| import torch
|
| from . import model_base
|
| from . import utils
|
|
|
| from . import sd1_clip
|
| from . import sdxl_clip
|
| import comfy.text_encoders.sd2_clip
|
| import comfy.text_encoders.sd3_clip
|
| import comfy.text_encoders.sa_t5
|
| import comfy.text_encoders.aura_t5
|
| import comfy.text_encoders.hydit
|
| import comfy.text_encoders.flux
|
|
|
| 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
|
| memory_usage_factor = 1.0
|
|
|
| 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):
|
| pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
|
| for p in pop_keys:
|
| if p in state_dict:
|
| state_dict.pop(p)
|
|
|
| replace_prefix = {"clip_l.": "cond_stage_model."}
|
| return utils.state_dict_prefix_replace(state_dict, replace_prefix)
|
|
|
| def clip_target(self, state_dict={}):
|
| 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,
|
| }
|
|
|
| unet_extra_config = {
|
| "num_heads": -1,
|
| "num_head_channels": 64,
|
| "attn_precision": torch.float32,
|
| }
|
|
|
| latent_format = latent_formats.SD15
|
| memory_usage_factor = 1.0
|
|
|
| def model_type(self, state_dict, prefix=""):
|
| if self.unet_config["in_channels"] == 4:
|
| k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix)
|
| out = state_dict.get(k, None)
|
| if out is not None and torch.std(out, unbiased=False) > 0.09:
|
| 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."
|
| 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, state_dict={}):
|
| return supported_models_base.ClipTarget(comfy.text_encoders.sd2_clip.SD2Tokenizer, comfy.text_encoders.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
|
| memory_usage_factor = 1.0
|
|
|
| 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, state_dict={}):
|
| 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
|
|
|
| memory_usage_factor = 0.7
|
|
|
| def model_type(self, state_dict, prefix=""):
|
| if 'edm_mean' in state_dict and 'edm_std' in state_dict:
|
| 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 "edm_vpred.sigma_max" in state_dict:
|
| self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item())
|
| if "edm_vpred.sigma_min" in state_dict:
|
| self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item())
|
| return model_base.ModelType.V_PREDICTION_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, state_dict={}):
|
| 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
|
| }
|
|
|
| unet_extra_config = {
|
| "num_heads": -1,
|
| "num_head_channels": 64,
|
| "attn_precision": torch.float32,
|
| }
|
|
|
| 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, state_dict={}):
|
| return None
|
|
|
| class SV3D_u(SVD_img2vid):
|
| 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": 256,
|
| "use_temporal_attention": True,
|
| "use_temporal_resblock": True
|
| }
|
|
|
| vae_key_prefix = ["conditioner.embedders.1.encoder."]
|
|
|
| def get_model(self, state_dict, prefix="", device=None):
|
| out = model_base.SV3D_u(self, device=device)
|
| return out
|
|
|
| class SV3D_p(SV3D_u):
|
| 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": 1280,
|
| "use_temporal_attention": True,
|
| "use_temporal_resblock": True
|
| }
|
|
|
|
|
| def get_model(self, state_dict, prefix="", device=None):
|
| out = model_base.SV3D_p(self, device=device)
|
| return out
|
|
|
| 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,
|
| }
|
|
|
| required_keys = {
|
| "cc_projection.weight": None,
|
| "cc_projection.bias": None,
|
| }
|
|
|
| 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, state_dict={}):
|
| 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, state_dict={}):
|
| 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
|
|
|
| class SD15_instructpix2pix(SD15):
|
| unet_config = {
|
| "context_dim": 768,
|
| "model_channels": 320,
|
| "use_linear_in_transformer": False,
|
| "adm_in_channels": None,
|
| "use_temporal_attention": False,
|
| "in_channels": 8,
|
| }
|
|
|
| def get_model(self, state_dict, prefix="", device=None):
|
| return model_base.SD15_instructpix2pix(self, device=device)
|
|
|
| class SDXL_instructpix2pix(SDXL):
|
| 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,
|
| "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
|
|
|
| memory_usage_factor = 1.2
|
|
|
| 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
|
| dtype_t5 = None
|
| 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
|
| t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
|
| if t5_key in state_dict:
|
| t5 = True
|
| dtype_t5 = state_dict[t5_key].dtype
|
|
|
| return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
|
|
|
| class StableAudio(supported_models_base.BASE):
|
| unet_config = {
|
| "audio_model": "dit1.0",
|
| }
|
|
|
| sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03}
|
|
|
| unet_extra_config = {}
|
| latent_format = latent_formats.StableAudio1
|
|
|
| text_encoder_key_prefix = ["text_encoders."]
|
| vae_key_prefix = ["pretransform.model."]
|
|
|
| def get_model(self, state_dict, prefix="", device=None):
|
| seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True)
|
| seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True)
|
| return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device)
|
|
|
| def process_unet_state_dict(self, state_dict):
|
| for k in list(state_dict.keys()):
|
| if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"):
|
| state_dict.pop(k)
|
| return state_dict
|
|
|
| def process_unet_state_dict_for_saving(self, state_dict):
|
| replace_prefix = {"": "model.model."}
|
| return utils.state_dict_prefix_replace(state_dict, replace_prefix)
|
|
|
| def clip_target(self, state_dict={}):
|
| return supported_models_base.ClipTarget(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model)
|
|
|
| class AuraFlow(supported_models_base.BASE):
|
| unet_config = {
|
| "cond_seq_dim": 2048,
|
| }
|
|
|
| sampling_settings = {
|
| "multiplier": 1.0,
|
| "shift": 1.73,
|
| }
|
|
|
| unet_extra_config = {}
|
| latent_format = latent_formats.SDXL
|
|
|
| vae_key_prefix = ["vae."]
|
| text_encoder_key_prefix = ["text_encoders."]
|
|
|
| def get_model(self, state_dict, prefix="", device=None):
|
| out = model_base.AuraFlow(self, device=device)
|
| return out
|
|
|
| def clip_target(self, state_dict={}):
|
| return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model)
|
|
|
| class HunyuanDiT(supported_models_base.BASE):
|
| unet_config = {
|
| "image_model": "hydit",
|
| }
|
|
|
| unet_extra_config = {
|
| "attn_precision": torch.float32,
|
| }
|
|
|
| sampling_settings = {
|
| "linear_start": 0.00085,
|
| "linear_end": 0.018,
|
| }
|
|
|
| latent_format = latent_formats.SDXL
|
|
|
| vae_key_prefix = ["vae."]
|
| text_encoder_key_prefix = ["text_encoders."]
|
|
|
| def get_model(self, state_dict, prefix="", device=None):
|
| out = model_base.HunyuanDiT(self, device=device)
|
| return out
|
|
|
| def clip_target(self, state_dict={}):
|
| return supported_models_base.ClipTarget(comfy.text_encoders.hydit.HyditTokenizer, comfy.text_encoders.hydit.HyditModel)
|
|
|
| class HunyuanDiT1(HunyuanDiT):
|
| unet_config = {
|
| "image_model": "hydit1",
|
| }
|
|
|
| unet_extra_config = {}
|
|
|
| sampling_settings = {
|
| "linear_start" : 0.00085,
|
| "linear_end" : 0.03,
|
| }
|
|
|
| class Flux(supported_models_base.BASE):
|
| unet_config = {
|
| "image_model": "flux",
|
| "guidance_embed": True,
|
| }
|
|
|
| sampling_settings = {
|
| }
|
|
|
| unet_extra_config = {}
|
| latent_format = latent_formats.Flux
|
|
|
| memory_usage_factor = 2.8
|
|
|
| supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
|
|
|
| vae_key_prefix = ["vae."]
|
| text_encoder_key_prefix = ["text_encoders."]
|
|
|
| def get_model(self, state_dict, prefix="", device=None):
|
| out = model_base.Flux(self, device=device)
|
| return out
|
|
|
| def clip_target(self, state_dict={}):
|
| pref = self.text_encoder_key_prefix[0]
|
| t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
|
| if t5_key in state_dict:
|
| dtype_t5 = state_dict[t5_key].dtype
|
| return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5))
|
|
|
| class FluxSchnell(Flux):
|
| unet_config = {
|
| "image_model": "flux",
|
| "guidance_embed": False,
|
| }
|
|
|
| sampling_settings = {
|
| "multiplier": 1.0,
|
| "shift": 1.0,
|
| }
|
|
|
| def get_model(self, state_dict, prefix="", device=None):
|
| out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device)
|
| return out
|
|
|
|
|
| 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, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell]
|
|
|
| models += [SVD_img2vid]
|
|
|
