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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 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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} |
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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."] = "cond_stage_model.clip_l." |
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state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) |
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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 = {"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): |
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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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} |
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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: |
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k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix) |
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out = state_dict[k] |
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if torch.std(out, unbiased=False) > 0.09: |
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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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state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.clip_h.transformer.text_model.", 24) |
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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): |
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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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} |
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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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} |
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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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} |
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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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state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.0.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) |
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keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection" |
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keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" |
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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): |
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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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} |
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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 "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"] = "cond_stage_model.clip_l.transformer.text_model" |
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state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.1.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) |
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keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection" |
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keys_to_replace["conditioner.embedders.1.model.text_projection.weight"] = "cond_stage_model.clip_g.text_projection" |
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keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" |
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state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) |
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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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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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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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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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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): |
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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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} |
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models = [SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B] |
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