Revert "[dreambooth] fixing the incompatibity in requirements.txt (#3190) (#3378)" (#3481)

examples/images/diffusion/scripts/img2img.py

@@ -20,8 +20,8 @@ from imwatermark import WatermarkEncoder
 
 
 from scripts.txt2img import put_watermark
+from ldm.util import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.ddpm import LatentDiffusion
 from utils import replace_module, getModelSize
 
 
@@ -36,7 +36,7 @@ def load_model_from_config(config, ckpt, verbose=False):
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
     sd = pl_sd["state_dict"]
-    model = LatentDiffusion(**config.model.get("params", dict()))
+    model = instantiate_from_config(config.model)
     m, u = model.load_state_dict(sd, strict=False)
     if len(m) > 0 and verbose:
         print("missing keys:")

examples/images/diffusion/scripts/inpaint.py

@@ -4,7 +4,7 @@ from PIL import Image
 from tqdm import tqdm
 import numpy as np
 import torch
-from ldm.models.diffusion.ddpm import LatentgDiffusion
+from main import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
 
 
@@ -57,7 +57,7 @@ if __name__ == "__main__":
     print(f"Found {len(masks)} inputs.")
 
     config = OmegaConf.load("models/ldm/inpainting_big/config.yaml")
-    model = LatentDiffusion(**config.model.get("params", dict()))
+    model = instantiate_from_config(config.model)
     model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"],
                           strict=False)
 

examples/images/diffusion/scripts/knn2img.py

@@ -13,10 +13,9 @@ import scann
 import time
 from multiprocessing import cpu_count
 
-from ldm.util import parallel_data_prefetch
+from ldm.util import instantiate_from_config, parallel_data_prefetch
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
-from ldm.models.diffusion.ddpm import LatentDiffusion
 from ldm.modules.encoders.modules import FrozenClipImageEmbedder, FrozenCLIPTextEmbedder
 
 DATABASES = [
@@ -45,7 +44,7 @@ def load_model_from_config(config, ckpt, verbose=False):
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
     sd = pl_sd["state_dict"]
-    model = LatentDiffusion(**config.model.get("params", dict()))
+    model = instantiate_from_config(config.model)
     m, u = model.load_state_dict(sd, strict=False)
     if len(m) > 0 and verbose:
         print("missing keys:")

examples/images/diffusion/scripts/sample_diffusion.py

@@ -8,6 +8,7 @@ from omegaconf import OmegaConf
 from PIL import Image
 
 from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.util import instantiate_from_config
 
 rescale = lambda x: (x + 1.) / 2.
 
@@ -217,7 +218,7 @@ def get_parser():
 
 
 def load_model_from_config(config, sd):
-    model = LatentDiffusion(**config.get("params", dict()))
+    model = instantiate_from_config(config)
     model.load_state_dict(sd,strict=False)
     model.cuda()
     model.eval()

examples/images/diffusion/scripts/tests/test_checkpoint.py

@@ -9,7 +9,7 @@ from diffusers import StableDiffusionPipeline
 import torch
 from ldm.util import instantiate_from_config
 from main import get_parser
-from ldm.modules.diffusionmodules.openaimodel import UNetModel
+
 if __name__ == "__main__":
     with torch.no_grad():
         yaml_path = "../../train_colossalai.yaml"
@@ -17,7 +17,7 @@ if __name__ == "__main__":
             config = f.read()
         base_config = yaml.load(config, Loader=yaml.FullLoader)
         unet_config = base_config['model']['params']['unet_config']
-        diffusion_model = UNetModel(**unet_config.get("params", dict())).to("cuda:0")
+        diffusion_model = instantiate_from_config(unet_config).to("cuda:0")
 
         pipe = StableDiffusionPipeline.from_pretrained(
             "/data/scratch/diffuser/stable-diffusion-v1-4"

examples/images/diffusion/scripts/txt2img.py

@@ -16,9 +16,9 @@ from torch import autocast
 from contextlib import nullcontext
 from imwatermark import WatermarkEncoder
 
+from ldm.util import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
-from ldm.models.diffusion.ddpm import LatentDiffusion
 from ldm.models.diffusion.dpm_solver import DPMSolverSampler
 from utils import replace_module, getModelSize
 
@@ -35,7 +35,7 @@ def load_model_from_config(config, ckpt, verbose=False):
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
     sd = pl_sd["state_dict"]
-    model = LatentDiffusion(**config.model.get("params", dict()))
+    model = instantiate_from_config(config.model)
     m, u = model.load_state_dict(sd, strict=False)
     if len(m) > 0 and verbose:
         print("missing keys:")