980 lines
38 KiB
Python
980 lines
38 KiB
Python
import torch
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import contextlib
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import copy
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import sd1_clip
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import sd2_clip
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from comfy import model_management
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from .ldm.util import instantiate_from_config
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from .ldm.models.autoencoder import AutoencoderKL
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import yaml
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from .cldm import cldm
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from .t2i_adapter import adapter
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from . import utils
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from . import clip_vision
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from . import gligen
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def load_model_weights(model, sd, verbose=False, load_state_dict_to=[]):
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m, u = model.load_state_dict(sd, strict=False)
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k = list(sd.keys())
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for x in k:
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# print(x)
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if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
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y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
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sd[y] = sd.pop(x)
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if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd:
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ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids']
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if ids.dtype == torch.float32:
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sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
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keys_to_replace = {
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"cond_stage_model.model.positional_embedding": "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight",
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"cond_stage_model.model.token_embedding.weight": "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight",
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"cond_stage_model.model.ln_final.weight": "cond_stage_model.transformer.text_model.final_layer_norm.weight",
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"cond_stage_model.model.ln_final.bias": "cond_stage_model.transformer.text_model.final_layer_norm.bias",
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}
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for x in keys_to_replace:
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if x in sd:
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sd[keys_to_replace[x]] = sd.pop(x)
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sd = utils.transformers_convert(sd, "cond_stage_model.model", "cond_stage_model.transformer.text_model", 24)
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for x in load_state_dict_to:
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x.load_state_dict(sd, strict=False)
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if len(m) > 0 and verbose:
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print("missing keys:")
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print(m)
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if len(u) > 0 and verbose:
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print("unexpected keys:")
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print(u)
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model.eval()
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return model
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LORA_CLIP_MAP = {
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"mlp.fc1": "mlp_fc1",
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"mlp.fc2": "mlp_fc2",
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"self_attn.k_proj": "self_attn_k_proj",
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"self_attn.q_proj": "self_attn_q_proj",
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"self_attn.v_proj": "self_attn_v_proj",
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"self_attn.out_proj": "self_attn_out_proj",
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}
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LORA_UNET_MAP_ATTENTIONS = {
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"proj_in": "proj_in",
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"proj_out": "proj_out",
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"transformer_blocks.0.attn1.to_q": "transformer_blocks_0_attn1_to_q",
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"transformer_blocks.0.attn1.to_k": "transformer_blocks_0_attn1_to_k",
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"transformer_blocks.0.attn1.to_v": "transformer_blocks_0_attn1_to_v",
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"transformer_blocks.0.attn1.to_out.0": "transformer_blocks_0_attn1_to_out_0",
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"transformer_blocks.0.attn2.to_q": "transformer_blocks_0_attn2_to_q",
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"transformer_blocks.0.attn2.to_k": "transformer_blocks_0_attn2_to_k",
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"transformer_blocks.0.attn2.to_v": "transformer_blocks_0_attn2_to_v",
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"transformer_blocks.0.attn2.to_out.0": "transformer_blocks_0_attn2_to_out_0",
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"transformer_blocks.0.ff.net.0.proj": "transformer_blocks_0_ff_net_0_proj",
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"transformer_blocks.0.ff.net.2": "transformer_blocks_0_ff_net_2",
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}
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LORA_UNET_MAP_RESNET = {
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"in_layers.2": "resnets_{}_conv1",
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"emb_layers.1": "resnets_{}_time_emb_proj",
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"out_layers.3": "resnets_{}_conv2",
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"skip_connection": "resnets_{}_conv_shortcut"
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}
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def load_lora(path, to_load):
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lora = utils.load_torch_file(path)
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patch_dict = {}
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loaded_keys = set()
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for x in to_load:
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alpha_name = "{}.alpha".format(x)
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alpha = None
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if alpha_name in lora.keys():
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alpha = lora[alpha_name].item()
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loaded_keys.add(alpha_name)
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A_name = "{}.lora_up.weight".format(x)
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B_name = "{}.lora_down.weight".format(x)
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mid_name = "{}.lora_mid.weight".format(x)
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if A_name in lora.keys():
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mid = None
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if mid_name in lora.keys():
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mid = lora[mid_name]
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loaded_keys.add(mid_name)
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patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid)
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loaded_keys.add(A_name)
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loaded_keys.add(B_name)
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hada_w1_a_name = "{}.hada_w1_a".format(x)
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hada_w1_b_name = "{}.hada_w1_b".format(x)
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hada_w2_a_name = "{}.hada_w2_a".format(x)
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hada_w2_b_name = "{}.hada_w2_b".format(x)
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hada_t1_name = "{}.hada_t1".format(x)
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hada_t2_name = "{}.hada_t2".format(x)
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if hada_w1_a_name in lora.keys():
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hada_t1 = None
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hada_t2 = None
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if hada_t1_name in lora.keys():
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hada_t1 = lora[hada_t1_name]
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hada_t2 = lora[hada_t2_name]
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loaded_keys.add(hada_t1_name)
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loaded_keys.add(hada_t2_name)
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patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)
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loaded_keys.add(hada_w1_a_name)
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loaded_keys.add(hada_w1_b_name)
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loaded_keys.add(hada_w2_a_name)
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loaded_keys.add(hada_w2_b_name)
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for x in lora.keys():
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if x not in loaded_keys:
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print("lora key not loaded", x)
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return patch_dict
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def model_lora_keys(model, key_map={}):
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sdk = model.state_dict().keys()
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counter = 0
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for b in range(12):
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tk = "model.diffusion_model.input_blocks.{}.1".format(b)
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up_counter = 0
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for c in LORA_UNET_MAP_ATTENTIONS:
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k = "{}.{}.weight".format(tk, c)
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if k in sdk:
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lora_key = "lora_unet_down_blocks_{}_attentions_{}_{}".format(counter // 2, counter % 2, LORA_UNET_MAP_ATTENTIONS[c])
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key_map[lora_key] = k
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up_counter += 1
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if up_counter >= 4:
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counter += 1
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for c in LORA_UNET_MAP_ATTENTIONS:
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k = "model.diffusion_model.middle_block.1.{}.weight".format(c)
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if k in sdk:
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lora_key = "lora_unet_mid_block_attentions_0_{}".format(LORA_UNET_MAP_ATTENTIONS[c])
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key_map[lora_key] = k
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counter = 3
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for b in range(12):
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tk = "model.diffusion_model.output_blocks.{}.1".format(b)
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up_counter = 0
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for c in LORA_UNET_MAP_ATTENTIONS:
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k = "{}.{}.weight".format(tk, c)
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if k in sdk:
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lora_key = "lora_unet_up_blocks_{}_attentions_{}_{}".format(counter // 3, counter % 3, LORA_UNET_MAP_ATTENTIONS[c])
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key_map[lora_key] = k
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up_counter += 1
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if up_counter >= 4:
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counter += 1
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counter = 0
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text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
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for b in range(24):
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for c in LORA_CLIP_MAP:
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k = "transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
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if k in sdk:
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lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
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key_map[lora_key] = k
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#Locon stuff
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ds_counter = 0
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counter = 0
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for b in range(12):
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tk = "model.diffusion_model.input_blocks.{}.0".format(b)
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key_in = False
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for c in LORA_UNET_MAP_RESNET:
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k = "{}.{}.weight".format(tk, c)
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if k in sdk:
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lora_key = "lora_unet_down_blocks_{}_{}".format(counter // 2, LORA_UNET_MAP_RESNET[c].format(counter % 2))
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key_map[lora_key] = k
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key_in = True
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for bb in range(3):
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k = "{}.{}.op.weight".format(tk[:-2], bb)
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if k in sdk:
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lora_key = "lora_unet_down_blocks_{}_downsamplers_0_conv".format(ds_counter)
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key_map[lora_key] = k
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ds_counter += 1
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if key_in:
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counter += 1
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counter = 0
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for b in range(3):
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tk = "model.diffusion_model.middle_block.{}".format(b)
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key_in = False
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for c in LORA_UNET_MAP_RESNET:
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k = "{}.{}.weight".format(tk, c)
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if k in sdk:
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lora_key = "lora_unet_mid_block_{}".format(LORA_UNET_MAP_RESNET[c].format(counter))
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key_map[lora_key] = k
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key_in = True
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if key_in:
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counter += 1
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counter = 0
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us_counter = 0
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for b in range(12):
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tk = "model.diffusion_model.output_blocks.{}.0".format(b)
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key_in = False
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for c in LORA_UNET_MAP_RESNET:
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k = "{}.{}.weight".format(tk, c)
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if k in sdk:
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lora_key = "lora_unet_up_blocks_{}_{}".format(counter // 3, LORA_UNET_MAP_RESNET[c].format(counter % 3))
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key_map[lora_key] = k
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key_in = True
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for bb in range(3):
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k = "{}.{}.conv.weight".format(tk[:-2], bb)
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if k in sdk:
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lora_key = "lora_unet_up_blocks_{}_upsamplers_0_conv".format(us_counter)
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key_map[lora_key] = k
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us_counter += 1
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if key_in:
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counter += 1
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return key_map
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class ModelPatcher:
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def __init__(self, model):
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self.model = model
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self.patches = []
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self.backup = {}
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self.model_options = {"transformer_options":{}}
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def clone(self):
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n = ModelPatcher(self.model)
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n.patches = self.patches[:]
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n.model_options = copy.deepcopy(self.model_options)
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return n
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def set_model_tomesd(self, ratio):
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self.model_options["transformer_options"]["tomesd"] = {"ratio": ratio}
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def set_model_sampler_cfg_function(self, sampler_cfg_function):
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self.model_options["sampler_cfg_function"] = sampler_cfg_function
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def model_dtype(self):
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return self.model.diffusion_model.dtype
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def add_patches(self, patches, strength=1.0):
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p = {}
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model_sd = self.model.state_dict()
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for k in patches:
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if k in model_sd:
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p[k] = patches[k]
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self.patches += [(strength, p)]
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return p.keys()
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def patch_model(self):
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model_sd = self.model.state_dict()
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for p in self.patches:
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for k in p[1]:
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v = p[1][k]
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key = k
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if key not in model_sd:
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print("could not patch. key doesn't exist in model:", k)
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continue
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weight = model_sd[key]
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if key not in self.backup:
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self.backup[key] = weight.clone()
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alpha = p[0]
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if len(v) == 4: #lora/locon
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mat1 = v[0]
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mat2 = v[1]
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if v[2] is not None:
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alpha *= v[2] / mat2.shape[0]
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if v[3] is not None:
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#locon mid weights, hopefully the math is fine because I didn't properly test it
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final_shape = [mat2.shape[1], mat2.shape[0], v[3].shape[2], v[3].shape[3]]
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mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1).float(), v[3].transpose(0, 1).flatten(start_dim=1).float()).reshape(final_shape).transpose(0, 1)
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weight += (alpha * torch.mm(mat1.flatten(start_dim=1).float(), mat2.flatten(start_dim=1).float())).reshape(weight.shape).type(weight.dtype).to(weight.device)
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else: #loha
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w1a = v[0]
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w1b = v[1]
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if v[2] is not None:
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alpha *= v[2] / w1b.shape[0]
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w2a = v[3]
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w2b = v[4]
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if v[5] is not None: #cp decomposition
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t1 = v[5]
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t2 = v[6]
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m1 = torch.einsum('i j k l, j r, i p -> p r k l', t1.float(), w1b.float(), w1a.float())
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m2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float(), w2b.float(), w2a.float())
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else:
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m1 = torch.mm(w1a.float(), w1b.float())
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m2 = torch.mm(w2a.float(), w2b.float())
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weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype).to(weight.device)
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return self.model
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def unpatch_model(self):
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model_sd = self.model.state_dict()
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keys = list(self.backup.keys())
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for k in keys:
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model_sd[k][:] = self.backup[k]
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del self.backup[k]
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self.backup = {}
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def load_lora_for_models(model, clip, lora_path, strength_model, strength_clip):
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key_map = model_lora_keys(model.model)
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key_map = model_lora_keys(clip.cond_stage_model, key_map)
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loaded = load_lora(lora_path, key_map)
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new_modelpatcher = model.clone()
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k = new_modelpatcher.add_patches(loaded, strength_model)
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new_clip = clip.clone()
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k1 = new_clip.add_patches(loaded, strength_clip)
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k = set(k)
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k1 = set(k1)
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for x in loaded:
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if (x not in k) and (x not in k1):
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print("NOT LOADED", x)
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return (new_modelpatcher, new_clip)
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class CLIP:
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def __init__(self, config={}, embedding_directory=None, no_init=False):
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if no_init:
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return
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self.target_clip = config["target"]
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if "params" in config:
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params = config["params"]
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else:
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params = {}
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if self.target_clip == "ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder":
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clip = sd2_clip.SD2ClipModel
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tokenizer = sd2_clip.SD2Tokenizer
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elif self.target_clip == "ldm.modules.encoders.modules.FrozenCLIPEmbedder":
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clip = sd1_clip.SD1ClipModel
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tokenizer = sd1_clip.SD1Tokenizer
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self.cond_stage_model = clip(**(params))
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self.tokenizer = tokenizer(embedding_directory=embedding_directory)
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self.patcher = ModelPatcher(self.cond_stage_model)
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self.layer_idx = None
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def clone(self):
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n = CLIP(no_init=True)
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n.target_clip = self.target_clip
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n.patcher = self.patcher.clone()
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n.cond_stage_model = self.cond_stage_model
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n.tokenizer = self.tokenizer
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n.layer_idx = self.layer_idx
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return n
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def load_from_state_dict(self, sd):
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self.cond_stage_model.transformer.load_state_dict(sd, strict=False)
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def add_patches(self, patches, strength=1.0):
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return self.patcher.add_patches(patches, strength)
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def clip_layer(self, layer_idx):
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self.layer_idx = layer_idx
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def tokenize(self, text, return_word_ids=False):
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return self.tokenizer.tokenize_with_weights(text, return_word_ids)
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def encode_from_tokens(self, tokens, return_pooled=False):
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if self.layer_idx is not None:
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self.cond_stage_model.clip_layer(self.layer_idx)
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try:
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self.patcher.patch_model()
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cond = self.cond_stage_model.encode_token_weights(tokens)
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self.patcher.unpatch_model()
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except Exception as e:
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self.patcher.unpatch_model()
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raise e
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if return_pooled:
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eos_token_index = max(range(len(tokens[0])), key=tokens[0].__getitem__)
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pooled = cond[:, eos_token_index]
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return cond, pooled
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return cond
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def encode(self, text):
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tokens = self.tokenize(text)
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return self.encode_from_tokens(tokens)
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class VAE:
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def __init__(self, ckpt_path=None, scale_factor=0.18215, device=None, config=None):
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if config is None:
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#default SD1.x/SD2.x VAE parameters
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ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
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self.first_stage_model = AutoencoderKL(ddconfig, {'target': 'torch.nn.Identity'}, 4, monitor="val/rec_loss", ckpt_path=ckpt_path)
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else:
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self.first_stage_model = AutoencoderKL(**(config['params']), ckpt_path=ckpt_path)
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self.first_stage_model = self.first_stage_model.eval()
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self.scale_factor = scale_factor
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if device is None:
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device = model_management.get_torch_device()
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self.device = device
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def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
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decode_fn = lambda a: (self.first_stage_model.decode(1. / self.scale_factor * a.to(self.device)) + 1.0)
|
|
output = torch.clamp((
|
|
(utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8) +
|
|
utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8) +
|
|
utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8))
|
|
/ 3.0) / 2.0, min=0.0, max=1.0)
|
|
return output
|
|
|
|
def decode(self, samples_in):
|
|
model_management.unload_model()
|
|
self.first_stage_model = self.first_stage_model.to(self.device)
|
|
try:
|
|
free_memory = model_management.get_free_memory(self.device)
|
|
batch_number = int((free_memory * 0.7) / (2562 * samples_in.shape[2] * samples_in.shape[3] * 64))
|
|
batch_number = max(1, batch_number)
|
|
|
|
pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu")
|
|
for x in range(0, samples_in.shape[0], batch_number):
|
|
samples = samples_in[x:x+batch_number].to(self.device)
|
|
pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(1. / self.scale_factor * samples) + 1.0) / 2.0, min=0.0, max=1.0).cpu()
|
|
except model_management.OOM_EXCEPTION as e:
|
|
print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
|
|
pixel_samples = self.decode_tiled_(samples_in)
|
|
|
|
self.first_stage_model = self.first_stage_model.cpu()
|
|
pixel_samples = pixel_samples.cpu().movedim(1,-1)
|
|
return pixel_samples
|
|
|
|
def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16):
|
|
model_management.unload_model()
|
|
self.first_stage_model = self.first_stage_model.to(self.device)
|
|
output = self.decode_tiled_(samples, tile_x, tile_y, overlap)
|
|
self.first_stage_model = self.first_stage_model.cpu()
|
|
return output.movedim(1,-1)
|
|
|
|
def encode(self, pixel_samples):
|
|
model_management.unload_model()
|
|
self.first_stage_model = self.first_stage_model.to(self.device)
|
|
pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
|
|
samples = self.first_stage_model.encode(2. * pixel_samples - 1.).sample() * self.scale_factor
|
|
self.first_stage_model = self.first_stage_model.cpu()
|
|
samples = samples.cpu()
|
|
return samples
|
|
|
|
def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
|
|
model_management.unload_model()
|
|
self.first_stage_model = self.first_stage_model.to(self.device)
|
|
pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
|
|
samples = utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4)
|
|
samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4)
|
|
samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4)
|
|
samples /= 3.0
|
|
self.first_stage_model = self.first_stage_model.cpu()
|
|
samples = samples.cpu()
|
|
return samples
|
|
|
|
def resize_image_to(tensor, target_latent_tensor, batched_number):
|
|
tensor = utils.common_upscale(tensor, target_latent_tensor.shape[3] * 8, target_latent_tensor.shape[2] * 8, 'nearest-exact', "center")
|
|
target_batch_size = target_latent_tensor.shape[0]
|
|
|
|
current_batch_size = tensor.shape[0]
|
|
print(current_batch_size, target_batch_size)
|
|
if current_batch_size == 1:
|
|
return tensor
|
|
|
|
per_batch = target_batch_size // batched_number
|
|
tensor = tensor[:per_batch]
|
|
|
|
if per_batch > tensor.shape[0]:
|
|
tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0)
|
|
|
|
current_batch_size = tensor.shape[0]
|
|
if current_batch_size == target_batch_size:
|
|
return tensor
|
|
else:
|
|
return torch.cat([tensor] * batched_number, dim=0)
|
|
|
|
class ControlNet:
|
|
def __init__(self, control_model, device=None):
|
|
self.control_model = control_model
|
|
self.cond_hint_original = None
|
|
self.cond_hint = None
|
|
self.strength = 1.0
|
|
if device is None:
|
|
device = model_management.get_torch_device()
|
|
self.device = device
|
|
self.previous_controlnet = None
|
|
|
|
def get_control(self, x_noisy, t, cond_txt, batched_number):
|
|
control_prev = None
|
|
if self.previous_controlnet is not None:
|
|
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond_txt, batched_number)
|
|
|
|
output_dtype = x_noisy.dtype
|
|
if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
|
|
if self.cond_hint is not None:
|
|
del self.cond_hint
|
|
self.cond_hint = None
|
|
self.cond_hint = resize_image_to(self.cond_hint_original, x_noisy, batched_number).to(self.control_model.dtype).to(self.device)
|
|
|
|
if self.control_model.dtype == torch.float16:
|
|
precision_scope = torch.autocast
|
|
else:
|
|
precision_scope = contextlib.nullcontext
|
|
|
|
with precision_scope(model_management.get_autocast_device(self.device)):
|
|
self.control_model = model_management.load_if_low_vram(self.control_model)
|
|
control = self.control_model(x=x_noisy, hint=self.cond_hint, timesteps=t, context=cond_txt)
|
|
self.control_model = model_management.unload_if_low_vram(self.control_model)
|
|
out = {'middle':[], 'output': []}
|
|
autocast_enabled = torch.is_autocast_enabled()
|
|
|
|
for i in range(len(control)):
|
|
if i == (len(control) - 1):
|
|
key = 'middle'
|
|
index = 0
|
|
else:
|
|
key = 'output'
|
|
index = i
|
|
x = control[i]
|
|
x *= self.strength
|
|
if x.dtype != output_dtype and not autocast_enabled:
|
|
x = x.to(output_dtype)
|
|
|
|
if control_prev is not None and key in control_prev:
|
|
prev = control_prev[key][index]
|
|
if prev is not None:
|
|
x += prev
|
|
out[key].append(x)
|
|
if control_prev is not None and 'input' in control_prev:
|
|
out['input'] = control_prev['input']
|
|
return out
|
|
|
|
def set_cond_hint(self, cond_hint, strength=1.0):
|
|
self.cond_hint_original = cond_hint
|
|
self.strength = strength
|
|
return self
|
|
|
|
def set_previous_controlnet(self, controlnet):
|
|
self.previous_controlnet = controlnet
|
|
return self
|
|
|
|
def cleanup(self):
|
|
if self.previous_controlnet is not None:
|
|
self.previous_controlnet.cleanup()
|
|
if self.cond_hint is not None:
|
|
del self.cond_hint
|
|
self.cond_hint = None
|
|
|
|
def copy(self):
|
|
c = ControlNet(self.control_model)
|
|
c.cond_hint_original = self.cond_hint_original
|
|
c.strength = self.strength
|
|
return c
|
|
|
|
def get_models(self):
|
|
out = []
|
|
if self.previous_controlnet is not None:
|
|
out += self.previous_controlnet.get_models()
|
|
out.append(self.control_model)
|
|
return out
|
|
|
|
def load_controlnet(ckpt_path, model=None):
|
|
controlnet_data = utils.load_torch_file(ckpt_path)
|
|
pth_key = 'control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight'
|
|
pth = False
|
|
sd2 = False
|
|
key = 'input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight'
|
|
if pth_key in controlnet_data:
|
|
pth = True
|
|
key = pth_key
|
|
elif key in controlnet_data:
|
|
pass
|
|
else:
|
|
net = load_t2i_adapter(controlnet_data)
|
|
if net is None:
|
|
print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path)
|
|
return net
|
|
|
|
context_dim = controlnet_data[key].shape[1]
|
|
|
|
use_fp16 = False
|
|
if model_management.should_use_fp16() and controlnet_data[key].dtype == torch.float16:
|
|
use_fp16 = True
|
|
|
|
if context_dim == 768:
|
|
#SD1.x
|
|
control_model = cldm.ControlNet(image_size=32,
|
|
in_channels=4,
|
|
hint_channels=3,
|
|
model_channels=320,
|
|
attention_resolutions=[ 4, 2, 1 ],
|
|
num_res_blocks=2,
|
|
channel_mult=[ 1, 2, 4, 4 ],
|
|
num_heads=8,
|
|
use_spatial_transformer=True,
|
|
transformer_depth=1,
|
|
context_dim=context_dim,
|
|
use_checkpoint=True,
|
|
legacy=False,
|
|
use_fp16=use_fp16)
|
|
else:
|
|
#SD2.x
|
|
control_model = cldm.ControlNet(image_size=32,
|
|
in_channels=4,
|
|
hint_channels=3,
|
|
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=context_dim,
|
|
use_checkpoint=True,
|
|
legacy=False,
|
|
use_fp16=use_fp16)
|
|
if pth:
|
|
if 'difference' in controlnet_data:
|
|
if model is not None:
|
|
m = model.patch_model()
|
|
model_sd = m.state_dict()
|
|
for x in controlnet_data:
|
|
c_m = "control_model."
|
|
if x.startswith(c_m):
|
|
sd_key = "model.diffusion_model.{}".format(x[len(c_m):])
|
|
if sd_key in model_sd:
|
|
cd = controlnet_data[x]
|
|
cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
|
|
model.unpatch_model()
|
|
else:
|
|
print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
|
|
|
|
class WeightsLoader(torch.nn.Module):
|
|
pass
|
|
w = WeightsLoader()
|
|
w.control_model = control_model
|
|
w.load_state_dict(controlnet_data, strict=False)
|
|
else:
|
|
control_model.load_state_dict(controlnet_data, strict=False)
|
|
|
|
if use_fp16:
|
|
control_model = control_model.half()
|
|
|
|
control = ControlNet(control_model)
|
|
return control
|
|
|
|
class T2IAdapter:
|
|
def __init__(self, t2i_model, channels_in, device=None):
|
|
self.t2i_model = t2i_model
|
|
self.channels_in = channels_in
|
|
self.strength = 1.0
|
|
if device is None:
|
|
device = model_management.get_torch_device()
|
|
self.device = device
|
|
self.previous_controlnet = None
|
|
self.control_input = None
|
|
self.cond_hint_original = None
|
|
self.cond_hint = None
|
|
|
|
def get_control(self, x_noisy, t, cond_txt, batched_number):
|
|
control_prev = None
|
|
if self.previous_controlnet is not None:
|
|
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond_txt, batched_number)
|
|
|
|
if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
|
|
if self.cond_hint is not None:
|
|
del self.cond_hint
|
|
self.cond_hint = None
|
|
self.cond_hint = resize_image_to(self.cond_hint_original, x_noisy, batched_number).float().to(self.device)
|
|
if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
|
|
self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
|
|
self.t2i_model.to(self.device)
|
|
self.control_input = self.t2i_model(self.cond_hint)
|
|
self.t2i_model.cpu()
|
|
|
|
output_dtype = x_noisy.dtype
|
|
out = {'input':[]}
|
|
|
|
autocast_enabled = torch.is_autocast_enabled()
|
|
for i in range(len(self.control_input)):
|
|
key = 'input'
|
|
x = self.control_input[i] * self.strength
|
|
if x.dtype != output_dtype and not autocast_enabled:
|
|
x = x.to(output_dtype)
|
|
|
|
if control_prev is not None and key in control_prev:
|
|
index = len(control_prev[key]) - i * 3 - 3
|
|
prev = control_prev[key][index]
|
|
if prev is not None:
|
|
x += prev
|
|
out[key].insert(0, None)
|
|
out[key].insert(0, None)
|
|
out[key].insert(0, x)
|
|
|
|
if control_prev is not None and 'input' in control_prev:
|
|
for i in range(len(out['input'])):
|
|
if out['input'][i] is None:
|
|
out['input'][i] = control_prev['input'][i]
|
|
if control_prev is not None and 'middle' in control_prev:
|
|
out['middle'] = control_prev['middle']
|
|
if control_prev is not None and 'output' in control_prev:
|
|
out['output'] = control_prev['output']
|
|
return out
|
|
|
|
def set_cond_hint(self, cond_hint, strength=1.0):
|
|
self.cond_hint_original = cond_hint
|
|
self.strength = strength
|
|
return self
|
|
|
|
def set_previous_controlnet(self, controlnet):
|
|
self.previous_controlnet = controlnet
|
|
return self
|
|
|
|
def copy(self):
|
|
c = T2IAdapter(self.t2i_model, self.channels_in)
|
|
c.cond_hint_original = self.cond_hint_original
|
|
c.strength = self.strength
|
|
return c
|
|
|
|
def cleanup(self):
|
|
if self.previous_controlnet is not None:
|
|
self.previous_controlnet.cleanup()
|
|
if self.cond_hint is not None:
|
|
del self.cond_hint
|
|
self.cond_hint = None
|
|
|
|
def get_models(self):
|
|
out = []
|
|
if self.previous_controlnet is not None:
|
|
out += self.previous_controlnet.get_models()
|
|
return out
|
|
|
|
def load_t2i_adapter(t2i_data):
|
|
keys = t2i_data.keys()
|
|
if "body.0.in_conv.weight" in keys:
|
|
cin = t2i_data['body.0.in_conv.weight'].shape[1]
|
|
model_ad = adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
|
|
elif 'conv_in.weight' in keys:
|
|
cin = t2i_data['conv_in.weight'].shape[1]
|
|
model_ad = adapter.Adapter(cin=cin, channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False)
|
|
else:
|
|
return None
|
|
model_ad.load_state_dict(t2i_data)
|
|
return T2IAdapter(model_ad, cin // 64)
|
|
|
|
|
|
class StyleModel:
|
|
def __init__(self, model, device="cpu"):
|
|
self.model = model
|
|
|
|
def get_cond(self, input):
|
|
return self.model(input.last_hidden_state)
|
|
|
|
|
|
def load_style_model(ckpt_path):
|
|
model_data = utils.load_torch_file(ckpt_path)
|
|
keys = model_data.keys()
|
|
if "style_embedding" in keys:
|
|
model = adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
|
|
else:
|
|
raise Exception("invalid style model {}".format(ckpt_path))
|
|
model.load_state_dict(model_data)
|
|
return StyleModel(model)
|
|
|
|
|
|
def load_clip(ckpt_path, embedding_directory=None):
|
|
clip_data = utils.load_torch_file(ckpt_path)
|
|
config = {}
|
|
if "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data:
|
|
config['target'] = 'ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder'
|
|
else:
|
|
config['target'] = 'ldm.modules.encoders.modules.FrozenCLIPEmbedder'
|
|
clip = CLIP(config=config, embedding_directory=embedding_directory)
|
|
clip.load_from_state_dict(clip_data)
|
|
return clip
|
|
|
|
def load_gligen(ckpt_path):
|
|
data = utils.load_torch_file(ckpt_path)
|
|
model = gligen.load_gligen(data)
|
|
if model_management.should_use_fp16():
|
|
model = model.half()
|
|
return model
|
|
|
|
def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=None):
|
|
with open(config_path, 'r') as stream:
|
|
config = yaml.safe_load(stream)
|
|
model_config_params = config['model']['params']
|
|
clip_config = model_config_params['cond_stage_config']
|
|
scale_factor = model_config_params['scale_factor']
|
|
vae_config = model_config_params['first_stage_config']
|
|
|
|
fp16 = False
|
|
if "unet_config" in model_config_params:
|
|
if "params" in model_config_params["unet_config"]:
|
|
if "use_fp16" in model_config_params["unet_config"]["params"]:
|
|
fp16 = model_config_params["unet_config"]["params"]["use_fp16"]
|
|
|
|
clip = None
|
|
vae = None
|
|
|
|
class WeightsLoader(torch.nn.Module):
|
|
pass
|
|
|
|
w = WeightsLoader()
|
|
load_state_dict_to = []
|
|
if output_vae:
|
|
vae = VAE(scale_factor=scale_factor, config=vae_config)
|
|
w.first_stage_model = vae.first_stage_model
|
|
load_state_dict_to = [w]
|
|
|
|
if output_clip:
|
|
clip = CLIP(config=clip_config, embedding_directory=embedding_directory)
|
|
w.cond_stage_model = clip.cond_stage_model
|
|
load_state_dict_to = [w]
|
|
|
|
model = instantiate_from_config(config["model"])
|
|
sd = utils.load_torch_file(ckpt_path)
|
|
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
|
|
|
|
if fp16:
|
|
model = model.half()
|
|
|
|
return (ModelPatcher(model), clip, vae)
|
|
|
|
|
|
def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None):
|
|
sd = utils.load_torch_file(ckpt_path)
|
|
sd_keys = sd.keys()
|
|
clip = None
|
|
clipvision = None
|
|
vae = None
|
|
|
|
fp16 = model_management.should_use_fp16()
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class WeightsLoader(torch.nn.Module):
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pass
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w = WeightsLoader()
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load_state_dict_to = []
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if output_vae:
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vae = VAE()
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w.first_stage_model = vae.first_stage_model
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load_state_dict_to = [w]
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if output_clip:
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clip_config = {}
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if "cond_stage_model.model.transformer.resblocks.22.attn.out_proj.weight" in sd_keys:
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clip_config['target'] = 'ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder'
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else:
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clip_config['target'] = 'ldm.modules.encoders.modules.FrozenCLIPEmbedder'
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clip = CLIP(config=clip_config, embedding_directory=embedding_directory)
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w.cond_stage_model = clip.cond_stage_model
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load_state_dict_to = [w]
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clipvision_key = "embedder.model.visual.transformer.resblocks.0.attn.in_proj_weight"
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noise_aug_config = None
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if clipvision_key in sd_keys:
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size = sd[clipvision_key].shape[1]
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if output_clipvision:
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clipvision = clip_vision.load_clipvision_from_sd(sd)
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noise_aug_key = "noise_augmentor.betas"
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if noise_aug_key in sd_keys:
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noise_aug_config = {}
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params = {}
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noise_schedule_config = {}
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noise_schedule_config["timesteps"] = sd[noise_aug_key].shape[0]
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noise_schedule_config["beta_schedule"] = "squaredcos_cap_v2"
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params["noise_schedule_config"] = noise_schedule_config
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noise_aug_config['target'] = "ldm.modules.encoders.noise_aug_modules.CLIPEmbeddingNoiseAugmentation"
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if size == 1280: #h
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params["timestep_dim"] = 1024
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elif size == 1024: #l
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params["timestep_dim"] = 768
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noise_aug_config['params'] = params
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|
|
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sd_config = {
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"linear_start": 0.00085,
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|
"linear_end": 0.012,
|
|
"num_timesteps_cond": 1,
|
|
"log_every_t": 200,
|
|
"timesteps": 1000,
|
|
"first_stage_key": "jpg",
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|
"cond_stage_key": "txt",
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|
"image_size": 64,
|
|
"channels": 4,
|
|
"cond_stage_trainable": False,
|
|
"monitor": "val/loss_simple_ema",
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|
"scale_factor": 0.18215,
|
|
"use_ema": False,
|
|
}
|
|
|
|
unet_config = {
|
|
"use_checkpoint": True,
|
|
"image_size": 32,
|
|
"out_channels": 4,
|
|
"attention_resolutions": [
|
|
4,
|
|
2,
|
|
1
|
|
],
|
|
"num_res_blocks": 2,
|
|
"channel_mult": [
|
|
1,
|
|
2,
|
|
4,
|
|
4
|
|
],
|
|
"use_spatial_transformer": True,
|
|
"transformer_depth": 1,
|
|
"legacy": False
|
|
}
|
|
|
|
if len(sd['model.diffusion_model.input_blocks.1.1.proj_in.weight'].shape) == 2:
|
|
unet_config['use_linear_in_transformer'] = True
|
|
|
|
unet_config["use_fp16"] = fp16
|
|
unet_config["model_channels"] = sd['model.diffusion_model.input_blocks.0.0.weight'].shape[0]
|
|
unet_config["in_channels"] = sd['model.diffusion_model.input_blocks.0.0.weight'].shape[1]
|
|
unet_config["context_dim"] = sd['model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight'].shape[1]
|
|
|
|
sd_config["unet_config"] = {"target": "ldm.modules.diffusionmodules.openaimodel.UNetModel", "params": unet_config}
|
|
model_config = {"target": "ldm.models.diffusion.ddpm.LatentDiffusion", "params": sd_config}
|
|
|
|
if noise_aug_config is not None: #SD2.x unclip model
|
|
sd_config["noise_aug_config"] = noise_aug_config
|
|
sd_config["image_size"] = 96
|
|
sd_config["embedding_dropout"] = 0.25
|
|
sd_config["conditioning_key"] = 'crossattn-adm'
|
|
model_config["target"] = "ldm.models.diffusion.ddpm.ImageEmbeddingConditionedLatentDiffusion"
|
|
elif unet_config["in_channels"] > 4: #inpainting model
|
|
sd_config["conditioning_key"] = "hybrid"
|
|
sd_config["finetune_keys"] = None
|
|
model_config["target"] = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
|
|
else:
|
|
sd_config["conditioning_key"] = "crossattn"
|
|
|
|
if unet_config["context_dim"] == 1024:
|
|
unet_config["num_head_channels"] = 64 #SD2.x
|
|
else:
|
|
unet_config["num_heads"] = 8 #SD1.x
|
|
|
|
unclip = 'model.diffusion_model.label_emb.0.0.weight'
|
|
if unclip in sd_keys:
|
|
unet_config["num_classes"] = "sequential"
|
|
unet_config["adm_in_channels"] = sd[unclip].shape[1]
|
|
|
|
if unet_config["context_dim"] == 1024 and unet_config["in_channels"] == 4: #only SD2.x non inpainting models are v prediction
|
|
k = "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.norm1.bias"
|
|
out = sd[k]
|
|
if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
|
|
sd_config["parameterization"] = 'v'
|
|
|
|
model = instantiate_from_config(model_config)
|
|
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
|
|
|
|
if fp16:
|
|
model = model.half()
|
|
|
|
return (ModelPatcher(model), clip, vae, clipvision)
|