213 lines
10 KiB
Python
213 lines
10 KiB
Python
import io
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import torch
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import requests
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import numpy as np
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from PIL import Image
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from omegaconf import OmegaConf
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from torchvision.transforms import ToTensor
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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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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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DDPMScheduler,
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DEISMultistepScheduler,
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DPMSolverMultistepScheduler,
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DPMSolverSinglestepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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HeunDiscreteScheduler,
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KDPM2AncestralDiscreteScheduler,
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KDPM2DiscreteScheduler,
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UniPCMultistepScheduler,
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)
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SCHEDULERS = {
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'DDIM' : DDIMScheduler,
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'DDPM' : DDPMScheduler,
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'DEISMultistep' : DEISMultistepScheduler,
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'DPMSolverMultistep' : DPMSolverMultistepScheduler,
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'DPMSolverSinglestep' : DPMSolverSinglestepScheduler,
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'EulerAncestralDiscrete' : EulerAncestralDiscreteScheduler,
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'EulerDiscrete' : EulerDiscreteScheduler,
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'HeunDiscrete' : HeunDiscreteScheduler,
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'KDPM2AncestralDiscrete' : KDPM2AncestralDiscreteScheduler,
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'KDPM2Discrete' : KDPM2DiscreteScheduler,
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'UniPCMultistep' : UniPCMultistepScheduler
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}
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def token_auto_concat_embeds(pipe, positive, negative):
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max_length = pipe.tokenizer.model_max_length
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positive_length = pipe.tokenizer(positive, return_tensors="pt").input_ids.shape[-1]
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negative_length = pipe.tokenizer(negative, return_tensors="pt").input_ids.shape[-1]
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print(f'Token length is model maximum: {max_length}, positive length: {positive_length}, negative length: {negative_length}.')
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if max_length < positive_length or max_length < negative_length:
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print('Concatenated embedding.')
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if positive_length > negative_length:
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positive_ids = pipe.tokenizer(positive, return_tensors="pt").input_ids.to("cuda")
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negative_ids = pipe.tokenizer(negative, truncation=False, padding="max_length", max_length=positive_ids.shape[-1], return_tensors="pt").input_ids.to("cuda")
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else:
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negative_ids = pipe.tokenizer(negative, return_tensors="pt").input_ids.to("cuda")
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positive_ids = pipe.tokenizer(positive, truncation=False, padding="max_length", max_length=negative_ids.shape[-1], return_tensors="pt").input_ids.to("cuda")
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else:
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positive_ids = pipe.tokenizer(positive, truncation=False, padding="max_length", max_length=max_length, return_tensors="pt").input_ids.to("cuda")
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negative_ids = pipe.tokenizer(negative, truncation=False, padding="max_length", max_length=max_length, return_tensors="pt").input_ids.to("cuda")
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positive_concat_embeds = []
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negative_concat_embeds = []
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for i in range(0, positive_ids.shape[-1], max_length):
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positive_concat_embeds.append(pipe.text_encoder(positive_ids[:, i: i + max_length])[0])
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negative_concat_embeds.append(pipe.text_encoder(negative_ids[:, i: i + max_length])[0])
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positive_prompt_embeds = torch.cat(positive_concat_embeds, dim=1)
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negative_prompt_embeds = torch.cat(negative_concat_embeds, dim=1)
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return positive_prompt_embeds, negative_prompt_embeds
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# Reference from : https://github.com/huggingface/diffusers/blob/main/scripts/convert_vae_pt_to_diffusers.py
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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["encoder.conv_out.weight"]
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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["encoder.norm_out.weight"]
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new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
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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["decoder.conv_out.weight"]
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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["decoder.norm_out.weight"]
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new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
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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({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
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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] 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({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
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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] 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 = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
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if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
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new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
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f"encoder.down.{i}.downsample.conv.weight"
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)
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new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
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f"encoder.down.{i}.downsample.conv.bias"
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)
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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(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
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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(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
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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(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
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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 for key in up_blocks[block_id] 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[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
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f"decoder.up.{block_id}.upsample.conv.weight"
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]
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new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
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f"decoder.up.{block_id}.upsample.conv.bias"
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]
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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(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
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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(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
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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(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
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conv_attn_to_linear(new_checkpoint)
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return new_checkpoint
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# Reference from : https://github.com/huggingface/diffusers/blob/main/scripts/convert_vae_pt_to_diffusers.py
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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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if checkpoint_path.endswith("safetensors"):
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from safetensors import safe_open
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checkpoint = {}
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with safe_open(checkpoint_path, framework="pt", device="cpu") as f:
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for key in f.keys():
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checkpoint[key] = f.get_tensor(key)
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else:
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checkpoint = torch.load(checkpoint_path, map_location=device)["state_dict"]
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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(checkpoint, vae_config)
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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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def convert_images_to_tensors(images: list[Image.Image]):
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return torch.stack([np.transpose(ToTensor()(image), (1, 2, 0)) for image in images])
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def convert_tensors_to_images(images: torch.tensor):
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return [Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8)) for image in images]
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def resize_images(images: list[Image.Image], size: tuple[int, int]):
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return [image.resize(size) for image in images] |