diff --git a/configs/v1-inference.yaml b/configs/v1-inference.yaml
new file mode 100644
index 0000000..e04b839
--- /dev/null
+++ b/configs/v1-inference.yaml
@@ -0,0 +1,70 @@
+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_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        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: ldm.modules.encoders.modules.FrozenCLIPEmbedder
\ No newline at end of file
diff --git a/configs/v2-inference-v.yaml b/configs/v2-inference-v.yaml
new file mode 100644
index 0000000..513cd63
--- /dev/null
+++ b/configs/v2-inference-v.yaml
@@ -0,0 +1,68 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    parameterization: "v"
+    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
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        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 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          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: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"
\ No newline at end of file
diff --git a/configs/v2-inference.yaml b/configs/v2-inference.yaml
new file mode 100644
index 0000000..0eb2539
--- /dev/null
+++ b/configs/v2-inference.yaml
@@ -0,0 +1,67 @@
+model:
+  base_learning_rate: 1.0e-4
+  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
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        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 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          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: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"
\ No newline at end of file
diff --git a/scripts/patch_fixer.py b/scripts/patch_fixer.py
new file mode 100644
index 0000000..8714704
--- /dev/null
+++ b/scripts/patch_fixer.py
@@ -0,0 +1,140 @@
+import collections
+import gc
+import os.path
+import traceback
+from collections import namedtuple
+
+import torch
+from ldm.util import instantiate_from_config
+from omegaconf import OmegaConf
+
+from modules import shared, devices, script_callbacks, sd_models
+from modules.paths import models_path
+from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
+from modules.sd_models import select_checkpoint, load_model_weights
+
+print("Fixing all the things that could have just been a pull request!")
+model_dir = "Stable-diffusion"
+model_path = os.path.abspath(os.path.join(models_path, model_dir))
+
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
+checkpoints_list = {}
+checkpoints_loaded = collections.OrderedDict()
+
+
+def get_config(checkpoint_info):
+    path = checkpoint_info[0]
+    model_config = checkpoint_info.config
+    checkpoint_dir = os.path.join(shared.script_path, "extensions", "sd_auto_fix", "configs")
+    if model_config == shared.cmd_opts.config:
+        try:
+            checkpoint = torch.load(path)
+            c_dict = checkpoint["state_dict"] if "state_dict" in checkpoint else checkpoint
+            v2_key = "cond_stage_model.model.ln_final.weight"
+            if v2_key in c_dict:
+                if "global_step" in checkpoint and checkpoint_info.config == shared.cmd_opts.config:
+                    if checkpoint["global_step"] == 875000 or checkpoint["global_step"] == 220000:
+                        model_config = os.path.join(checkpoint_dir, "v2-inference.yaml")
+                    else:
+                        model_config = os.path.join(checkpoint_dir, "v2-inference-v.yaml")
+                print(f"V2 Model detected, selecting model config: {model_config}")
+            del checkpoint
+        except Exception as e:
+            print(f"Exception: {e}")
+            traceback.print_exc()
+            pass
+    return model_config
+
+
+def load_model(checkpoint_info=None):
+    from modules import lowvram, sd_hijack
+    checkpoint_info = checkpoint_info or select_checkpoint()
+    model_config = get_config(checkpoint_info)
+
+    if model_config != shared.cmd_opts.config:
+        print(f"Loading config from: {model_config}")
+
+    if shared.sd_model:
+        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+        shared.sd_model = None
+        gc.collect()
+        devices.torch_gc()
+
+    sd_config = OmegaConf.load(model_config)
+
+    if should_hijack_inpainting(checkpoint_info):
+        # Hardcoded config for now...
+        sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
+        sd_config.model.params.use_ema = False
+        sd_config.model.params.conditioning_key = "hybrid"
+        sd_config.model.params.unet_config.params.in_channels = 9
+
+        # Create a "fake" config with a different name so that we know to unload it when switching models.
+        checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
+
+    do_inpainting_hijack()
+
+    if shared.cmd_opts.no_half:
+        sd_config.model.params.unet_config.params.use_fp16 = False
+
+    sd_model = instantiate_from_config(sd_config.model)
+    load_model_weights(sd_model, checkpoint_info)
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
+    else:
+        sd_model.to(shared.device)
+
+    sd_hijack.model_hijack.hijack(sd_model)
+
+    sd_model.eval()
+    shared.sd_model = sd_model
+
+    script_callbacks.model_loaded_callback(sd_model)
+
+    print(f"Model loaded.")
+    return sd_model
+
+
+def reload_model_weights(sd_model=None, info=None):
+    from modules import lowvram, devices, sd_hijack
+    checkpoint_info = info or select_checkpoint()
+
+    if not sd_model:
+        sd_model = shared.sd_model
+
+    if sd_model.sd_model_checkpoint == checkpoint_info.filename:
+        return
+
+    model_config = get_config(checkpoint_info)
+    checkpoint_info = checkpoint_info._replace(config=model_config)
+    if sd_model.sd_checkpoint_info.config != model_config or should_hijack_inpainting(
+            checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+        del sd_model
+        checkpoints_loaded.clear()
+        load_model(checkpoint_info)
+        return shared.sd_model
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.send_everything_to_cpu()
+    else:
+        sd_model.to(devices.cpu)
+
+    sd_hijack.model_hijack.undo_hijack(sd_model)
+
+    load_model_weights(sd_model, checkpoint_info)
+
+    sd_hijack.model_hijack.hijack(sd_model)
+    script_callbacks.model_loaded_callback(sd_model)
+
+    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+        sd_model.to(devices.device)
+
+    print(f"Weights loaded.")
+    return sd_model
+
+
+# This is so effing ridiculous that we have to do this $hit.
+
+sd_models.load_model = load_model
+sd_models.reload_model_weights = reload_model_weights