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| import torch | |
| from . import model_base | |
| from . import utils | |
| from . import sd1_clip | |
| from . import sd2_clip | |
| from . import sdxl_clip | |
| from . import supported_models_base | |
| from . import latent_formats | |
| from . import diffusers_convert | |
| class SD15(supported_models_base.BASE): | |
| unet_config = { | |
| "context_dim": 768, | |
| "model_channels": 320, | |
| "use_linear_in_transformer": False, | |
| "adm_in_channels": None, | |
| "use_temporal_attention": False, | |
| } | |
| unet_extra_config = { | |
| "num_heads": 8, | |
| "num_head_channels": -1, | |
| } | |
| latent_format = latent_formats.SD15 | |
| def process_clip_state_dict(self, state_dict): | |
| k = list(state_dict.keys()) | |
| for x in k: | |
| if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): | |
| y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") | |
| state_dict[y] = state_dict.pop(x) | |
| if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict: | |
| ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] | |
| if ids.dtype == torch.float32: | |
| state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() | |
| replace_prefix = {} | |
| replace_prefix["cond_stage_model."] = "cond_stage_model.clip_l." | |
| state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) | |
| return state_dict | |
| def process_clip_state_dict_for_saving(self, state_dict): | |
| replace_prefix = {"clip_l.": "cond_stage_model."} | |
| return utils.state_dict_prefix_replace(state_dict, replace_prefix) | |
| def clip_target(self): | |
| return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel) | |
| class SD20(supported_models_base.BASE): | |
| unet_config = { | |
| "context_dim": 1024, | |
| "model_channels": 320, | |
| "use_linear_in_transformer": True, | |
| "adm_in_channels": None, | |
| "use_temporal_attention": False, | |
| } | |
| latent_format = latent_formats.SD15 | |
| def model_type(self, state_dict, prefix=""): | |
| if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction | |
| k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix) | |
| out = state_dict[k] | |
| if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out. | |
| return model_base.ModelType.V_PREDICTION | |
| return model_base.ModelType.EPS | |
| def process_clip_state_dict(self, state_dict): | |
| replace_prefix = {} | |
| replace_prefix["conditioner.embedders.0.model."] = "cond_stage_model.model." #SD2 in sgm format | |
| state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) | |
| state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.clip_h.transformer.text_model.", 24) | |
| return state_dict | |
| def process_clip_state_dict_for_saving(self, state_dict): | |
| replace_prefix = {} | |
| replace_prefix["clip_h"] = "cond_stage_model.model" | |
| state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) | |
| state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict) | |
| return state_dict | |
| def clip_target(self): | |
| return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel) | |
| class SD21UnclipL(SD20): | |
| unet_config = { | |
| "context_dim": 1024, | |
| "model_channels": 320, | |
| "use_linear_in_transformer": True, | |
| "adm_in_channels": 1536, | |
| "use_temporal_attention": False, | |
| } | |
| clip_vision_prefix = "embedder.model.visual." | |
| noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768} | |
| class SD21UnclipH(SD20): | |
| unet_config = { | |
| "context_dim": 1024, | |
| "model_channels": 320, | |
| "use_linear_in_transformer": True, | |
| "adm_in_channels": 2048, | |
| "use_temporal_attention": False, | |
| } | |
| clip_vision_prefix = "embedder.model.visual." | |
| noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024} | |
| class SDXLRefiner(supported_models_base.BASE): | |
| unet_config = { | |
| "model_channels": 384, | |
| "use_linear_in_transformer": True, | |
| "context_dim": 1280, | |
| "adm_in_channels": 2560, | |
| "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0], | |
| "use_temporal_attention": False, | |
| } | |
| latent_format = latent_formats.SDXL | |
| def get_model(self, state_dict, prefix="", device=None): | |
| return model_base.SDXLRefiner(self, device=device) | |
| def process_clip_state_dict(self, state_dict): | |
| keys_to_replace = {} | |
| replace_prefix = {} | |
| state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.0.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) | |
| keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection" | |
| keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" | |
| state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) | |
| return state_dict | |
| def process_clip_state_dict_for_saving(self, state_dict): | |
| replace_prefix = {} | |
| state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") | |
| if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: | |
| state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") | |
| replace_prefix["clip_g"] = "conditioner.embedders.0.model" | |
| state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) | |
| return state_dict_g | |
| def clip_target(self): | |
| return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel) | |
| class SDXL(supported_models_base.BASE): | |
| unet_config = { | |
| "model_channels": 320, | |
| "use_linear_in_transformer": True, | |
| "transformer_depth": [0, 0, 2, 2, 10, 10], | |
| "context_dim": 2048, | |
| "adm_in_channels": 2816, | |
| "use_temporal_attention": False, | |
| } | |
| latent_format = latent_formats.SDXL | |
| def model_type(self, state_dict, prefix=""): | |
| if "v_pred" in state_dict: | |
| return model_base.ModelType.V_PREDICTION | |
| else: | |
| return model_base.ModelType.EPS | |
| def get_model(self, state_dict, prefix="", device=None): | |
| out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) | |
| if self.inpaint_model(): | |
| out.set_inpaint() | |
| return out | |
| def process_clip_state_dict(self, state_dict): | |
| keys_to_replace = {} | |
| replace_prefix = {} | |
| replace_prefix["conditioner.embedders.0.transformer.text_model"] = "cond_stage_model.clip_l.transformer.text_model" | |
| state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.1.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) | |
| keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection" | |
| keys_to_replace["conditioner.embedders.1.model.text_projection.weight"] = "cond_stage_model.clip_g.text_projection" | |
| keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" | |
| state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) | |
| state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) | |
| return state_dict | |
| def process_clip_state_dict_for_saving(self, state_dict): | |
| replace_prefix = {} | |
| keys_to_replace = {} | |
| state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") | |
| if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: | |
| state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") | |
| for k in state_dict: | |
| if k.startswith("clip_l"): | |
| state_dict_g[k] = state_dict[k] | |
| replace_prefix["clip_g"] = "conditioner.embedders.1.model" | |
| replace_prefix["clip_l"] = "conditioner.embedders.0" | |
| state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) | |
| return state_dict_g | |
| def clip_target(self): | |
| return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel) | |
| class SSD1B(SDXL): | |
| unet_config = { | |
| "model_channels": 320, | |
| "use_linear_in_transformer": True, | |
| "transformer_depth": [0, 0, 2, 2, 4, 4], | |
| "context_dim": 2048, | |
| "adm_in_channels": 2816, | |
| "use_temporal_attention": False, | |
| } | |
| class Segmind_Vega(SDXL): | |
| unet_config = { | |
| "model_channels": 320, | |
| "use_linear_in_transformer": True, | |
| "transformer_depth": [0, 0, 1, 1, 2, 2], | |
| "context_dim": 2048, | |
| "adm_in_channels": 2816, | |
| "use_temporal_attention": False, | |
| } | |
| class SVD_img2vid(supported_models_base.BASE): | |
| unet_config = { | |
| "model_channels": 320, | |
| "in_channels": 8, | |
| "use_linear_in_transformer": True, | |
| "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], | |
| "context_dim": 1024, | |
| "adm_in_channels": 768, | |
| "use_temporal_attention": True, | |
| "use_temporal_resblock": True | |
| } | |
| clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual." | |
| latent_format = latent_formats.SD15 | |
| sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002} | |
| def get_model(self, state_dict, prefix="", device=None): | |
| out = model_base.SVD_img2vid(self, device=device) | |
| return out | |
| def clip_target(self): | |
| return None | |
| class Stable_Zero123(supported_models_base.BASE): | |
| unet_config = { | |
| "context_dim": 768, | |
| "model_channels": 320, | |
| "use_linear_in_transformer": False, | |
| "adm_in_channels": None, | |
| "use_temporal_attention": False, | |
| "in_channels": 8, | |
| } | |
| unet_extra_config = { | |
| "num_heads": 8, | |
| "num_head_channels": -1, | |
| } | |
| clip_vision_prefix = "cond_stage_model.model.visual." | |
| latent_format = latent_formats.SD15 | |
| def get_model(self, state_dict, prefix="", device=None): | |
| out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"]) | |
| return out | |
| def clip_target(self): | |
| return None | |
| class SD_X4Upscaler(SD20): | |
| unet_config = { | |
| "context_dim": 1024, | |
| "model_channels": 256, | |
| 'in_channels': 7, | |
| "use_linear_in_transformer": True, | |
| "adm_in_channels": None, | |
| "use_temporal_attention": False, | |
| } | |
| unet_extra_config = { | |
| "disable_self_attentions": [True, True, True, False], | |
| "num_classes": 1000, | |
| "num_heads": 8, | |
| "num_head_channels": -1, | |
| } | |
| latent_format = latent_formats.SD_X4 | |
| sampling_settings = { | |
| "linear_start": 0.0001, | |
| "linear_end": 0.02, | |
| } | |
| def get_model(self, state_dict, prefix="", device=None): | |
| out = model_base.SD_X4Upscaler(self, device=device) | |
| return out | |
| models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler] | |
| models += [SVD_img2vid] | |