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from mmgp import offload
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import torch, json, os
import math
from diffusers.image_processor import VaeImageProcessor
from .transformer_qwenimage import QwenImageTransformer2DModel
from diffusers.utils import logging, replace_example_docstring
from diffusers.utils.torch_utils import randn_tensor
from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, AutoTokenizer, Qwen2VLProcessor
from .autoencoder_kl_qwenimage import AutoencoderKLQwenImage
from diffusers import FlowMatchEulerDiscreteScheduler
from .pipeline_qwenimage import QwenImagePipeline
from PIL import Image
from shared.utils.utils import calculate_new_dimensions, convert_tensor_to_image
from shared.utils import files_locator as fl
def stitch_images(img1, img2):
# Resize img2 to match img1's height
width1, height1 = img1.size
width2, height2 = img2.size
new_width2 = int(width2 * height1 / height2)
img2_resized = img2.resize((new_width2, height1), Image.Resampling.LANCZOS)
stitched = Image.new('RGB', (width1 + new_width2, height1))
stitched.paste(img1, (0, 0))
stitched.paste(img2_resized, (width1, 0))
return stitched
class model_factory():
def __init__(
self,
checkpoint_dir,
model_filename = None,
model_type = None,
model_def = None,
base_model_type = None,
text_encoder_filename = None,
quantizeTransformer = False,
save_quantized = False,
dtype = torch.bfloat16,
VAE_dtype = torch.float32,
mixed_precision_transformer = False,
):
transformer_filename = model_filename[0]
processor = None
tokenizer = None
if base_model_type in ["qwen_image_edit_20B", "qwen_image_edit_plus_20B"]:
processor = Qwen2VLProcessor.from_pretrained(os.path.join(checkpoint_dir,"Qwen2.5-VL-7B-Instruct"))
tokenizer = AutoTokenizer.from_pretrained(os.path.join(checkpoint_dir,"Qwen2.5-VL-7B-Instruct"))
self.base_model_type = base_model_type
base_config_file = "models/qwen/configs/qwen_image_20B.json"
with open(base_config_file, 'r', encoding='utf-8') as f:
transformer_config = json.load(f)
transformer_config.pop("_diffusers_version")
transformer_config.pop("_class_name")
transformer_config.pop("pooled_projection_dim")
from accelerate import init_empty_weights
with init_empty_weights():
transformer = QwenImageTransformer2DModel(**transformer_config)
source = model_def.get("source", None)
if source is not None:
offload.load_model_data(transformer, source)
else:
offload.load_model_data(transformer, transformer_filename)
# transformer = offload.fast_load_transformers_model("transformer_quanto.safetensors", writable_tensors= True , modelClass=QwenImageTransformer2DModel, defaultConfigPath="transformer_config.json")
if not source is None:
from wgp import save_model
save_model(transformer, model_type, dtype, None)
if save_quantized:
from wgp import save_quantized_model
save_quantized_model(transformer, model_type, model_filename[0], dtype, base_config_file)
text_encoder = offload.fast_load_transformers_model(text_encoder_filename, writable_tensors= True , modelClass=Qwen2_5_VLForConditionalGeneration, defaultConfigPath= os.path.join(checkpoint_dir, "Qwen2.5-VL-7B-Instruct", "config.json"))
# text_encoder = offload.fast_load_transformers_model(text_encoder_filename, do_quantize=True, writable_tensors= True , modelClass=Qwen2_5_VLForConditionalGeneration, defaultConfigPath="text_encoder_config.json", verboseLevel=2)
# text_encoder.to(torch.float16)
# offload.save_model(text_encoder, "text_encoder_quanto_fp16.safetensors", do_quantize= True)
vae = offload.fast_load_transformers_model( os.path.join(checkpoint_dir,"qwen_vae.safetensors"), writable_tensors= True , modelClass=AutoencoderKLQwenImage, defaultConfigPath=os.path.join(checkpoint_dir,"qwen_vae_config.json"))
self.pipeline = QwenImagePipeline(vae, text_encoder, tokenizer, transformer, processor)
self.vae=vae
self.text_encoder=text_encoder
self.tokenizer=tokenizer
self.transformer=transformer
self.processor = processor
def generate(
self,
seed: int | None = None,
input_prompt: str = "replace the logo with the text 'Black Forest Labs'",
n_prompt = None,
sampling_steps: int = 20,
input_ref_images = None,
input_frames= None,
input_masks= None,
width= 832,
height=480,
guide_scale: float = 4,
fit_into_canvas = None,
callback = None,
loras_slists = None,
batch_size = 1,
video_prompt_type = "",
VAE_tile_size = None,
joint_pass = True,
sample_solver='default',
denoising_strength = 1.,
model_mode = 0,
outpainting_dims = None,
**bbargs
):
# Generate with different aspect ratios
aspect_ratios = {
"1:1": (1328, 1328),
"16:9": (1664, 928),
"9:16": (928, 1664),
"4:3": (1472, 1140),
"3:4": (1140, 1472)
}
if sample_solver =='lightning':
scheduler_config = {
"base_image_seq_len": 256,
"base_shift": math.log(3), # We use shift=3 in distillation
"invert_sigmas": False,
"max_image_seq_len": 8192,
"max_shift": math.log(3), # We use shift=3 in distillation
"num_train_timesteps": 1000,
"shift": 1.0,
"shift_terminal": None, # set shift_terminal to None
"stochastic_sampling": False,
"time_shift_type": "exponential",
"use_beta_sigmas": False,
"use_dynamic_shifting": True,
"use_exponential_sigmas": False,
"use_karras_sigmas": False,
}
else:
scheduler_config = {
"base_image_seq_len": 256,
"base_shift": 0.5,
"invert_sigmas": False,
"max_image_seq_len": 8192,
"max_shift": 0.9,
"num_train_timesteps": 1000,
"shift": 1.0,
"shift_terminal": 0.02,
"stochastic_sampling": False,
"time_shift_type": "exponential",
"use_beta_sigmas": False,
"use_dynamic_shifting": True,
"use_exponential_sigmas": False,
"use_karras_sigmas": False
}
self.scheduler=FlowMatchEulerDiscreteScheduler(**scheduler_config)
self.pipeline.scheduler = self.scheduler
if VAE_tile_size is not None:
self.vae.use_tiling = VAE_tile_size[0]
self.vae.tile_latent_min_height = VAE_tile_size[1]
self.vae.tile_latent_min_width = VAE_tile_size[1]
qwen_edit_plus = self.base_model_type in ["qwen_image_edit_plus_20B"]
self.vae.enable_slicing()
# width, height = aspect_ratios["16:9"]
if n_prompt is None or len(n_prompt) == 0:
n_prompt= "text, watermark, copyright, blurry, low resolution"
image_mask = None if input_masks is None else convert_tensor_to_image(input_masks, mask_levels= True)
if input_frames is not None:
input_ref_images = [convert_tensor_to_image(input_frames) ] + ([] if input_ref_images is None else input_ref_images )
if input_ref_images is not None:
# image stiching method
stiched = input_ref_images[0]
if "K" in video_prompt_type :
w, h = input_ref_images[0].size
height, width = calculate_new_dimensions(height, width, h, w, fit_into_canvas)
if not qwen_edit_plus:
for new_img in input_ref_images[1:]:
stiched = stitch_images(stiched, new_img)
input_ref_images = [stiched]
image = self.pipeline(
prompt=input_prompt,
negative_prompt=n_prompt,
image = input_ref_images,
image_mask = image_mask,
width=width,
height=height,
num_inference_steps=sampling_steps,
num_images_per_prompt = batch_size,
true_cfg_scale=guide_scale,
callback = callback,
pipeline=self,
loras_slists=loras_slists,
joint_pass = joint_pass,
denoising_strength=denoising_strength,
generator=torch.Generator(device="cuda").manual_seed(seed),
lora_inpaint = image_mask is not None and model_mode == 1,
outpainting_dims = outpainting_dims,
qwen_edit_plus = qwen_edit_plus,
)
if image is None: return None
return image.transpose(0, 1)
def get_loras_transformer(self, get_model_recursive_prop, model_type, model_mode, **kwargs):
if model_mode == 0: return [], []
preloadURLs = get_model_recursive_prop(model_type, "preload_URLs")
if len(preloadURLs) == 0: return [], []
return [ fl.locate_file(os.path.basename(preloadURLs[0]))] , [1]
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