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 class Stable_Cascade_C(supported_models_base.BASE): unet_config = { "stable_cascade_stage": 'c', } unet_extra_config = {} latent_format = latent_formats.SC_Prior supported_inference_dtypes = [torch.bfloat16, torch.float32] sampling_settings = { "shift": 2.0, } def process_unet_state_dict(self, state_dict): key_list = list(state_dict.keys()) for y in ["weight", "bias"]: suffix = "in_proj_{}".format(y) keys = filter(lambda a: a.endswith(suffix), key_list) for k_from in keys: weights = state_dict.pop(k_from) prefix = k_from[:-(len(suffix) + 1)] shape_from = weights.shape[0] // 3 for x in range(3): p = ["to_q", "to_k", "to_v"] k_to = "{}.{}.{}".format(prefix, p[x], y) state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)] return state_dict def get_model(self, state_dict, prefix="", device=None): out = model_base.StableCascade_C(self, device=device) return out def clip_target(self): return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel) class Stable_Cascade_B(Stable_Cascade_C): unet_config = { "stable_cascade_stage": 'b', } unet_extra_config = {} latent_format = latent_formats.SC_B supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] sampling_settings = { "shift": 1.0, } def get_model(self, state_dict, prefix="", device=None): out = model_base.StableCascade_B(self, device=device) return out models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B] models += [SVD_img2vid]