Fix for train.controlnet.controlnet_v1_5_1node_100steps

nemo/collections/multimodal/models/text_to_image/controlnet/controlnet.py

@@ -550,11 +550,18 @@ def load_from_unet(self, from_pretrained_unet, from_NeMo=True):
             print("Loading unet blocks from sd")
 
             state_dict = torch.load(from_pretrained_unet, map_location='cpu')
-            state_dict = state_dict['state_dict']
+            if 'state_dict' in state_dict.keys():
+                state_dict = state_dict['state_dict']
             model_state_dict = self.state_dict()
+            model_state_keys = model_state_dict.keys()
 
             re_state_dict = {}
             for key_, value_ in state_dict.items():
+                # check if key is a raw parameter
+                if key_ in model_state_keys:
+                    re_state_dict[key_] = value_
+                    continue
+                # prune from model prefix
                 if key_.startswith('model.model.diffusion_model'):
                     re_state_dict[key_.replace('model.model.diffusion_model.', '')] = value_
                 if key_.startswith('model.diffusion_model'):

nemo/collections/multimodal/modules/stable_diffusion/diffusionmodules/openaimodel.py

@@ -971,6 +971,8 @@ def __init__(
                 )
                 logging.info(f"Missing keys: {missing_key}")
                 logging.info(f"Unexpected keys: {unexpected_keys}")
+            else:
+                logging.info(f"There are no missing keys, model loaded properly!")
 
         if unet_precision == "fp16-mixed":  # AMP O2
             self.convert_to_fp16()
@@ -1217,6 +1219,7 @@ def _state_key_mapping(self, state_dict: dict):
     def _load_pretrained_model(self, state_dict, ignore_mismatched_sizes=False, from_NeMo=False):
         state_dict = self._strip_unet_key_prefix(state_dict)
         if not from_NeMo:
+            logging.info("creating state key mapping from HF")
             state_dict = self._state_key_mapping(state_dict)
         state_dict = self._legacy_unet_ckpt_mapping(state_dict)
 

scripts/checkpoint_converters/convert_stablediffusion_hf_to_nemo.py

@@ -13,13 +13,14 @@
 # limitations under the License.
 
 r"""
-Conversion script to convert HuggingFace Starcoder2 checkpoints into nemo checkpoint.
+Conversion script to convert HuggingFace StableDiffusion checkpoints into nemo checkpoint.
   Example to run this conversion script:
     python convert_hf_starcoder2_to_nemo.py \
      --input_name_or_path <path_to_sc2_checkpoints_folder> \
-     --output_path <path_to_output_nemo_file>
+     --output_path <path_to_output_nemo_file> --model <unet|vae>
 """
 
+import os
 from argparse import ArgumentParser
 
 import numpy as np
@@ -29,8 +30,6 @@
 
 from nemo.utils import logging
 
-intkey = lambda x: int(x)
-
 
 def filter_keys(rule, dict):
     keys = list(dict.keys())
@@ -95,7 +94,7 @@ def __getitem__(self, name: str):
                 return None
             # either more than 1 match (error) or exactly 1 (success)
             if np.sum(p_flag) > 1:
-                print(f"error: multiple matches of key {name} with {keys}")
+                logging.warning(f"warning: multiple matches of key {name} with {keys}")
             else:
                 i = np.where(p_flag)[0][0]
                 n = numdots(keys[i])
@@ -130,14 +129,9 @@ def get_args():
     return args
 
 
-def make_tiny_config(config):
-    '''dial down the config file to make things tractable'''
-    # TODO
-    return config
-
-
 def load_hf_ckpt(in_dir, args):
     ckpt = {}
+    assert os.path.isdir(in_dir), "Currently supports only directories with a safetensor file in it."
     with safetensors.safe_open(in_dir + "/diffusion_pytorch_model.safetensors", framework="pt") as f:
         for k in f.keys():
             ckpt[k] = f.get_tensor(k)
@@ -161,9 +155,9 @@ def sanity_check(hf_tree, hf_unet, nemo_unet):
     # check if i'm introducing new keys
     for hfk, nk in hf_to_nemo_mapping(hf_tree).items():
         if nk not in nemo_unet.keys():
-            print(nk)
+            logging.info(nk)
         if hfk not in hf_unet.keys():
-            print(hfk)
+            logging.info(hfk)
 
 
 def convert_input_keys(hf_tree: SegTree):
@@ -174,7 +168,7 @@ def convert_input_keys(hf_tree: SegTree):
     # start counting blocks from now on
     nemo_inp_blk = 1
     down_blocks = hf_tree['down_blocks']
-    down_blocks_keys = sorted(list(down_blocks.nodes.keys()), key=intkey)
+    down_blocks_keys = sorted(list(down_blocks.nodes.keys()), key=int)
     for downblockid in down_blocks_keys:
         block = down_blocks[str(downblockid)]
         # compute number of resnets, attentions, downsamplers in this block
@@ -183,14 +177,14 @@ def convert_input_keys(hf_tree: SegTree):
         downsamplers = block.nodes.get('downsamplers', SegTree())
 
         if len(attentions) == 0:  # no attentions, this is a DownBlock2d
-            for resid in sorted(list(resnets.nodes.keys()), key=intkey):
+            for resid in sorted(list(resnets.nodes.keys()), key=int):
                 resid = str(resid)
                 resnets[resid].convert_name = f"input_blocks.{nemo_inp_blk}.0"
                 map_resnet_block(resnets[resid])
                 nemo_inp_blk += 1
         elif len(attentions) == len(resnets):
             # there are attention blocks here -- each resnet+attention becomes a block
-            for resid in sorted(list(resnets.nodes.keys()), key=intkey):
+            for resid in sorted(list(resnets.nodes.keys()), key=int):
                 resid = str(resid)
                 resnets[resid].convert_name = f"input_blocks.{nemo_inp_blk}.0"
                 map_resnet_block(resnets[resid])
@@ -199,7 +193,6 @@ def convert_input_keys(hf_tree: SegTree):
                 nemo_inp_blk += 1
         else:
             logging.warning("number of attention blocks is not the same as resnets - whats going on?")
-
         # if there is a downsampler, then also append it
         if len(downsamplers) > 0:
             for k in downsamplers.nodes.keys():
@@ -217,10 +210,9 @@ def clean_convert_names(tree):
 def map_attention_block(att_tree: SegTree):
     '''this HF tree can either be an AttentionBlock or a DualAttention block
     currently assumed AttentionBlock
-
     '''
 
-    # TODO (rohit): Add check for dual attention block
+    # TODO(@rohitrango): Add check for dual attention block, but this works for both SD and SDXL
     def check_att_type(tree):
         return "att_block"
 
@@ -237,7 +229,7 @@ def check_att_type(tree):
             dup_convert_name_recursive(tblock['norm1'], 'attn1.norm')
             dup_convert_name_recursive(tblock['norm2'], 'attn2.norm')
             dup_convert_name_recursive(tblock['norm3'], 'ff.net.0')
-            # map ff module
+            # map ff
             tblock['ff'].convert_name = "ff"
             tblock['ff.net'].convert_name = 'net'
             dup_convert_name_recursive(tblock['ff.net.0'], '1')
@@ -272,12 +264,16 @@ def hf_to_nemo_mapping(tree: SegTree):
 
 def convert_cond_keys(tree: SegTree):
     # map all conditioning keys
-    tree['add_embedding'].convert_name = 'label_emb.0'
-    dup_convert_name_recursive(tree['add_embedding.linear_1'], '0')
-    dup_convert_name_recursive(tree['add_embedding.linear_2'], '2')
-    tree['time_embedding'].convert_name = 'time_embed'
-    dup_convert_name_recursive(tree['time_embedding.linear_1'], '0')
-    dup_convert_name_recursive(tree['time_embedding.linear_2'], '2')
+    if tree.nodes.get("add_embedding"):
+        logging.info("Add embedding found...")
+        tree['add_embedding'].convert_name = 'label_emb.0'
+        dup_convert_name_recursive(tree['add_embedding.linear_1'], '0')
+        dup_convert_name_recursive(tree['add_embedding.linear_2'], '2')
+    if tree.nodes.get("time_embedding"):
+        logging.info("Time embedding found...")
+        tree['time_embedding'].convert_name = 'time_embed'
+        dup_convert_name_recursive(tree['time_embedding.linear_1'], '0')
+        dup_convert_name_recursive(tree['time_embedding.linear_2'], '2')
 
 
 def convert_middle_keys(tree: SegTree):
@@ -298,7 +294,7 @@ def convert_output_keys(hf_tree: SegTree):
     '''output keys is similar to input keys'''
     nemo_inp_blk = 0
     up_blocks = hf_tree['up_blocks']
-    up_blocks_keys = sorted(list(up_blocks.nodes.keys()), key=intkey)
+    up_blocks_keys = sorted(list(up_blocks.nodes.keys()), key=int)
 
     for downblockid in up_blocks_keys:
         block = up_blocks[str(downblockid)]
@@ -307,16 +303,16 @@ def convert_output_keys(hf_tree: SegTree):
         attentions = block.nodes.get('attentions', SegTree())
         upsamplers = block.nodes.get('upsamplers', SegTree())
 
-        if len(attentions) == 0:  # no attentions, this is a DownBlock2d
-            for resid in sorted(list(resnets.nodes.keys()), key=intkey):
+        if len(attentions) == 0:  # no attentions, this is a UpBlock2D
+            for resid in sorted(list(resnets.nodes.keys()), key=int):
                 resid = str(resid)
                 resnets[resid].convert_name = f"output_blocks.{nemo_inp_blk}.0"
                 map_resnet_block(resnets[resid])
                 nemo_inp_blk += 1
 
         elif len(attentions) == len(resnets):
             # there are attention blocks here -- each resnet+attention becomes a block
-            for resid in sorted(list(resnets.nodes.keys()), key=intkey):
+            for resid in sorted(list(resnets.nodes.keys()), key=int):
                 resid = str(resid)
                 resnets[resid].convert_name = f"output_blocks.{nemo_inp_blk}.0"
                 map_resnet_block(resnets[resid])
@@ -326,11 +322,13 @@ def convert_output_keys(hf_tree: SegTree):
         else:
             logging.warning("number of attention blocks is not the same as resnets - whats going on?")
 
-        # if there is a downsampler, then also append it
+        # if there is a upsampler, then also append it
         if len(upsamplers) > 0:
-            # for k in upsamplers.nodes.keys():
             nemo_inp_blk -= 1
-            upsamplers['0'].convert_name = f"output_blocks.{nemo_inp_blk}.2"
+            upsamplenum = (
+                1 if len(attentions) == 0 else 2
+            )  # if there are attention modules, upsample is module2, else it is module 1 (to stay consistent with SD)
+            upsamplers['0'].convert_name = f"output_blocks.{nemo_inp_blk}.{upsamplenum}"
             dup_convert_name_recursive(upsamplers['0.conv'], 'conv')
             nemo_inp_blk += 1
 
@@ -387,6 +385,7 @@ def convert_decoder(hf_tree: SegTree):
     decoder['mid_block'].convert_name = 'mid'
     dup_convert_name_recursive(decoder[f'mid_block.resnets.0'], 'block_1')
     dup_convert_name_recursive(decoder[f'mid_block.resnets.1'], 'block_2')
+    # attention blocks
     att = decoder['mid_block.attentions.0']
     att.convert_name = 'attn_1'
     dup_convert_name_recursive(att['group_norm'], 'norm')
@@ -443,6 +442,7 @@ def convert(args):
 
     for hf_key, nemo_key in mapping.items():
         nemo_ckpt[nemo_key] = hf_ckpt[hf_key]
+    # save this
     torch.save(nemo_ckpt, args.output_path)
     logging.info(f"Saved nemo file to {args.output_path}")