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update

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Dhruv Nair
2024-01-17 08:50:37 +00:00
parent 1bd8ba3de1
commit 1cce591cd1
1 changed files with 0 additions and 73 deletions
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73
src/diffusers/loaders/single_file_utils.py
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@@ -246,10 +246,6 @@ def infer_model_type(pipeline_class_name, original_config, model_type=None, **kw
if model_type is not None:
return model_type
if pipeline_class_name in ["StableUnCLIPPipeline", "StableUnCLIPImg2ImgPipeline"]:
model_type = "FrozenOpenCLIPEmbedder"
return model_type
has_cond_stage_config = (
"cond_stage_config" in original_config["model"]["params"]
and original_config["model"]["params"]["cond_stage_config"] is not None
@@ -305,17 +301,6 @@ def determine_image_size(pipeline_class_name, original_config, checkpoint, **kwa
return image_size
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments
def shave_segments(path, n_shave_prefix_segments=1):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split(".")[n_shave_prefix_segments:])
else:
return ".".join(path.split(".")[:n_shave_prefix_segments])
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear
def conv_attn_to_linear(checkpoint):
keys = list(checkpoint.keys())
@@ -776,57 +761,6 @@ def convert_ldm_vae_checkpoint(checkpoint, config):
return new_checkpoint
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_bert_checkpoint
def convert_ldm_bert_checkpoint(checkpoint, config):
def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight
hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias
def _copy_linear(hf_linear, pt_linear):
hf_linear.weight = pt_linear.weight
hf_linear.bias = pt_linear.bias
def _copy_layer(hf_layer, pt_layer):
# copy layer norms
_copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
_copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])
# copy attn
_copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])
# copy MLP
pt_mlp = pt_layer[1][1]
_copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
_copy_linear(hf_layer.fc2, pt_mlp.net[2])
def _copy_layers(hf_layers, pt_layers):
for i, hf_layer in enumerate(hf_layers):
if i != 0:
i += i
pt_layer = pt_layers[i : i + 2]
_copy_layer(hf_layer, pt_layer)
hf_model = LDMBertModel(config).eval()
# copy embeds
hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
# copy layer norm
_copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
# copy hidden layers
_copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)
_copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)
return hf_model
def convert_ldm_clip_checkpoint(checkpoint, local_files_only=False):
try:
config = CLIPTextConfig.from_pretrained(LDM_CLIP_CONFIG_NAME, local_files_only=local_files_only)
@@ -1151,13 +1085,6 @@ def create_text_encoders_and_tokenizers(
"text_encoder_2": text_encoder_2,
}
elif pipeline_class_name == "LDMTextToImagePipeline":
text_config = create_ldm_bert_config(original_config)
text_encoder = convert_ldm_bert_checkpoint(checkpoint, text_config)
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased", local_files_only=local_files_only)
return {"text_encoder": text_encoder, "tokenizer": tokenizer}
return