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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."] = "clip_l." | |
| state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) | |
| 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."] = "clip_h." #SD2 in sgm format | |
| replace_prefix["cond_stage_model.model."] = "clip_h." | |
| state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) | |
| state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.") | |
| 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 = {} | |
| replace_prefix["conditioner.embedders.0.model."] = "clip_g." | |
| state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) | |
| state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") | |
| 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 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5 | |
| self.latent_format = latent_formats.SDXL_Playground_2_5() | |
| self.sampling_settings["sigma_data"] = 0.5 | |
| self.sampling_settings["sigma_max"] = 80.0 | |
| self.sampling_settings["sigma_min"] = 0.002 | |
| return model_base.ModelType.EDM | |
| elif "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"] = "clip_l.transformer.text_model" | |
| replace_prefix["conditioner.embedders.1.model."] = "clip_g." | |
| state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) | |
| state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) | |
| state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") | |
| 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") | |
| for k in state_dict: | |
| if k.startswith("clip_l"): | |
| state_dict_g[k] = state_dict[k] | |
| state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1)) | |
| pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] | |
| for p in pop_keys: | |
| if p in state_dict_g: | |
| state_dict_g.pop(p) | |
| 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 KOALA_700M(SDXL): | |
| unet_config = { | |
| "model_channels": 320, | |
| "use_linear_in_transformer": True, | |
| "transformer_depth": [0, 2, 5], | |
| "context_dim": 2048, | |
| "adm_in_channels": 2816, | |
| "use_temporal_attention": False, | |
| } | |
| class KOALA_1B(SDXL): | |
| unet_config = { | |
| "model_channels": 320, | |
| "use_linear_in_transformer": True, | |
| "transformer_depth": [0, 2, 6], | |
| "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, | |
| } | |
| vae_key_prefix = ["vae."] | |
| text_encoder_key_prefix = ["text_encoder."] | |
| clip_vision_prefix = "clip_l_vision." | |
| 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 process_clip_state_dict(self, state_dict): | |
| state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) | |
| if "clip_g.text_projection" in state_dict: | |
| state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 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, | |
| } | |
| clip_vision_prefix = None | |
| 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, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B] | |
| models += [SVD_img2vid] | |