532 lines
18 KiB
Python
532 lines
18 KiB
Python
import torch
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from . import model_base
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from . import utils
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from . import sd1_clip
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from . import sd2_clip
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from . import sdxl_clip
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from . import sd3_clip
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from . import supported_models_base
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from . import latent_formats
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from . import diffusers_convert
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class SD15(supported_models_base.BASE):
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unet_config = {
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"context_dim": 768,
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"model_channels": 320,
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"use_linear_in_transformer": False,
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"adm_in_channels": None,
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"use_temporal_attention": False,
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}
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unet_extra_config = {
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"num_heads": 8,
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"num_head_channels": -1,
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}
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latent_format = latent_formats.SD15
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def process_clip_state_dict(self, state_dict):
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k = list(state_dict.keys())
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for x in k:
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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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state_dict[y] = state_dict.pop(x)
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if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict:
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ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids']
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if ids.dtype == torch.float32:
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state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
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replace_prefix = {}
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replace_prefix["cond_stage_model."] = "clip_l."
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state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
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return state_dict
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def process_clip_state_dict_for_saving(self, state_dict):
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pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
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for p in pop_keys:
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if p in state_dict:
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state_dict.pop(p)
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replace_prefix = {"clip_l.": "cond_stage_model."}
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return utils.state_dict_prefix_replace(state_dict, replace_prefix)
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def clip_target(self, state_dict={}):
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return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel)
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class SD20(supported_models_base.BASE):
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unet_config = {
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"context_dim": 1024,
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"adm_in_channels": None,
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"use_temporal_attention": False,
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}
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unet_extra_config = {
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"num_heads": -1,
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"num_head_channels": 64,
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"attn_precision": torch.float32,
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}
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latent_format = latent_formats.SD15
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def model_type(self, state_dict, prefix=""):
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if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
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k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix)
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out = state_dict.get(k, None)
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if out is not None and torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
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return model_base.ModelType.V_PREDICTION
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return model_base.ModelType.EPS
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def process_clip_state_dict(self, state_dict):
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replace_prefix = {}
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replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format
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replace_prefix["cond_stage_model.model."] = "clip_h."
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state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
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state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.")
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return state_dict
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def process_clip_state_dict_for_saving(self, state_dict):
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replace_prefix = {}
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replace_prefix["clip_h"] = "cond_stage_model.model"
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state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
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state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
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return state_dict
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def clip_target(self, state_dict={}):
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return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)
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class SD21UnclipL(SD20):
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unet_config = {
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"context_dim": 1024,
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"adm_in_channels": 1536,
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"use_temporal_attention": False,
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}
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clip_vision_prefix = "embedder.model.visual."
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noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768}
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class SD21UnclipH(SD20):
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unet_config = {
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"context_dim": 1024,
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"adm_in_channels": 2048,
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"use_temporal_attention": False,
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}
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clip_vision_prefix = "embedder.model.visual."
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noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024}
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class SDXLRefiner(supported_models_base.BASE):
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unet_config = {
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"model_channels": 384,
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"use_linear_in_transformer": True,
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"context_dim": 1280,
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"adm_in_channels": 2560,
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"transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0],
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"use_temporal_attention": False,
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}
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latent_format = latent_formats.SDXL
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def get_model(self, state_dict, prefix="", device=None):
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return model_base.SDXLRefiner(self, device=device)
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def process_clip_state_dict(self, state_dict):
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keys_to_replace = {}
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replace_prefix = {}
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replace_prefix["conditioner.embedders.0.model."] = "clip_g."
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state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
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state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
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state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
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return state_dict
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def process_clip_state_dict_for_saving(self, state_dict):
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replace_prefix = {}
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state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
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if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g:
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state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
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replace_prefix["clip_g"] = "conditioner.embedders.0.model"
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state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
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return state_dict_g
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def clip_target(self, state_dict={}):
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return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel)
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class SDXL(supported_models_base.BASE):
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unet_config = {
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"transformer_depth": [0, 0, 2, 2, 10, 10],
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"context_dim": 2048,
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"adm_in_channels": 2816,
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"use_temporal_attention": False,
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}
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latent_format = latent_formats.SDXL
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def model_type(self, state_dict, prefix=""):
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if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5
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self.latent_format = latent_formats.SDXL_Playground_2_5()
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self.sampling_settings["sigma_data"] = 0.5
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self.sampling_settings["sigma_max"] = 80.0
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self.sampling_settings["sigma_min"] = 0.002
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return model_base.ModelType.EDM
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elif "edm_vpred.sigma_max" in state_dict:
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self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item())
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if "edm_vpred.sigma_min" in state_dict:
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self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item())
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return model_base.ModelType.V_PREDICTION_EDM
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elif "v_pred" in state_dict:
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return model_base.ModelType.V_PREDICTION
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else:
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return model_base.ModelType.EPS
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def get_model(self, state_dict, prefix="", device=None):
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out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device)
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if self.inpaint_model():
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out.set_inpaint()
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return out
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def process_clip_state_dict(self, state_dict):
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keys_to_replace = {}
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replace_prefix = {}
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replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model"
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replace_prefix["conditioner.embedders.1.model."] = "clip_g."
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state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
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state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
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state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
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return state_dict
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def process_clip_state_dict_for_saving(self, state_dict):
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replace_prefix = {}
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keys_to_replace = {}
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state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
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for k in state_dict:
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if k.startswith("clip_l"):
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state_dict_g[k] = state_dict[k]
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state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1))
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pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
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for p in pop_keys:
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if p in state_dict_g:
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state_dict_g.pop(p)
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replace_prefix["clip_g"] = "conditioner.embedders.1.model"
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replace_prefix["clip_l"] = "conditioner.embedders.0"
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state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
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return state_dict_g
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def clip_target(self, state_dict={}):
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return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel)
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class SSD1B(SDXL):
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unet_config = {
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"transformer_depth": [0, 0, 2, 2, 4, 4],
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"context_dim": 2048,
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"adm_in_channels": 2816,
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"use_temporal_attention": False,
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}
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class Segmind_Vega(SDXL):
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unet_config = {
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"transformer_depth": [0, 0, 1, 1, 2, 2],
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"context_dim": 2048,
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"adm_in_channels": 2816,
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"use_temporal_attention": False,
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}
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class KOALA_700M(SDXL):
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unet_config = {
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"transformer_depth": [0, 2, 5],
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"context_dim": 2048,
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"adm_in_channels": 2816,
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"use_temporal_attention": False,
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}
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class KOALA_1B(SDXL):
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unet_config = {
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"model_channels": 320,
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"use_linear_in_transformer": True,
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"transformer_depth": [0, 2, 6],
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"context_dim": 2048,
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"adm_in_channels": 2816,
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"use_temporal_attention": False,
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}
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class SVD_img2vid(supported_models_base.BASE):
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unet_config = {
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"model_channels": 320,
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"in_channels": 8,
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"use_linear_in_transformer": True,
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"transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
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"context_dim": 1024,
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"adm_in_channels": 768,
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"use_temporal_attention": True,
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"use_temporal_resblock": True
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}
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unet_extra_config = {
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"num_heads": -1,
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"num_head_channels": 64,
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"attn_precision": torch.float32,
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}
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clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual."
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latent_format = latent_formats.SD15
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sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002}
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def get_model(self, state_dict, prefix="", device=None):
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out = model_base.SVD_img2vid(self, device=device)
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return out
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def clip_target(self, state_dict={}):
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return None
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class SV3D_u(SVD_img2vid):
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unet_config = {
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"model_channels": 320,
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"in_channels": 8,
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"use_linear_in_transformer": True,
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"transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
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"context_dim": 1024,
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"adm_in_channels": 256,
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"use_temporal_attention": True,
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"use_temporal_resblock": True
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}
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vae_key_prefix = ["conditioner.embedders.1.encoder."]
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def get_model(self, state_dict, prefix="", device=None):
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out = model_base.SV3D_u(self, device=device)
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return out
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class SV3D_p(SV3D_u):
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unet_config = {
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"model_channels": 320,
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"in_channels": 8,
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"use_linear_in_transformer": True,
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"transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
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"context_dim": 1024,
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"adm_in_channels": 1280,
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"use_temporal_attention": True,
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"use_temporal_resblock": True
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}
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def get_model(self, state_dict, prefix="", device=None):
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out = model_base.SV3D_p(self, device=device)
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return out
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class Stable_Zero123(supported_models_base.BASE):
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unet_config = {
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"context_dim": 768,
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"model_channels": 320,
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"use_linear_in_transformer": False,
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"adm_in_channels": None,
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"use_temporal_attention": False,
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"in_channels": 8,
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}
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unet_extra_config = {
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"num_heads": 8,
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"num_head_channels": -1,
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}
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required_keys = {
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"cc_projection.weight": None,
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"cc_projection.bias": None,
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}
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clip_vision_prefix = "cond_stage_model.model.visual."
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latent_format = latent_formats.SD15
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def get_model(self, state_dict, prefix="", device=None):
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out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"])
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return out
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def clip_target(self, state_dict={}):
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return None
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class SD_X4Upscaler(SD20):
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unet_config = {
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"context_dim": 1024,
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"model_channels": 256,
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'in_channels': 7,
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"use_linear_in_transformer": True,
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"adm_in_channels": None,
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"use_temporal_attention": False,
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}
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unet_extra_config = {
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"disable_self_attentions": [True, True, True, False],
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"num_classes": 1000,
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"num_heads": 8,
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"num_head_channels": -1,
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}
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latent_format = latent_formats.SD_X4
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sampling_settings = {
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"linear_start": 0.0001,
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"linear_end": 0.02,
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}
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def get_model(self, state_dict, prefix="", device=None):
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out = model_base.SD_X4Upscaler(self, device=device)
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return out
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class Stable_Cascade_C(supported_models_base.BASE):
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unet_config = {
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"stable_cascade_stage": 'c',
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}
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unet_extra_config = {}
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latent_format = latent_formats.SC_Prior
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supported_inference_dtypes = [torch.bfloat16, torch.float32]
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sampling_settings = {
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"shift": 2.0,
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}
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vae_key_prefix = ["vae."]
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text_encoder_key_prefix = ["text_encoder."]
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clip_vision_prefix = "clip_l_vision."
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def process_unet_state_dict(self, state_dict):
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key_list = list(state_dict.keys())
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for y in ["weight", "bias"]:
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suffix = "in_proj_{}".format(y)
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keys = filter(lambda a: a.endswith(suffix), key_list)
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for k_from in keys:
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weights = state_dict.pop(k_from)
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prefix = k_from[:-(len(suffix) + 1)]
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shape_from = weights.shape[0] // 3
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for x in range(3):
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p = ["to_q", "to_k", "to_v"]
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k_to = "{}.{}.{}".format(prefix, p[x], y)
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state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)]
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return state_dict
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def process_clip_state_dict(self, state_dict):
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state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
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if "clip_g.text_projection" in state_dict:
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state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1)
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return state_dict
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def get_model(self, state_dict, prefix="", device=None):
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out = model_base.StableCascade_C(self, device=device)
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return out
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def clip_target(self, state_dict={}):
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return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel)
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class Stable_Cascade_B(Stable_Cascade_C):
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unet_config = {
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"stable_cascade_stage": 'b',
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}
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unet_extra_config = {}
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latent_format = latent_formats.SC_B
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supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
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sampling_settings = {
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"shift": 1.0,
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}
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clip_vision_prefix = None
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def get_model(self, state_dict, prefix="", device=None):
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out = model_base.StableCascade_B(self, device=device)
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return out
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|
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class SD15_instructpix2pix(SD15):
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unet_config = {
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"context_dim": 768,
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"model_channels": 320,
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"use_linear_in_transformer": False,
|
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"adm_in_channels": None,
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"use_temporal_attention": False,
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"in_channels": 8,
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}
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|
|
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def get_model(self, state_dict, prefix="", device=None):
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return model_base.SD15_instructpix2pix(self, device=device)
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|
|
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class SDXL_instructpix2pix(SDXL):
|
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unet_config = {
|
|
"model_channels": 320,
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"use_linear_in_transformer": True,
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|
"transformer_depth": [0, 0, 2, 2, 10, 10],
|
|
"context_dim": 2048,
|
|
"adm_in_channels": 2816,
|
|
"use_temporal_attention": False,
|
|
"in_channels": 8,
|
|
}
|
|
|
|
def get_model(self, state_dict, prefix="", device=None):
|
|
return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device)
|
|
|
|
class SD3(supported_models_base.BASE):
|
|
unet_config = {
|
|
"in_channels": 16,
|
|
"pos_embed_scaling_factor": None,
|
|
}
|
|
|
|
sampling_settings = {
|
|
"shift": 3.0,
|
|
}
|
|
|
|
unet_extra_config = {}
|
|
latent_format = latent_formats.SD3
|
|
text_encoder_key_prefix = ["text_encoders."]
|
|
|
|
def get_model(self, state_dict, prefix="", device=None):
|
|
out = model_base.SD3(self, device=device)
|
|
return out
|
|
|
|
def clip_target(self, state_dict={}):
|
|
clip_l = False
|
|
clip_g = False
|
|
t5 = False
|
|
pref = self.text_encoder_key_prefix[0]
|
|
if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
|
|
clip_l = True
|
|
if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
|
|
clip_g = True
|
|
if "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref) in state_dict:
|
|
t5 = True
|
|
|
|
class SD3ClipModel(sd3_clip.SD3ClipModel):
|
|
def __init__(self, device="cpu", dtype=None):
|
|
super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, device=device, dtype=dtype)
|
|
|
|
return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, SD3ClipModel)
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|
|
|
|
|
models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3]
|
|
|
|
models += [SVD_img2vid]
|