mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
import torch
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def get_qwen_text_encoder_filename(text_encoder_quantization):
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text_encoder_filename = "ckpts/Qwen2.5-VL-7B-Instruct/Qwen2.5-VL-7B-Instruct_bf16.safetensors"
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if text_encoder_quantization =="int8":
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text_encoder_filename = text_encoder_filename.replace("bf16", "quanto_bf16_int8")
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return text_encoder_filename
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class family_handler():
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@staticmethod
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def query_model_def(base_model_type, model_def):
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model_def_output = {
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"image_outputs" : True,
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"sample_solvers":[
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("Default", "default"),
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("Lightning", "lightning")],
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"guidance_max_phases" : 1,
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"lock_image_refs_ratios": True,
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}
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return model_def_output
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@staticmethod
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def query_supported_types():
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return ["qwen_image_20B", "qwen_image_edit_20B"]
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@staticmethod
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def query_family_maps():
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return {}, {}
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@staticmethod
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def query_model_family():
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return "qwen"
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@staticmethod
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def query_family_infos():
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return {"qwen":(40, "Qwen")}
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@staticmethod
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def query_model_files(computeList, base_model_type, model_filename, text_encoder_quantization):
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text_encoder_filename = get_qwen_text_encoder_filename(text_encoder_quantization)
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return {
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"repoId" : "DeepBeepMeep/Qwen_image",
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"sourceFolderList" : ["", "Qwen2.5-VL-7B-Instruct"],
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"fileList" : [ ["qwen_vae.safetensors", "qwen_vae_config.json"], ["merges.txt", "tokenizer_config.json", "config.json", "vocab.json", "video_preprocessor_config.json", "preprocessor_config.json"] + computeList(text_encoder_filename) ]
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}
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@staticmethod
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def load_model(model_filename, model_type, base_model_type, model_def, quantizeTransformer = False, text_encoder_quantization = None, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False, save_quantized = False):
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from .qwen_main import model_factory
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from mmgp import offload
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pipe_processor = model_factory(
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checkpoint_dir="ckpts",
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model_filename=model_filename,
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model_type = model_type,
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model_def = model_def,
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base_model_type=base_model_type,
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text_encoder_filename= get_qwen_text_encoder_filename(text_encoder_quantization),
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quantizeTransformer = quantizeTransformer,
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dtype = dtype,
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VAE_dtype = VAE_dtype,
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mixed_precision_transformer = mixed_precision_transformer,
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save_quantized = save_quantized
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)
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pipe = {"tokenizer" : pipe_processor.tokenizer, "transformer" : pipe_processor.transformer, "text_encoder" : pipe_processor.text_encoder, "vae" : pipe_processor.vae}
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return pipe_processor, pipe
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@staticmethod
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def fix_settings(base_model_type, settings_version, model_def, ui_defaults):
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if ui_defaults.get("sample_solver", "") == "":
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ui_defaults["sample_solver"] = "default"
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@staticmethod
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def update_default_settings(base_model_type, model_def, ui_defaults):
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ui_defaults.update({
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"guidance_scale": 4,
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"sample_solver": "default",
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})
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if model_def.get("reference_image", False):
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ui_defaults.update({
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"video_prompt_type": "KI",
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})
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