215 lines
7.0 KiB
Python
215 lines
7.0 KiB
Python
import collections
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import gc
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import os.path
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import traceback
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from collections import namedtuple
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import ldm.modules.attention
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import torch
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from einops import rearrange, repeat
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from ldm.util import instantiate_from_config
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from omegaconf import OmegaConf
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from torch import einsum
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from modules import shared, devices, script_callbacks, sd_models
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from modules.paths import models_path
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from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
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from modules.sd_models import select_checkpoint, load_model_weights
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print("Fixing all the things!")
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model_dir = "Stable-diffusion"
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model_path = os.path.abspath(os.path.join(models_path, model_dir))
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CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
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checkpoints_list = {}
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checkpoints_loaded = collections.OrderedDict()
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_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
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def exists(val):
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return val is not None
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def default(val, d):
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return val if exists(val) else d
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def fixed_forward(self, x, context=None, mask=None):
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h = self.heads
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q = self.to_q(x)
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context = default(context, x)
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k = self.to_k(context)
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v = self.to_v(context)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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# force cast to fp32 to avoid overflowing
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if _ATTN_PRECISION == "fp32":
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with torch.autocast(enabled=False, device_type='cuda'):
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q, k = q.float(), k.float()
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sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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else:
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sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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del q, k
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if exists(mask):
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mask = rearrange(mask, 'b ... -> b (...)')
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, 'b j -> (b h) () j', h=h)
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sim.masked_fill_(~mask, max_neg_value)
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# attention, what we cannot get enough of
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sim = sim.softmax(dim=-1)
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out = einsum('b i j, b j d -> b i d', sim, v)
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out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
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return self.to_out(out)
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def get_config(checkpoint_info):
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path = checkpoint_info[0]
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model_config = checkpoint_info.config
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checkpoint_dir = os.path.join(shared.script_path, "extensions", "sd_auto_fix", "configs")
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if model_config == shared.cmd_opts.config:
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try:
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checkpoint = torch.load(path)
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c_dict = checkpoint["state_dict"] if "state_dict" in checkpoint else checkpoint
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v2_key = "cond_stage_model.model.ln_final.weight"
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if v2_key in c_dict:
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model_config = os.path.join(checkpoint_dir, "v2-inference-v.yaml")
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if "global_step" in checkpoint and checkpoint_info.config == shared.cmd_opts.config:
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if checkpoint["global_step"] == 875000 or checkpoint["global_step"] == 220000:
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model_config = os.path.join(checkpoint_dir, "v2-inference.yaml")
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print(f"V2 Model detected, selecting model config: {model_config.replace(checkpoint_dir, '')}")
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del checkpoint
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except Exception as e:
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print(f"Exception: {e}")
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traceback.print_exc()
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pass
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return model_config
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def load_model(checkpoint_info=None):
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from modules import lowvram, sd_hijack
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checkpoint_info = checkpoint_info or select_checkpoint()
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model_config = get_config(checkpoint_info)
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if model_config != shared.cmd_opts.config:
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print(f"Loading config from: {model_config}")
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if shared.sd_model:
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sd_hijack.model_hijack.undo_hijack(shared.sd_model)
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shared.sd_model = None
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gc.collect()
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devices.torch_gc()
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sd_config = OmegaConf.load(model_config)
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if should_hijack_inpainting(checkpoint_info):
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# Hardcoded config for now...
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sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
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sd_config.model.params.use_ema = False
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sd_config.model.params.conditioning_key = "hybrid"
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sd_config.model.params.unet_config.params.in_channels = 9
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# Create a "fake" config with a different name so that we know to unload it when switching models.
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checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
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do_inpainting_hijack()
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sd_model = instantiate_from_config(sd_config.model)
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load_model_weights(sd_model, checkpoint_info)
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
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else:
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sd_model.to(shared.device)
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sd_hijack.model_hijack.hijack(sd_model)
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is_v21 = False
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try:
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is_v21 = sd_config.model.params.parameterization == "v"
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except:
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pass
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try:
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is_v21 = sd_config.model.params.unet_config.params.num_head_channels == 64
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except:
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pass
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if is_v21 and not shared.cmd_opts.xformers and not shared.cmd_opts.force_enable_xformers and not shared.cmd_opts.no_half:
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print("Fixing attention for v21 model.")
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ldm.modules.attention.CrossAttention.forward = fixed_forward
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sd_model.eval()
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shared.sd_model = sd_model
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script_callbacks.model_loaded_callback(sd_model)
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print(f"Model loaded.")
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return sd_model
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def reload_model_weights(sd_model=None, info=None):
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from modules import lowvram, devices, sd_hijack
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checkpoint_info = info or select_checkpoint()
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if not sd_model:
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sd_model = shared.sd_model
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if sd_model is not None and checkpoint_info is not None:
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if sd_model.sd_model_checkpoint == checkpoint_info.filename:
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return
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model_config = get_config(checkpoint_info)
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checkpoint_info = checkpoint_info._replace(config=model_config)
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if sd_model.sd_checkpoint_info.config != model_config or should_hijack_inpainting(
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checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
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del sd_model
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checkpoints_loaded.clear()
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load_model(checkpoint_info)
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return shared.sd_model
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.send_everything_to_cpu()
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else:
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sd_model.to(devices.cpu)
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sd_hijack.model_hijack.undo_hijack(sd_model)
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load_model_weights(sd_model, checkpoint_info)
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sd_hijack.model_hijack.hijack(sd_model)
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sd_config = OmegaConf.load(model_config)
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is_v21 = False
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try:
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is_v21 = sd_config.model.params.parameterization == "v"
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except:
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pass
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try:
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is_v21 = sd_config.model.params.unet_config.params.num_head_channels == 64
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except:
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pass
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if is_v21 and not shared.cmd_opts.xformers and not shared.cmd_opts.force_enable_xformers and not shared.cmd_opts.no_half:
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print("Fixing attention for v21 model.")
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ldm.modules.attention.CrossAttention.forward = fixed_forward
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script_callbacks.model_loaded_callback(sd_model)
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_model.to(devices.device)
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print(f"Weights loaded.")
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return sd_model
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sd_models.load_model = load_model
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sd_models.reload_model_weights = reload_model_weights
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