V 1.0.5 - SANA 2
This commit is contained in:
DESKTOP-CNFQ7PM\Primere
+185
-102
@@ -45,6 +45,10 @@ from ComfyUI_ExtraModels.utils.dtype import string_to_dtype
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from ComfyUI_ExtraModels.HunYuanDiT.tenc import load_clip, load_t5
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from ComfyUI_ExtraModels.PixArt.conf import pixart_conf
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from ComfyUI_ExtraModels.PixArt.loader import load_pixart
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from ComfyUI_ExtraModels.Sana.conf import sana_conf, sana_res
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from ComfyUI_ExtraModels.Sana.loader import load_sana
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from ComfyUI_ExtraModels.VAE.conf import vae_conf
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from ComfyUI_ExtraModels.VAE.loader import EXVAE
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import numpy as np
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import difflib
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import datetime
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@@ -288,11 +292,11 @@ class PrimereModelConceptSelector:
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"pixart_refiner_denoise": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.01}),
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"pixart_refiner_ignore_prompt": ("BOOLEAN", {"default": False, "label_on": "Send prompt to refiner", "label_off": "Ignore prompt"}),
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"sana_model": (["None"] + DIFFUSIONLIST,),
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"sana_model": (["None"] + DIFFUSIONLIST + MODELLIST,),
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"sana_encoder": (["None"] + TEXT_ENCODERS_PATHS,),
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"sana_vae": (["None"] + VAELIST,),
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"sana_weight_dtype": (["Auto", "fp16", "bf16", "fp32"], {"default": "fp16"}),
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"sana_precision": (['fp16', 'quant8', 'quant4'], {"default": "fp16"}),
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"sana_precision": (['fp32', 'fp16', 'quant8', 'quant4'], {"default": "fp16"}),
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},
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"optional": SAMPLER_INPUTS
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}
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@@ -521,6 +525,11 @@ class PrimereConceptDataTuple:
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def INPUT_TYPES(cls):
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return {
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"required": {
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"forceInput": True}),
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"scheduler_name": (comfy.samplers.KSampler.SCHEDULERS, {"forceInput": True}),
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"steps": ("INT", {"forceInput": True}),
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"cfg": ("FLOAT", {"forceInput": True}),
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"override_steps": ("OVERRIDE_STEPS", {"default": False, "forceInput": True}),
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"clip_selection": ("CLIP_SELECTION", {"default": True, "forceInput": True}),
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"vae_selection": ("VAE_SELECTION", {"default": True, "forceInput": True}),
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@@ -819,88 +828,110 @@ class PrimereCKPTLoader:
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is_link = os.path.islink(str(fullpathFile))
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if is_link == True:
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fullpathFile = Path(str(fullpathFile)).resolve()
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dtype = utility.get_dtype_by_name(sana_weight_dtype)
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dtype = utility.get_dtype_by_name(sana_weight_dtype)
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text_encoder_dir = os.path.join(folder_paths.models_dir, 'text_encoders', encoder_path)
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if os.path.exists(text_encoder_dir) == False:
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LLM_PRIMERE_ROOT = os.path.join(PRIMERE_ROOT, 'Nodes', 'Downloads', 'LLM')
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text_encoder_dir = os.path.join(LLM_PRIMERE_ROOT, encoder_path)
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vae_path = folder_paths.get_full_path("vae", sana_vae)
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cfg = create_dc_ae_model_cfg('dc-ae-f32c32-sana-1.0')
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vae = DCAE(cfg)
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state_dict = comfy.utils.load_torch_file(vae_path, safe_load=True)
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vae.load_state_dict(state_dict, strict=False)
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vae_dtype = model_management.vae_dtype(device, [torch.float16, torch.bfloat16, torch.float32])
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vae.to(vae_dtype).eval()
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text_encoder_dtype = model_management.text_encoder_dtype(device)
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if "T5" in encoder_path:
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tokenizer = T5Tokenizer.from_pretrained(str(text_encoder_dir))
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llm_model = None
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text_encoder = T5EncoderModel.from_pretrained(str(text_encoder_dir), torch_dtype=text_encoder_dtype)
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if concept_data['scheduler_name'] == 'flow_dpm-solver':
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cfg = create_dc_ae_model_cfg('dc-ae-f32c32-sana-1.0')
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vae = DCAE(cfg)
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state_dict = comfy.utils.load_torch_file(vae_path, safe_load=True)
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vae.load_state_dict(state_dict, strict=False)
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vae_dtype = model_management.vae_dtype(device, [torch.float16, torch.bfloat16, torch.float32])
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vae.to(vae_dtype).eval()
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if "T5" in encoder_path:
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tokenizer = T5Tokenizer.from_pretrained(str(text_encoder_dir))
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llm_model = None
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text_encoder = T5EncoderModel.from_pretrained(str(text_encoder_dir), torch_dtype=text_encoder_dtype)
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else:
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tokenizer = AutoTokenizer.from_pretrained(text_encoder_dir)
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quantization_config = BitsAndBytesConfig(load_in_8bit=True) if precision == '8-bit' else BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=text_encoder_dtype) if precision == '4-bit' else None
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llm_model = AutoModelForCausalLM.from_pretrained(text_encoder_dir, quantization_config=quantization_config, torch_dtype=text_encoder_dtype) if '-4bit' not in encoder_path else AutoModelForCausalLM.from_pretrained(text_encoder_dir, torch_dtype=text_encoder_dtype)
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tokenizer.padding_side = "right"
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text_encoder = llm_model.get_decoder()
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text_encoder.to(device)
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state_dict = comfy.utils.load_torch_file(str(fullpathFile), safe_load=True)
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is_1600M = state_dict['final_layer.scale_shift_table'].shape[1] == 2240 # 1.6b: 2240 0.6b: 1152
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if '512px' in ckpt_name:
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config_path = os.path.join(PRIMERE_ROOT, 'components', 'sana', 'configs', 'sana_config', '512ms', 'Sana_1600M_img512.yaml') if is_1600M else os.path.join(PRIMERE_ROOT, 'components', 'sana', 'configs', 'sana_config', '512ms', 'Sana_600M_img512.yaml')
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else:
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config_path = os.path.join(PRIMERE_ROOT, 'components', 'sana', 'configs', 'sana_config', '1024ms', 'Sana_1600M_img1024_AdamW.yaml') if is_1600M else os.path.join(PRIMERE_ROOT, 'components', 'sana', 'configs', 'sana_config', '1024ms', 'Sana_600M_img1024.yaml')
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config = pyrallis.load(SanaConfig, open(config_path))
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pred_sigma = getattr(config.scheduler, "pred_sigma", True)
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learn_sigma = getattr(config.scheduler, "learn_sigma", True) and pred_sigma
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image_size = config.model.image_size
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latent_size = image_size // config.vae.vae_downsample_rate
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model_kwargs = {
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"input_size": latent_size,
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"pe_interpolation": config.model.pe_interpolation,
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"config": config,
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"model_max_length": config.text_encoder.model_max_length,
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"qk_norm": config.model.qk_norm,
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"micro_condition": config.model.micro_condition,
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"caption_channels": text_encoder.config.hidden_size, # Gemma2: 2304
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"y_norm": config.text_encoder.y_norm,
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"attn_type": config.model.attn_type,
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"ffn_type": config.model.ffn_type,
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"mlp_ratio": config.model.mlp_ratio,
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"mlp_acts": list(config.model.mlp_acts),
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"in_channels": config.vae.vae_latent_dim,
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"y_norm_scale_factor": config.text_encoder.y_norm_scale_factor,
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"use_pe": config.model.use_pe,
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"pred_sigma": pred_sigma,
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"learn_sigma": learn_sigma,
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"use_fp32_attention": config.model.get("fp32_attention", False) and config.model.mixed_precision != "bf16",
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}
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unet = build_model(config.model.model, **model_kwargs)
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unet.to(dtype)
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state_dict = state_dict.get("state_dict", state_dict)
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if "pos_embed" in state_dict:
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del state_dict["pos_embed"]
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missing, unexpected = unet.load_state_dict(state_dict, strict=False)
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del state_dict
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unet.eval().to(dtype)
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pipe = SanaPipeline(config, vae, dtype, unet)
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SANA_MODEL = {
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'pipe': pipe,
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'unet': unet,
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'text_encoder_model': llm_model,
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'tokenizer': tokenizer,
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'text_encoder': text_encoder,
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'vae': vae,
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'device': device
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}
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SANA_VAE = sana_utils.first_stage_model(vae)
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SANA_CLIP = sana_utils.cond_stage_model(tokenizer, text_encoder)
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else:
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model = list(sana_conf.keys())
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model_conf = sana_conf[model[1]]
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SANA_MODEL = load_sana(model_path = str(fullpathFile), model_conf = model_conf)
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vae_config = vae_conf['dcae-f32c32-sana-1.0']
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SANA_VAE = EXVAE(vae_path, vae_config, string_to_dtype(sana_weight_dtype.upper(), "vae"))
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tokenizer = AutoTokenizer.from_pretrained(text_encoder_dir)
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quantization_config = BitsAndBytesConfig(load_in_8bit=True) if precision == '8-bit' else BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=text_encoder_dtype) if precision == '4-bit' else None
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llm_model = AutoModelForCausalLM.from_pretrained(text_encoder_dir, quantization_config=quantization_config, torch_dtype=text_encoder_dtype) if '-4bit' not in encoder_path else AutoModelForCausalLM.from_pretrained(text_encoder_dir, torch_dtype=text_encoder_dtype)
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text_encoder_model = AutoModelForCausalLM.from_pretrained(text_encoder_dir, torch_dtype = text_encoder_dtype)
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tokenizer.padding_side = "right"
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text_encoder = llm_model.get_decoder()
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text_encoder = text_encoder_model.get_decoder()
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if device != "cpu":
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text_encoder = text_encoder.to(device)
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text_encoder.to(device)
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state_dict = comfy.utils.load_torch_file(str(fullpathFile), safe_load=True)
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is_1600M = state_dict['final_layer.scale_shift_table'].shape[1] == 2240 # 1.6b: 2240 0.6b: 1152
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if '512px' in ckpt_name:
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config_path = os.path.join(PRIMERE_ROOT, 'components', 'sana', 'configs', 'sana_config', '512ms', 'Sana_1600M_img512.yaml') if is_1600M else os.path.join(PRIMERE_ROOT, 'components', 'sana', 'configs', 'sana_config', '512ms', 'Sana_600M_img512.yaml')
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else:
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config_path = os.path.join(PRIMERE_ROOT, 'components', 'sana', 'configs', 'sana_config', '1024ms', 'Sana_1600M_img1024_AdamW.yaml') if is_1600M else os.path.join(PRIMERE_ROOT, 'components', 'sana', 'configs', 'sana_config', '1024ms', 'Sana_600M_img1024.yaml')
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config = pyrallis.load(SanaConfig, open(config_path))
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pred_sigma = getattr(config.scheduler, "pred_sigma", True)
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learn_sigma = getattr(config.scheduler, "learn_sigma", True) and pred_sigma
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image_size = config.model.image_size
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latent_size = image_size // config.vae.vae_downsample_rate
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model_kwargs = {
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"input_size": latent_size,
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"pe_interpolation": config.model.pe_interpolation,
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"config": config,
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"model_max_length": config.text_encoder.model_max_length,
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"qk_norm": config.model.qk_norm,
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"micro_condition": config.model.micro_condition,
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"caption_channels": text_encoder.config.hidden_size, # Gemma2: 2304
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"y_norm": config.text_encoder.y_norm,
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"attn_type": config.model.attn_type,
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"ffn_type": config.model.ffn_type,
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"mlp_ratio": config.model.mlp_ratio,
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"mlp_acts": list(config.model.mlp_acts),
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"in_channels": config.vae.vae_latent_dim,
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"y_norm_scale_factor": config.text_encoder.y_norm_scale_factor,
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"use_pe": config.model.use_pe,
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"pred_sigma": pred_sigma,
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"learn_sigma": learn_sigma,
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"use_fp32_attention": config.model.get("fp32_attention", False) and config.model.mixed_precision != "bf16",
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}
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unet = build_model(config.model.model, **model_kwargs)
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unet.to(dtype)
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state_dict = state_dict.get("state_dict", state_dict)
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if "pos_embed" in state_dict:
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del state_dict["pos_embed"]
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missing, unexpected = unet.load_state_dict(state_dict, strict=False)
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del state_dict
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unet.eval().to(dtype)
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pipe = SanaPipeline(config, vae, dtype, unet)
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SANA_MODEL = {
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'pipe': pipe,
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'unet': unet,
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'text_encoder_model': llm_model,
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'tokenizer': tokenizer,
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'text_encoder': text_encoder,
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'vae': vae,
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'device': device
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}
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SANA_VAE = sana_utils.first_stage_model(vae)
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SANA_CLIP = sana_utils.cond_stage_model(tokenizer, text_encoder)
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SANA_CLIP = {
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"tokenizer": tokenizer,
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"text_encoder": text_encoder,
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"text_encoder_model": text_encoder_model,
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}
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return (SANA_MODEL,) + (SANA_CLIP,) + (SANA_VAE,) + (MODEL_VERSION,)
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@@ -1373,8 +1404,15 @@ class PrimereCKPTLoader:
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force_lora_weighth = re.findall(r"(?i)(\d+)LoWe", ckpt_name)
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if len(force_lora_weighth) > 0:
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strength_lightning_lora_model = int(force_lora_weighth[0])
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strength_hypersd_lora_model = int(force_lora_weighth[0])
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if len(str(force_lora_weighth[0])) > 1:
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FirstWCH = str(force_lora_weighth[0])[:1]
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OtherWCH = str(force_lora_weighth[0])[1:]
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strength_lora_float = float(FirstWCH + '.' + OtherWCH)
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strength_lightning_lora_model = strength_lora_float
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strength_hypersd_lora_model = strength_lora_float
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else:
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strength_lightning_lora_model = int(force_lora_weighth[0])
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strength_hypersd_lora_model = int(force_lora_weighth[0])
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if lightningModeValid == True and loaded_model is None:
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OUTPUT_MODEL = utility.BDanceConceptHelper(self, model_concept, lightningModeValid, lightning_selector, lightning_model_step, OUTPUT_MODEL, lora_name, unet_name, ckpt_name, strength_lightning_lora_model)
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@@ -1949,42 +1987,87 @@ class PrimereCLIP:
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if 'SANA' in model_concept:
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device = model_management.get_torch_device()
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base_ratios = eval(f"ASPECT_RATIO_{1024}_TEST")
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clip.text_encoder.to(device)
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null_caption_token = clip.tokenizer(
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negative_text,
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max_length=300,
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padding="max_length",
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truncation=True,
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return_tensors="pt",
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).to(device)
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null_caption_embs = clip.text_encoder(null_caption_token.input_ids, null_caption_token.attention_mask)[0]
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preset_te_prompt = ['Create one detailed perfect prompt for given topic for stable diffusion text-to-image text2image DiT models.', 'Please generate only the one enhanced description for the prompt below and avoid including any additional commentary or evaluations:', 'User Prompt: ']
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if 'scheduler_name' in workflow_tuple:
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sana_scheduler_name = workflow_tuple['scheduler_name']
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else:
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sana_scheduler_name = 'flow_dpm-solver'
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prompts = []
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with torch.no_grad():
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prompts.append(prepare_prompt_ar(positive_text, base_ratios, device=device, show=False)[0].strip())
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chi_prompt = "\n".join(preset_te_prompt)
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prompts_all = [chi_prompt + positive_text]
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num_chi_prompt_tokens = len(clip.tokenizer.encode(chi_prompt))
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max_length_all = (num_chi_prompt_tokens + 300 - 2) # magic number 2: [bos], [_]
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caption_token = clip.tokenizer(
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prompts_all,
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max_length=max_length_all,
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preset_te_prompt = ['Create one detailed perfect prompt from given User Prompt for stable diffusion text-to-image text2image modern DiT models.', 'Generate only the one enhanced description for the prompt below, avoid including any additional questions comments or evaluations:', 'User Prompt: ']
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chi_prompt = "\n".join(preset_te_prompt)
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if sana_scheduler_name == 'flow_dpm-solver':
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base_ratios = eval(f"ASPECT_RATIO_{1024}_TEST")
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clip.text_encoder.to(device)
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null_caption_token = clip.tokenizer(
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negative_text,
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max_length=300,
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padding="max_length",
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truncation=True,
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return_tensors="pt",
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).to(device)
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select_index = [0] + list(range(-300 + 1, 0))
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caption_embs = clip.text_encoder(caption_token.input_ids, caption_token.attention_mask)[0][:, None][:, :, select_index]
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emb_masks = caption_token.attention_mask[:, select_index]
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null_y = null_caption_embs.repeat(len(prompts), 1, 1)[:, None]
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null_caption_embs = clip.text_encoder(null_caption_token.input_ids, null_caption_token.attention_mask)[0]
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prompts = []
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with torch.no_grad():
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prompts.append(prepare_prompt_ar(positive_text, base_ratios, device=device, show=False)[0].strip())
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prompts_all = [chi_prompt + positive_text]
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num_chi_prompt_tokens = len(clip.tokenizer.encode(chi_prompt))
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max_length_all = (num_chi_prompt_tokens + 300 - 2) # magic number 2: [bos], [_]
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caption_token = clip.tokenizer(
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prompts_all,
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max_length=max_length_all,
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padding="max_length",
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truncation=True,
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return_tensors="pt",
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).to(device)
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select_index = [0] + list(range(-300 + 1, 0))
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caption_embs = clip.text_encoder(caption_token.input_ids, caption_token.attention_mask)[0][:, None][:, :, select_index]
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emb_masks = caption_token.attention_mask[:, select_index]
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null_y = null_caption_embs.repeat(len(prompts), 1, 1)[:, None]
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clip.text_encoder.to(model_management.text_encoder_offload_device())
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comfy.model_management.soft_empty_cache(True)
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clip.text_encoder.to(model_management.text_encoder_offload_device())
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comfy.model_management.soft_empty_cache(True)
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return ([[caption_embs, {"emb_masks": emb_masks}]], [[null_y, {}]], positive_text, negative_text, t5xxl_prompt, "", "", workflow_tuple)
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return ([[caption_embs, {"emb_masks": emb_masks}]], [[null_y, {}]], positive_text, negative_text, t5xxl_prompt, "", "", workflow_tuple)
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else:
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tokenizer = clip["tokenizer"]
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text_encoder = clip["text_encoder"]
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with torch.no_grad():
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full_prompt = chi_prompt + positive_text
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num_chi_tokens = len(tokenizer.encode(chi_prompt))
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max_length = num_chi_tokens + 300 - 2
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tokens = tokenizer(
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[full_prompt],
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max_length=max_length,
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padding="max_length",
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truncation=True,
|
||||
return_tensors="pt"
|
||||
).to(text_encoder.device)
|
||||
select_idx = [0] + list(range(-300 + 1, 0))
|
||||
embs_plus = text_encoder(tokens.input_ids, tokens.attention_mask)[0][:, None][:, :, select_idx]
|
||||
emb_plus_masks = tokens.attention_mask[:, select_idx]
|
||||
sana_embs_pos = embs_plus * emb_plus_masks.unsqueeze(-1)
|
||||
|
||||
with torch.no_grad():
|
||||
full_prompt = chi_prompt + negative_text
|
||||
num_chi_tokens = len(tokenizer.encode(chi_prompt))
|
||||
max_length = num_chi_tokens + 300 - 2
|
||||
|
||||
tokens = tokenizer(
|
||||
[full_prompt],
|
||||
max_length=max_length,
|
||||
padding="max_length",
|
||||
truncation=True,
|
||||
return_tensors="pt"
|
||||
).to(text_encoder.device)
|
||||
select_idx = [0] + list(range(-300 + 1, 0))
|
||||
embs_minus = text_encoder(tokens.input_ids, tokens.attention_mask)[0][:, None][:, :, select_idx]
|
||||
emb_minus_masks = tokens.attention_mask[:, select_idx]
|
||||
sana_embs_neg = embs_minus * emb_minus_masks.unsqueeze(-1)
|
||||
|
||||
return ([[sana_embs_pos, {}]], [[sana_embs_neg, {}]], positive_text, negative_text, t5xxl_prompt, "", "", workflow_tuple)
|
||||
|
||||
if model_concept == 'PixartSigma':
|
||||
cond_pos_ref = None
|
||||
|
||||
+30
-12
@@ -639,15 +639,35 @@ class PrimereKSampler:
|
||||
|
||||
match model_concept:
|
||||
case 'SANA1024' | 'SANA512':
|
||||
device = model['device']
|
||||
samples_out = primeresamplers.PSamplerSana(self, device, seed, model,
|
||||
steps, cfg, sampler_name, scheduler_name,
|
||||
positive, negative,
|
||||
latent_image, denoise,
|
||||
variation_extender, variation_batch_step_original,
|
||||
batch_counter, variation_extender_original,
|
||||
variation_batch_step, variation_level, variation_limit,
|
||||
align_your_steps, noise_extender_ksampler, WORKFLOWDATA, prompt)[0]
|
||||
if scheduler_name == 'flow_dpm-solver':
|
||||
device = model['device']
|
||||
samples_out = primeresamplers.PSamplerSana(self, device, seed, model,
|
||||
steps, cfg, sampler_name, scheduler_name,
|
||||
positive, negative,
|
||||
latent_image, denoise,
|
||||
variation_extender, variation_batch_step_original,
|
||||
batch_counter, variation_extender_original,
|
||||
variation_batch_step, variation_level, variation_limit,
|
||||
align_your_steps, noise_extender_ksampler, WORKFLOWDATA, prompt)[0]
|
||||
else:
|
||||
if device == 'DEFAULT':
|
||||
device = model_management.get_torch_device()
|
||||
latentWidth, latentHeigth = utility.getLatentSize(latent_image)
|
||||
|
||||
latent = torch.zeros([1, 32, (latentHeigth * 8) // 32, (latentWidth * 8) // 32], device=device)
|
||||
latent_image = {"samples": latent}
|
||||
samples_out = primeresamplers.PKSampler(self, device, seed, model,
|
||||
steps, cfg, sampler_name, scheduler_name,
|
||||
positive, negative,
|
||||
latent_image, denoise,
|
||||
variation_extender, variation_batch_step_original, batch_counter, variation_extender_original, variation_batch_step, variation_level, variation_limit,
|
||||
align_your_steps, noise_extender_ksampler)[0]
|
||||
|
||||
try:
|
||||
comfy.model_management.soft_empty_cache()
|
||||
comfy.model_management.cleanup_models(True)
|
||||
except Exception:
|
||||
print('No need to clear cache...')
|
||||
|
||||
case "PixartSigma":
|
||||
samples_out = primeresamplers.PSamplerPixart(self, device, seed, model,
|
||||
@@ -729,9 +749,7 @@ class PrimereKSampler:
|
||||
steps, cfg, sampler_name, scheduler_name,
|
||||
positive, negative,
|
||||
latent_image, denoise,
|
||||
variation_extender, variation_batch_step_original,
|
||||
batch_counter, variation_extender_original,
|
||||
variation_batch_step, variation_level, variation_limit,
|
||||
variation_extender, variation_batch_step_original, batch_counter, variation_extender_original, variation_batch_step, variation_level, variation_limit,
|
||||
align_your_steps, noise_extender_ksampler)[0]
|
||||
|
||||
case _:
|
||||
|
||||
@@ -131,16 +131,16 @@ def PSamplerSana(self, device, seed, model,
|
||||
pag_applied_layers = None if 'pag_applied_layers' not in model.keys() else model['pag_applied_layers']
|
||||
|
||||
latent_out = model['pipe'](
|
||||
cond=positive,
|
||||
uncond=negative,
|
||||
guidance_scale=cfg,
|
||||
pag_guidance_scale=2,
|
||||
num_inference_steps=(steps + 1),
|
||||
generator=torch.Generator(device=device).manual_seed(seed),
|
||||
latents=latent_image['samples'],
|
||||
noise_scheduler=scheduler_name,
|
||||
output_type=True,
|
||||
pag_applied_layers=pag_applied_layers,
|
||||
cond = positive,
|
||||
uncond = negative,
|
||||
guidance_scale = cfg,
|
||||
pag_guidance_scale = 2,
|
||||
num_inference_steps = (steps + 1),
|
||||
generator = torch.Generator(device=device).manual_seed(seed),
|
||||
latents = latent_image['samples'],
|
||||
noise_scheduler = scheduler_name,
|
||||
output_type = True,
|
||||
pag_applied_layers = pag_applied_layers,
|
||||
)
|
||||
|
||||
model['unet'].to(comfy.model_management.unet_offload_device())
|
||||
|
||||
@@ -60,9 +60,9 @@ PREVIEW_PATH_BY_TYPE = {
|
||||
}
|
||||
|
||||
WORKFLOW_SORT_LIST = ['exif_status', 'exif_data_count', 'meta_source', 'pic2story', 'positive', 'positive_l', 'positive_r', 'negative', 'negative_l', 'negative_r', 'prompt_state', 'decoded_positive', 'decoded_negative', 'pic2story_positive',
|
||||
'model', 'model_concept', 'concept_data', 'model_version', 'is_sdxl', 'model_hash', 'vae', 'vae_hash', 'vae_name_sd', 'vae_name_sdxl', 'sampler', 'scheduler', 'steps',
|
||||
'model', 'model_concept', 'model_version', 'is_sdxl', 'model_hash', 'vae', 'vae_hash', 'vae_name_sd', 'vae_name_sdxl', 'sampler', 'scheduler', 'steps',
|
||||
'cfg', 'seed', 'width', 'height', 'size_string', 'preferred', 'saved_image_width', 'saved_image_heigth', 'upscaler_ratio',
|
||||
'vae_name_sd', 'vae_name_sdxl']
|
||||
'vae_name_sd', 'vae_name_sdxl', 'concept_data']
|
||||
|
||||
|
||||
def merge_str_to_tuple(item1, item2):
|
||||
|
||||
Reference in New Issue
Block a user