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https://github.com/lllyasviel/stable-diffusion-webui-forge.git
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increased support for custom CLIPs (#2642)
increased support for custom CLIPs more forms recognised now can be applied to sd1.5, sdxl, (sd3)
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DenOfEquity
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184bb04f8d
@ -209,12 +209,208 @@ def replace_state_dict(sd, asd, guess):
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for k, v in asd.items():
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sd[vae_key_prefix + k] = v
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if 'text_model.encoder.layers.0.layer_norm1.weight' in asd:
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keys_to_delete = [k for k in sd if k.startswith(f"{text_encoder_key_prefix}clip_l.")]
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for k in keys_to_delete:
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del sd[k]
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for k, v in asd.items():
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sd[f"{text_encoder_key_prefix}clip_l.transformer.{k}"] = v
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## identify model type
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flux_test_key = "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale"
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sd3_test_key = "model.diffusion_model.final_layer.adaLN_modulation.1.bias"
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legacy_test_key = "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight"
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model_type = "-"
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if legacy_test_key in sd:
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match sd[legacy_test_key].shape[1]:
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case 768:
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model_type = "sd1"
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case 1024:
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model_type = "sd2"
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case 2048:
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model_type = "sdxl"
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elif flux_test_key in sd:
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model_type = "flux"
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elif sd3_test_key in sd:
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model_type = "sd3"
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## prefixes used by various model types for CLIP-L
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prefix_L = {
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"-" : None,
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"sd1" : "cond_stage_model.transformer.",
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"sd2" : None,
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"sdxl": "conditioner.embedders.0.transformer.",
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"flux": "text_encoders.clip_l.transformer.",
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"sd3" : "text_encoders.clip_l.transformer.",
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}
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## prefixes used by various model types for CLIP-G
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prefix_G = {
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"-" : None,
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"sd1" : None,
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"sd2" : None,
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"sdxl": "conditioner.embedders.1.model.",
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"flux": None,
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"sd3" : "text_encoders.clip_g.",
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}
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## prefixes used by various model types for CLIP-H
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prefix_H = {
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"-" : None,
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"sd1" : None,
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"sd2" : "conditioner.embedders.0.model.",
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"sdxl": None,
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"flux": None,
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"sd3" : None,
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}
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## VAE format 0 (extracted from model, could be sd1, sd2, sdxl, sd3).
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if "first_stage_model.decoder.conv_in.weight" in asd:
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channels = asd["first_stage_model.decoder.conv_in.weight"].shape[1]
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if model_type == "sd1" or model_type == "sd2" or model_type == "sdxl":
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if channels == 4:
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for k, v in asd.items():
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sd[k] = v
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elif model_type == "sd3":
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if channels == 16:
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for k, v in asd.items():
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sd[k] = v
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## CLIP-H
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CLIP_H = { # key to identify source model old_prefix
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'cond_stage_model.model.ln_final.weight' : 'cond_stage_model.model.',
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# 'text_model.encoder.layers.0.layer_norm1.bias' : 'text_model'. # would need converting
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}
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for CLIP_key in CLIP_H.keys():
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if CLIP_key in asd and asd[CLIP_key].shape[0] == 1024:
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new_prefix = prefix_H[model_type]
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old_prefix = CLIP_H[CLIP_key]
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if new_prefix is not None:
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for k, v in asd.items():
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new_k = k.replace(old_prefix, new_prefix)
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sd[new_k] = v
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## CLIP-G
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CLIP_G = { # key to identify source model old_prefix
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'conditioner.embedders.1.model.transformer.resblocks.0.ln_1.bias' : 'conditioner.embedders.1.model.',
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'text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm1.bias' : 'text_encoders.clip_g.',
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'text_model.encoder.layers.0.layer_norm1.bias' : '',
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'transformer.resblocks.0.ln_1.bias' : ''
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}
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for CLIP_key in CLIP_G.keys():
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if CLIP_key in asd and asd[CLIP_key].shape[0] == 1280:
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new_prefix = prefix_G[model_type]
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old_prefix = CLIP_G[CLIP_key]
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if new_prefix is not None:
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if "resblocks" not in CLIP_key: # need to convert
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def convert_transformers(statedict, prefix_from, prefix_to, number):
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keys_to_replace = {
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"{}text_model.embeddings.position_embedding.weight" : "{}positional_embedding",
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"{}text_model.embeddings.token_embedding.weight" : "{}token_embedding.weight",
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"{}text_model.final_layer_norm.weight" : "{}ln_final.weight",
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"{}text_model.final_layer_norm.bias" : "{}ln_final.bias",
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"text_projection.weight" : "{}text_projection",
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}
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resblock_to_replace = {
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"layer_norm1" : "ln_1",
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"layer_norm2" : "ln_2",
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"mlp.fc1" : "mlp.c_fc",
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"mlp.fc2" : "mlp.c_proj",
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"self_attn.out_proj" : "attn.out_proj" ,
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}
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for x in keys_to_replace:
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k = x.format(prefix_from)
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statedict[keys_to_replace[x].format(prefix_to)] = statedict.pop(k)
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for resblock in range(number):
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for y in ["weight", "bias"]:
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for x in resblock_to_replace:
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k = "{}text_model.encoder.layers.{}.{}.{}".format(prefix_from, resblock, x, y)
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k_to = "{}transformer.resblocks.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y)
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statedict[k_to] = statedict.pop(k)
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k_from = "{}text_model.encoder.layers.{}.{}.{}".format(prefix_from, resblock, "self_attn.q_proj", y)
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weightsQ = statedict.pop(k_from)
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k_from = "{}text_model.encoder.layers.{}.{}.{}".format(prefix_from, resblock, "self_attn.k_proj", y)
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weightsK = statedict.pop(k_from)
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k_from = "{}text_model.encoder.layers.{}.{}.{}".format(prefix_from, resblock, "self_attn.v_proj", y)
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weightsV = statedict.pop(k_from)
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k_to = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_to, resblock, y)
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statedict[k_to] = torch.cat((weightsQ, weightsK, weightsV))
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return statedict
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asd = convert_transformers(asd, old_prefix, new_prefix, 32)
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new_prefix = ""
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if old_prefix == "":
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for k, v in asd.items():
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new_k = new_prefix + k
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sd[new_k] = v
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else:
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for k, v in asd.items():
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new_k = k.replace(old_prefix, new_prefix)
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sd[new_k] = v
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## CLIP-L
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CLIP_L = { # key to identify source model old_prefix
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'cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.bias' : 'cond_stage_model.transformer.',
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'conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm1.bias' : 'conditioner.embedders.0.transformer.',
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'text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm1.bias' : 'text_encoders.clip_l.transformer.',
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'text_model.encoder.layers.0.layer_norm1.bias' : '',
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'transformer.resblocks.0.ln_1.bias' : ''
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}
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for CLIP_key in CLIP_L.keys():
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if CLIP_key in asd and asd[CLIP_key].shape[0] == 768:
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new_prefix = prefix_L[model_type]
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old_prefix = CLIP_L[CLIP_key]
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if new_prefix is not None:
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if "resblocks" in CLIP_key: # need to convert
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def transformers_convert(statedict, prefix_from, prefix_to, number):
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keys_to_replace = {
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"positional_embedding" : "{}text_model.embeddings.position_embedding.weight",
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"token_embedding.weight": "{}text_model.embeddings.token_embedding.weight",
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"ln_final.weight" : "{}text_model.final_layer_norm.weight",
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"ln_final.bias" : "{}text_model.final_layer_norm.bias",
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}
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resblock_to_replace = {
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"ln_1" : "layer_norm1",
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"ln_2" : "layer_norm2",
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"mlp.c_fc" : "mlp.fc1",
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"mlp.c_proj" : "mlp.fc2",
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"attn.out_proj" : "self_attn.out_proj",
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}
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for k in keys_to_replace:
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statedict[keys_to_replace[k].format(prefix_to)] = statedict.pop(k)
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for resblock in range(number):
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for y in ["weight", "bias"]:
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for x in resblock_to_replace:
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k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y)
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k_to = "{}text_model.encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y)
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statedict[k_to] = statedict.pop(k)
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k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y)
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weights = statedict.pop(k_from)
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shape_from = weights.shape[0] // 3
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for x in range(3):
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p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
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k_to = "{}text_model.encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y)
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statedict[k_to] = weights[shape_from*x:shape_from*(x + 1)]
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return statedict
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asd = transformers_convert(asd, old_prefix, new_prefix, 12)
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new_prefix = ""
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if old_prefix == "":
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for k, v in asd.items():
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new_k = new_prefix + k
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sd[new_k] = v
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else:
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for k, v in asd.items():
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new_k = k.replace(old_prefix, new_prefix)
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sd[new_k] = v
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if 'encoder.block.0.layer.0.SelfAttention.k.weight' in asd:
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keys_to_delete = [k for k in sd if k.startswith(f"{text_encoder_key_prefix}t5xxl.")]
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@ -227,9 +423,8 @@ def replace_state_dict(sd, asd, guess):
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def preprocess_state_dict(sd):
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if any("double_block" in k for k in sd.keys()):
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if not any(k.startswith("model.diffusion_model") for k in sd.keys()):
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sd = {f"model.diffusion_model.{k}": v for k, v in sd.items()}
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if not any(k.startswith("model.diffusion_model") for k in sd.keys()):
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sd = {f"model.diffusion_model.{k}": v for k, v in sd.items()}
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return sd
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@ -243,11 +438,14 @@ def split_state_dict(sd, additional_state_dicts: list = None):
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for asd in additional_state_dicts:
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asd = load_torch_file(asd)
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sd = replace_state_dict(sd, asd, guess)
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del asd
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guess.clip_target = guess.clip_target(sd)
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guess.model_type = guess.model_type(sd)
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guess.ztsnr = 'ztsnr' in sd
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sd = guess.process_vae_state_dict(sd)
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state_dict = {
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guess.unet_target: try_filter_state_dict(sd, guess.unet_key_prefix),
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guess.vae_target: try_filter_state_dict(sd, guess.vae_key_prefix)
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