(resnets)
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meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
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assign_to_checkpoint(
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paths,
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new_checkpoint,
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vae_state_dict,
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additional_replacements=[meta_path],
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config=config,
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)
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mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
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num_mid_res_blocks = 2
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for i in range(1, num_mid_res_blocks + 1):
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resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
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paths = renew_vae_resnet_paths(resnets)
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meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
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assign_to_checkpoint(
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paths,
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new_checkpoint,
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vae_state_dict,
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additional_replacements=[meta_path],
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config=config,
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)
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mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
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paths = renew_vae_attention_paths(mid_attentions)
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meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
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assign_to_checkpoint(
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paths,
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new_checkpoint,
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vae_state_dict,
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additional_replacements=[meta_path],
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config=config,
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)
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conv_attn_to_linear(new_checkpoint)
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for i in range(num_up_blocks):
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block_id = num_up_blocks - 1 - i
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resnets = [
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key
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for key in up_blocks[block_id]
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if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
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]
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if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
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new_checkpoint[
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f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"
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] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.weight"]
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new_checkpoint[
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f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"
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] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.bias"]
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paths = renew_vae_resnet_paths(resnets)
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meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
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assign_to_checkpoint(
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paths,
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new_checkpoint,
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vae_state_dict,
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additional_replacements=[meta_path],
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config=config,
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)
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mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
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num_mid_res_blocks = 2
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for i in range(1, num_mid_res_blocks + 1):
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resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
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paths = renew_vae_resnet_paths(resnets)
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meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
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assign_to_checkpoint(
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paths,
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new_checkpoint,
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vae_state_dict,
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additional_replacements=[meta_path],
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config=config,
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)
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mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
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paths = renew_vae_attention_paths(mid_attentions)
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meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
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assign_to_checkpoint(
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paths,
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new_checkpoint,
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vae_state_dict,
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additional_replacements=[meta_path],
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config=config,
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)
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conv_attn_to_linear(new_checkpoint)
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return new_checkpoint
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def vae_pt_to_vae_diffuser(
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checkpoint_path: str,
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output_path: str,
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):
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# Only support V1
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r = requests.get(
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" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
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)
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io_obj = io.BytesIO(r.content)
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original_config = OmegaConf.load(io_obj)
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image_size = 512
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device = "cuda" if torch.cuda.is_available() else "cpu"
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checkpoint = torch.load(checkpoint_path, map_location=device)
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# Convert the VAE model.
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vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
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converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(
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checkpoint["state_dict"], vae_config
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)
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vae = AutoencoderKL(**vae_config)
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vae.load_state_dict(converted_vae_checkpoint)
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vae.save_pretrained(output_path)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--vae_pt_path",
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default="/Users/cwq/code/github/lama-cleaner/scripts/anything-v4.0.vae.pt",
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type=str,
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help="Path to the VAE.pt to convert.",
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)
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parser.add_argument(
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"--dump_path",
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default="diffusion_pytorch_model.bin",
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type=str,
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help="Path to the VAE.pt to convert.",
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)
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args = parser.parse_args()
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vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
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11
scripts/environment.yaml
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11
scripts/environment.yaml
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name: lama-cleaner
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channels:
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- defaults
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- conda-forge
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dependencies:
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- conda
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- git
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- git-lfs
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- python=3.10
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- invoke
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- rich
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30
scripts/pack.bat
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30
scripts/pack.bat
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@echo off
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set "PYTHONNOUSERSITE=1"
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SET BUILD_DIST=lama-cleaner
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SET BUILD_ENV=installer
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SET USER_SCRIPTS=user_scripts
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echo Creating a distributable package..
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@call conda env create --prefix %BUILD_ENV% -f environment.yaml
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echo Finish creating environment
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@call conda activate .\%BUILD_ENV%
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@call conda install -c conda-forge -y conda-pack
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@call conda pack --n-threads -1 --prefix %BUILD_ENV% --format tar
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mkdir %BUILD_DIST%\%BUILD_ENV%
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echo "Copy user scripts file %USER_SCRIPTS%"
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copy %USER_SCRIPTS%\* %BUILD_DIST%
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cd %BUILD_DIST%
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@call tar -xf ..\%BUILD_ENV%.tar -C %BUILD_ENV%
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cd ..
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@call conda deactivate
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rmdir /s /q %BUILD_ENV%
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del %BUILD_ENV%.tar
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38
scripts/pack.sh
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38
scripts/pack.sh
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#!/bin/bash
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# Prepare basic python environment
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set -e
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# Ensuer not use user's python package
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export PYTHONNOUSERSITE=1
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BUILD_DIST=lama-cleaner
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BUILD_ENV=installer
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USER_SCRIPTS=user_scripts
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echo "Creating a distributable package.."
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source ~/miniconda3/etc/profile.d/conda.sh
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conda install -c conda-forge -y conda-pack
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conda env create --prefix $BUILD_ENV -f environment.yaml
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conda activate ./$BUILD_ENV
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conda pack --n-threads -1 --prefix $BUILD_ENV --format tar
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mkdir -p ${BUILD_DIST}/$BUILD_ENV
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echo "Copy user scripts file ${USER_SCRIPTS}"
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cp ${USER_SCRIPTS}/* $BUILD_DIST
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cd $BUILD_DIST
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tar -xf ../${BUILD_ENV}.tar -C $BUILD_ENV
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cd ..
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rm -rf $BUILD_ENV
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rm ${BUILD_ENV}.tar
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echo "zip ${BUILD_DIST}.zip"
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zip -q -r $BUILD_DIST.zip $BUILD_DIST
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361
scripts/tool.py
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361
scripts/tool.py
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import glob
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import os
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from typing import Dict, List, Union
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import torch
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from diffusers.utils import is_safetensors_available
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from huggingface_hub.constants import HF_HUB_CACHE
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if is_safetensors_available():
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import safetensors.torch
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from huggingface_hub import snapshot_download
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from diffusers import DiffusionPipeline, __version__
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from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
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from diffusers.utils import (
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CONFIG_NAME,
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ONNX_WEIGHTS_NAME,
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WEIGHTS_NAME,
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)
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class CheckpointMergerPipeline(DiffusionPipeline):
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"""
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A class that supports merging diffusion models based on the discussion here:
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https://github.com/huggingface/diffusers/issues/877
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Example usage:-
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pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger.py")
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merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","prompthero/openjourney"], interp = 'inv_sigmoid', alpha = 0.8, force = True)
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merged_pipe.to('cuda')
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prompt = "An astronaut riding a unicycle on Mars"
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results = merged_pipe(prompt)
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## For more details, see the docstring for the merge method.
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"""
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def __init__(self):
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self.register_to_config()
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super().__init__()
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def _compare_model_configs(self, dict0, dict1):
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if dict0 == dict1:
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return True
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else:
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config0, meta_keys0 = self._remove_meta_keys(dict0)
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config1, meta_keys1 = self._remove_meta_keys(dict1)
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if config0 == config1:
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print(f"Warning !: Mismatch in keys {meta_keys0} and {meta_keys1}.")
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return True
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return False
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def _remove_meta_keys(self, config_dict: Dict):
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meta_keys = []
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||||
temp_dict = config_dict.copy()
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||||
for key in config_dict.keys():
|
||||
if key.startswith("_"):
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||||
temp_dict.pop(key)
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||||
meta_keys.append(key)
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||||
return (temp_dict, meta_keys)
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||||
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||||
@torch.no_grad()
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def merge(
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self,
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pretrained_model_name_or_path_list: List[Union[str, os.PathLike]],
|
||||
**kwargs,
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||||
):
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||||
"""
|
||||
Returns a new pipeline object of the class 'DiffusionPipeline' with the merged checkpoints(weights) of the models passed
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||||
in the argument 'pretrained_model_name_or_path_list' as a list.
|
||||
|
||||
Parameters:
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||||
-----------
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||||
pretrained_model_name_or_path_list : A list of valid pretrained model names in the HuggingFace hub or paths to locally stored models in the HuggingFace format.
|
||||
|
||||
**kwargs:
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||||
Supports all the default DiffusionPipeline.get_config_dict kwargs viz..
|
||||
|
||||
cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map.
|
||||
|
||||
alpha - The interpolation parameter. Ranges from 0 to 1. It affects the ratio in which the checkpoints are merged. A 0.8 alpha
|
||||
would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2
|
||||
|
||||
interp - The interpolation method to use for the merging. Supports "sigmoid", "inv_sigmoid", "add_diff" and None.
|
||||
Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_diff" is supported.
|
||||
|
||||
force - Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
|
||||
|
||||
"""
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||||
# Default kwargs from DiffusionPipeline
|
||||
cache_dir = kwargs.pop("cache_dir", HF_HUB_CACHE)
|
||||
resume_download = kwargs.pop("resume_download", False)
|
||||
force_download = kwargs.pop("force_download", False)
|
||||
proxies = kwargs.pop("proxies", None)
|
||||
local_files_only = kwargs.pop("local_files_only", False)
|
||||
use_auth_token = kwargs.pop("use_auth_token", None)
|
||||
revision = kwargs.pop("revision", None)
|
||||
torch_dtype = kwargs.pop("torch_dtype", None)
|
||||
device_map = kwargs.pop("device_map", None)
|
||||
|
||||
alpha = kwargs.pop("alpha", 0.5)
|
||||
interp = kwargs.pop("interp", None)
|
||||
|
||||
print("Received list", pretrained_model_name_or_path_list)
|
||||
print(f"Combining with alpha={alpha}, interpolation mode={interp}")
|
||||
|
||||
checkpoint_count = len(pretrained_model_name_or_path_list)
|
||||
# Ignore result from model_index_json comparision of the two checkpoints
|
||||
force = kwargs.pop("force", False)
|
||||
|
||||
# If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
|
||||
if checkpoint_count > 3 or checkpoint_count < 2:
|
||||
raise ValueError(
|
||||
"Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
|
||||
" passed."
|
||||
)
|
||||
|
||||
print("Received the right number of checkpoints")
|
||||
# chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
|
||||
# chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None
|
||||
|
||||
# Validate that the checkpoints can be merged
|
||||
# Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
|
||||
config_dicts = []
|
||||
for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
|
||||
config_dict = DiffusionPipeline.load_config(
|
||||
pretrained_model_name_or_path,
|
||||
cache_dir=cache_dir,
|
||||
resume_download=resume_download,
|
||||
force_download=force_download,
|
||||
proxies=proxies,
|
||||
local_files_only=local_files_only,
|
||||
use_auth_token=use_auth_token,
|
||||
revision=revision,
|
||||
)
|
||||
config_dicts.append(config_dict)
|
||||
|
||||
comparison_result = True
|
||||
for idx in range(1, len(config_dicts)):
|
||||
comparison_result &= self._compare_model_configs(
|
||||
config_dicts[idx - 1], config_dicts[idx]
|
||||
)
|
||||
if not force and comparison_result is False:
|
||||
raise ValueError(
|
||||
"Incompatible checkpoints. Please check model_index.json for the models."
|
||||
)
|
||||
print(config_dicts[0], config_dicts[1])
|
||||
print("Compatible model_index.json files found")
|
||||
# Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
|
||||
cached_folders = []
|
||||
for pretrained_model_name_or_path, config_dict in zip(
|
||||
pretrained_model_name_or_path_list, config_dicts
|
||||
):
|
||||
folder_names = [k for k in config_dict.keys() if not k.startswith("_")]
|
||||
allow_patterns = [os.path.join(k, "*") for k in folder_names]
|
||||
allow_patterns += [
|
||||
WEIGHTS_NAME,
|
||||
SCHEDULER_CONFIG_NAME,
|
||||
CONFIG_NAME,
|
||||
ONNX_WEIGHTS_NAME,
|
||||
DiffusionPipeline.config_name,
|
||||
]
|
||||
requested_pipeline_class = config_dict.get("_class_name")
|
||||
user_agent = {
|
||||
"diffusers": __version__,
|
||||
"pipeline_class": requested_pipeline_class,
|
||||
}
|
||||
|
||||
cached_folder = (
|
||||
pretrained_model_name_or_path
|
||||
if os.path.isdir(pretrained_model_name_or_path)
|
||||
else snapshot_download(
|
||||
pretrained_model_name_or_path,
|
||||
cache_dir=cache_dir,
|
||||
resume_download=resume_download,
|
||||
proxies=proxies,
|
||||
local_files_only=local_files_only,
|
||||
use_auth_token=use_auth_token,
|
||||
revision=revision,
|
||||
allow_patterns=allow_patterns,
|
||||
user_agent=user_agent,
|
||||
)
|
||||
)
|
||||
print("Cached Folder", cached_folder)
|
||||
cached_folders.append(cached_folder)
|
||||
|
||||
# Step 3:-
|
||||
# Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
|
||||
final_pipe = DiffusionPipeline.from_pretrained(
|
||||
cached_folders[0], torch_dtype=torch_dtype, device_map=device_map
|
||||
)
|
||||
final_pipe.to(self.device)
|
||||
|
||||
checkpoint_path_2 = None
|
||||
if len(cached_folders) > 2:
|
||||
checkpoint_path_2 = os.path.join(cached_folders[2])
|
||||
|
||||
if interp == "sigmoid":
|
||||
theta_func = CheckpointMergerPipeline.sigmoid
|
||||
elif interp == "inv_sigmoid":
|
||||
theta_func = CheckpointMergerPipeline.inv_sigmoid
|
||||
elif interp == "add_diff":
|
||||
theta_func = CheckpointMergerPipeline.add_difference
|
||||
else:
|
||||
theta_func = CheckpointMergerPipeline.weighted_sum
|
||||
|
||||
# Find each module's state dict.
|
||||
for attr in final_pipe.config.keys():
|
||||
if not attr.startswith("_"):
|
||||
checkpoint_path_1 = os.path.join(cached_folders[1], attr)
|
||||
if os.path.exists(checkpoint_path_1):
|
||||
files = list(
|
||||
(
|
||||
*glob.glob(
|
||||
os.path.join(checkpoint_path_1, "*.safetensors")
|
||||
),
|
||||
*glob.glob(os.path.join(checkpoint_path_1, "*.bin")),
|
||||
)
|
||||
)
|
||||
checkpoint_path_1 = files[0] if len(files) > 0 else None
|
||||
if len(cached_folders) < 3:
|
||||
checkpoint_path_2 = None
|
||||
else:
|
||||
checkpoint_path_2 = os.path.join(cached_folders[2], attr)
|
||||
if os.path.exists(checkpoint_path_2):
|
||||
files = list(
|
||||
(
|
||||
*glob.glob(
|
||||
os.path.join(checkpoint_path_2, "*.safetensors")
|
||||
),
|
||||
*glob.glob(os.path.join(checkpoint_path_2, "*.bin")),
|
||||
)
|
||||
)
|
||||
checkpoint_path_2 = files[0] if len(files) > 0 else None
|
||||
# For an attr if both checkpoint_path_1 and 2 are None, ignore.
|
||||
# If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
|
||||
if checkpoint_path_1 is None and checkpoint_path_2 is None:
|
||||
print(f"Skipping {attr}: not present in 2nd or 3d model")
|
||||
continue
|
||||
|
||||
try:
|
||||
module = getattr(final_pipe, attr)
|
||||
if isinstance(
|
||||
module, bool
|
||||
): # ignore requires_safety_checker boolean
|
||||
continue
|
||||
theta_0 = getattr(module, "state_dict")
|
||||
theta_0 = theta_0()
|
||||
|
||||
update_theta_0 = getattr(module, "load_state_dict")
|
||||
|
||||
theta_1 = (
|
||||
safetensors.torch.load_file(checkpoint_path_1)
|
||||
if (
|
||||
is_safetensors_available()
|
||||
and checkpoint_path_1.endswith(".safetensors")
|
||||
)
|
||||
else torch.load(checkpoint_path_1, map_location="cpu")
|
||||
)
|
||||
|
||||
if attr in ["vae", "text_encoder"]:
|
||||
print(f"Direct use theta1 {attr}: {checkpoint_path_1}")
|
||||
update_theta_0(theta_1)
|
||||
del theta_1
|
||||
del theta_0
|
||||
continue
|
||||
|
||||
theta_2 = None
|
||||
if checkpoint_path_2:
|
||||
theta_2 = (
|
||||
safetensors.torch.load_file(checkpoint_path_2)
|
||||
if (
|
||||
is_safetensors_available()
|
||||
and checkpoint_path_2.endswith(".safetensors")
|
||||
)
|
||||
else torch.load(checkpoint_path_2, map_location="cpu")
|
||||
)
|
||||
|
||||
if not theta_0.keys() == theta_1.keys():
|
||||
print(f"Skipping {attr}: key mismatch")
|
||||
continue
|
||||
if theta_2 and not theta_1.keys() == theta_2.keys():
|
||||
print(f"Skipping {attr}:y mismatch")
|
||||
except Exception as e:
|
||||
print(f"Skipping {attr} do to an unexpected error: {str(e)}")
|
||||
continue
|
||||
print(f"MERGING {attr}")
|
||||
|
||||
for key in theta_0.keys():
|
||||
if theta_2:
|
||||
theta_0[key] = theta_func(
|
||||
theta_0[key], theta_1[key], theta_2[key], alpha
|
||||
)
|
||||
else:
|
||||
theta_0[key] = theta_func(
|
||||
theta_0[key], theta_1[key], None, alpha
|
||||
)
|
||||
|
||||
del theta_1
|
||||
del theta_2
|
||||
update_theta_0(theta_0)
|
||||
|
||||
del theta_0
|
||||
return final_pipe
|
||||
|
||||
@staticmethod
|
||||
def weighted_sum(theta0, theta1, theta2, alpha):
|
||||
return ((1 - alpha) * theta0) + (alpha * theta1)
|
||||
|
||||
# Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
|
||||
@staticmethod
|
||||
def sigmoid(theta0, theta1, theta2, alpha):
|
||||
alpha = alpha * alpha * (3 - (2 * alpha))
|
||||
return theta0 + ((theta1 - theta0) * alpha)
|
||||
|
||||
# Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
|
||||
@staticmethod
|
||||
def inv_sigmoid(theta0, theta1, theta2, alpha):
|
||||
import math
|
||||
|
||||
alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
|
||||
return theta0 + ((theta1 - theta0) * alpha)
|
||||
|
||||
@staticmethod
|
||||
def add_difference(theta0, theta1, theta2, alpha):
|
||||
# theta0 + (theta1 - theta2) * (1.0 - alpha)
|
||||
|
||||
diff = (theta1 - theta2) * (1.0 - alpha)
|
||||
# print(f"theta0.shape: {theta0.shape}, diff shape: {diff.shape}")
|
||||
# theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
|
||||
if theta0.shape != diff.shape:
|
||||
theta0[:, 0:4, :, :] = theta0[:, 0:4, :, :] + diff
|
||||
else:
|
||||
theta0 = theta0 + diff
|
||||
return theta0
|
||||
|
||||
|
||||
pipe = CheckpointMergerPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
|
||||
merged_pipe = pipe.merge(
|
||||
[
|
||||
"runwayml/stable-diffusion-inpainting",
|
||||
# "SG161222/Realistic_Vision_V1.4",
|
||||
"dreamlike-art/dreamlike-diffusion-1.0",
|
||||
"runwayml/stable-diffusion-v1-5",
|
||||
],
|
||||
force=True,
|
||||
interp="add_diff",
|
||||
alpha=0,
|
||||
)
|
||||
|
||||
merged_pipe = merged_pipe.to(torch.float16)
|
||||
merged_pipe.save_pretrained(
|
||||
"dreamlike-diffusion-1.0-inpainting", safe_serialization=True
|
||||
)
|
||||
9
scripts/user_scripts/win_config.bat
Normal file
9
scripts/user_scripts/win_config.bat
Normal file
@@ -0,0 +1,9 @@
|
||||
@echo off
|
||||
|
||||
set PATH=C:\Windows\System32;%PATH%
|
||||
|
||||
@call installer\Scripts\activate.bat
|
||||
|
||||
@call iopaint start-web-config --config-file %0\..\installer_config.json
|
||||
|
||||
PAUSE
|
||||
13
scripts/user_scripts/win_setup.bat
Normal file
13
scripts/user_scripts/win_setup.bat
Normal file
@@ -0,0 +1,13 @@
|
||||
@echo off
|
||||
|
||||
set PATH=C:\Windows\System32;%PATH%
|
||||
|
||||
@call installer\Scripts\activate.bat
|
||||
|
||||
@call conda-unpack
|
||||
|
||||
@call pip install torch==2.1.2 torchvision==0.16.2 --extra-index-url https://download.pytorch.org/whl/cu118
|
||||
@call pip3 install -U iopaint
|
||||
@call iopaint install-plugins-packages
|
||||
|
||||
PAUSE
|
||||
14
scripts/user_scripts/win_setup_cn.bat
Normal file
14
scripts/user_scripts/win_setup_cn.bat
Normal file
@@ -0,0 +1,14 @@
|
||||
@echo off
|
||||
|
||||
set PATH=C:\Windows\System32;%PATH%
|
||||
|
||||
@call installer\Scripts\activate.bat
|
||||
|
||||
@call conda-unpack
|
||||
|
||||
@call pip config set global.extra-index-url "https://pypi.tuna.tsinghua.edu.cn/simple https://mirrors.cloud.tencent.com/pypi/simple"
|
||||
@call pip install torch==2.1.2 torchvision==0.16.2 --extra-index-url https://download.pytorch.org/whl/cu118
|
||||
@call pip3 install -U iopaint
|
||||
@call iopaint install-plugins-packages
|
||||
|
||||
PAUSE
|
||||
9
scripts/user_scripts/win_start.bat
Normal file
9
scripts/user_scripts/win_start.bat
Normal file
@@ -0,0 +1,9 @@
|
||||
@echo off
|
||||
|
||||
set PATH=C:\Windows\System32;%PATH%
|
||||
|
||||
@call installer\Scripts\activate.bat
|
||||
|
||||
@call iopaint start --config %0\..\installer_config.json
|
||||
|
||||
PAUSE
|
||||
10
scripts/user_scripts/win_start_cn.bat
Normal file
10
scripts/user_scripts/win_start_cn.bat
Normal file
@@ -0,0 +1,10 @@
|
||||
@echo off
|
||||
|
||||
set PATH=C:\Windows\System32;%PATH%
|
||||
|
||||
@call installer\Scripts\activate.bat
|
||||
|
||||
@call set HF_ENDPOINT=https://hf-mirror.com
|
||||
@call iopaint start --config %0\..\installer_config.json
|
||||
|
||||
PAUSE
|
||||
9
scripts/user_scripts/win_update.bat
Normal file
9
scripts/user_scripts/win_update.bat
Normal file
@@ -0,0 +1,9 @@
|
||||
@echo off
|
||||
|
||||
set PATH=C:\Windows\System32;%PATH%
|
||||
|
||||
@call installer\Scripts\activate.bat
|
||||
|
||||
@call pip3 install -U iopaint
|
||||
|
||||
PAUSE
|
||||
Reference in New Issue
Block a user