diff options
Diffstat (limited to 'modules/sd_models_config.py')
| -rw-r--r-- | modules/sd_models_config.py | 112 |
1 files changed, 112 insertions, 0 deletions
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py new file mode 100644 index 00000000..91c21700 --- /dev/null +++ b/modules/sd_models_config.py @@ -0,0 +1,112 @@ +import re
+import os
+
+import torch
+
+from modules import shared, paths, sd_disable_initialization
+
+sd_configs_path = shared.sd_configs_path
+sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
+
+
+config_default = shared.sd_default_config
+config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
+config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
+config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
+config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
+config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
+config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
+config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
+
+
+def is_using_v_parameterization_for_sd2(state_dict):
+ """
+ Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
+ """
+
+ import ldm.modules.diffusionmodules.openaimodel
+ from modules import devices
+
+ device = devices.cpu
+
+ with sd_disable_initialization.DisableInitialization():
+ unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
+ use_checkpoint=True,
+ use_fp16=False,
+ image_size=32,
+ 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,
+ use_spatial_transformer=True,
+ use_linear_in_transformer=True,
+ transformer_depth=1,
+ context_dim=1024,
+ legacy=False
+ )
+ unet.eval()
+
+ with torch.no_grad():
+ unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
+ unet.load_state_dict(unet_sd, strict=True)
+ unet.to(device=device, dtype=torch.float)
+
+ test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
+ x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
+
+ out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
+
+ return out < -1
+
+
+def guess_model_config_from_state_dict(sd, filename):
+ sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
+ diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
+
+ if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
+ return config_depth_model
+
+ if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
+ if diffusion_model_input.shape[1] == 9:
+ return config_sd2_inpainting
+ elif is_using_v_parameterization_for_sd2(sd):
+ return config_sd2v
+ else:
+ return config_sd2
+
+ if diffusion_model_input is not None:
+ if diffusion_model_input.shape[1] == 9:
+ return config_inpainting
+ if diffusion_model_input.shape[1] == 8:
+ return config_instruct_pix2pix
+
+ if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
+ return config_alt_diffusion
+
+ return config_default
+
+
+def find_checkpoint_config(state_dict, info):
+ if info is None:
+ return guess_model_config_from_state_dict(state_dict, "")
+
+ config = find_checkpoint_config_near_filename(info)
+ if config is not None:
+ return config
+
+ return guess_model_config_from_state_dict(state_dict, info.filename)
+
+
+def find_checkpoint_config_near_filename(info):
+ if info is None:
+ return None
+
+ config = os.path.splitext(info.filename)[0] + ".yaml"
+ if os.path.exists(config):
+ return config
+
+ return None
+
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