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import torch
import gradio as gr
from shared.utils import files_locator as fl
def get_qwen_text_encoder_filename(text_encoder_quantization):
text_encoder_filename = "Qwen2.5-VL-7B-Instruct/Qwen2.5-VL-7B-Instruct_bf16.safetensors"
if text_encoder_quantization =="int8":
text_encoder_filename = text_encoder_filename.replace("bf16", "quanto_bf16_int8")
return fl.locate_file(text_encoder_filename, True)
class family_handler():
@staticmethod
def query_model_def(base_model_type, model_def):
extra_model_def = {
"image_outputs" : True,
"sample_solvers":[
("Default", "default"),
("Lightning", "lightning")],
"guidance_max_phases" : 1,
"fit_into_canvas_image_refs": 0,
"profiles_dir": ["qwen"],
}
if base_model_type in ["qwen_image_edit_20B", "qwen_image_edit_plus_20B"]:
extra_model_def["inpaint_support"] = True
extra_model_def["image_ref_choices"] = {
"choices": [
("None", ""),
("Conditional Images is first Main Subject / Landscape and may be followed by People / Objects", "KI"),
("Conditional Images are People / Objects", "I"),
],
"letters_filter": "KI",
}
extra_model_def["background_removal_label"]= "Remove Backgrounds only behind People / Objects except main Subject / Landscape"
extra_model_def["video_guide_outpainting"] = [2]
extra_model_def["model_modes"] = {
"choices": [
("Lora Inpainting: Inpainted area completely unrelated to occulted content", 1),
("Masked Denoising : Inpainted area may reuse some content that has been occulted", 0),
],
"default": 1,
"label" : "Inpainting Method",
"image_modes" : [2],
}
if base_model_type in ["qwen_image_edit_plus_20B"]:
extra_model_def["guide_preprocessing"] = {
"selection": ["", "PV", "SV", "CV"],
}
extra_model_def["mask_preprocessing"] = {
"selection": ["", "A"],
"visible": False,
}
return extra_model_def
@staticmethod
def query_supported_types():
return ["qwen_image_20B", "qwen_image_edit_20B", "qwen_image_edit_plus_20B"]
@staticmethod
def query_family_maps():
return {}, {}
@staticmethod
def query_model_family():
return "qwen"
@staticmethod
def query_family_infos():
return {"qwen":(40, "Qwen")}
@staticmethod
def query_model_files(computeList, base_model_type, model_filename, text_encoder_quantization):
text_encoder_filename = get_qwen_text_encoder_filename(text_encoder_quantization)
return {
"repoId" : "DeepBeepMeep/Qwen_image",
"sourceFolderList" : ["", "Qwen2.5-VL-7B-Instruct"],
"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) ]
}
@staticmethod
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, submodel_no_list = None):
from .qwen_main import model_factory
from mmgp import offload
pipe_processor = model_factory(
checkpoint_dir="ckpts",
model_filename=model_filename,
model_type = model_type,
model_def = model_def,
base_model_type=base_model_type,
text_encoder_filename= get_qwen_text_encoder_filename(text_encoder_quantization),
quantizeTransformer = quantizeTransformer,
dtype = dtype,
VAE_dtype = VAE_dtype,
mixed_precision_transformer = mixed_precision_transformer,
save_quantized = save_quantized
)
pipe = {"tokenizer" : pipe_processor.tokenizer, "transformer" : pipe_processor.transformer, "text_encoder" : pipe_processor.text_encoder, "vae" : pipe_processor.vae}
return pipe_processor, pipe
@staticmethod
def fix_settings(base_model_type, settings_version, model_def, ui_defaults):
if ui_defaults.get("sample_solver", "") == "":
ui_defaults["sample_solver"] = "default"
if settings_version < 2.32:
ui_defaults["denoising_strength"] = 1.
@staticmethod
def update_default_settings(base_model_type, model_def, ui_defaults):
ui_defaults.update({
"guidance_scale": 4,
"sample_solver": "default",
})
if base_model_type in ["qwen_image_edit_20B"]:
ui_defaults.update({
"video_prompt_type": "KI",
"denoising_strength" : 1.,
"model_mode" : 0,
})
elif base_model_type in ["qwen_image_edit_plus_20B"]:
ui_defaults.update({
"video_prompt_type": "I",
"denoising_strength" : 1.,
"model_mode" : 0,
})
@staticmethod
def validate_generative_settings(base_model_type, model_def, inputs):
if base_model_type in ["qwen_image_edit_20B", "qwen_image_edit_plus_20B"]:
model_mode = inputs["model_mode"]
denoising_strength= inputs["denoising_strength"]
video_guide_outpainting= inputs["video_guide_outpainting"]
from wgp import get_outpainting_dims
outpainting_dims = get_outpainting_dims(video_guide_outpainting)
if denoising_strength < 1 and model_mode == 1:
gr.Info("Denoising Strength will be ignored while using Lora Inpainting")
if outpainting_dims is not None and model_mode == 0 :
return "Outpainting is not supported with Masked Denoising "
@staticmethod
def get_rgb_factors(base_model_type ):
from shared.RGB_factors import get_rgb_factors
latent_rgb_factors, latent_rgb_factors_bias = get_rgb_factors("qwen")
return latent_rgb_factors, latent_rgb_factors_bias
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