mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
273 lines
12 KiB
Python
273 lines
12 KiB
Python
import os
|
|
import re
|
|
import time
|
|
from dataclasses import dataclass
|
|
from glob import iglob
|
|
from mmgp import offload as offload
|
|
import torch
|
|
from shared.utils.utils import calculate_new_dimensions
|
|
from .sampling import denoise, get_schedule, prepare_kontext, prepare_prompt, prepare_multi_ip, unpack
|
|
from .modules.layers import get_linear_split_map
|
|
from transformers import SiglipVisionModel, SiglipImageProcessor
|
|
import torchvision.transforms.functional as TVF
|
|
import math
|
|
from shared.utils.utils import convert_image_to_tensor, convert_tensor_to_image
|
|
|
|
from .util import (
|
|
aspect_ratio_to_height_width,
|
|
load_ae,
|
|
load_clip,
|
|
load_flow_model,
|
|
load_t5,
|
|
save_image,
|
|
)
|
|
|
|
from PIL import Image
|
|
|
|
def resize_and_centercrop_image(image, target_height_ref1, target_width_ref1):
|
|
target_height_ref1 = int(target_height_ref1 // 64 * 64)
|
|
target_width_ref1 = int(target_width_ref1 // 64 * 64)
|
|
h, w = image.shape[-2:]
|
|
if h < target_height_ref1 or w < target_width_ref1:
|
|
# 计算长宽比
|
|
aspect_ratio = w / h
|
|
if h < target_height_ref1:
|
|
new_h = target_height_ref1
|
|
new_w = new_h * aspect_ratio
|
|
if new_w < target_width_ref1:
|
|
new_w = target_width_ref1
|
|
new_h = new_w / aspect_ratio
|
|
else:
|
|
new_w = target_width_ref1
|
|
new_h = new_w / aspect_ratio
|
|
if new_h < target_height_ref1:
|
|
new_h = target_height_ref1
|
|
new_w = new_h * aspect_ratio
|
|
else:
|
|
aspect_ratio = w / h
|
|
tgt_aspect_ratio = target_width_ref1 / target_height_ref1
|
|
if aspect_ratio > tgt_aspect_ratio:
|
|
new_h = target_height_ref1
|
|
new_w = new_h * aspect_ratio
|
|
else:
|
|
new_w = target_width_ref1
|
|
new_h = new_w / aspect_ratio
|
|
# 使用 TVF.resize 进行图像缩放
|
|
image = TVF.resize(image, (math.ceil(new_h), math.ceil(new_w)))
|
|
# 计算中心裁剪的参数
|
|
top = (image.shape[-2] - target_height_ref1) // 2
|
|
left = (image.shape[-1] - target_width_ref1) // 2
|
|
# 使用 TVF.crop 进行中心裁剪
|
|
image = TVF.crop(image, top, left, target_height_ref1, target_width_ref1)
|
|
return image
|
|
|
|
|
|
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
|
|
):
|
|
self.device = torch.device(f"cuda")
|
|
self.VAE_dtype = VAE_dtype
|
|
self.dtype = dtype
|
|
torch_device = "cpu"
|
|
self.guidance_max_phases = model_def.get("guidance_max_phases", 0)
|
|
|
|
# model_filename = ["c:/temp/flux1-schnell.safetensors"]
|
|
|
|
self.t5 = load_t5(torch_device, text_encoder_filename, max_length=512)
|
|
self.clip = load_clip(torch_device)
|
|
self.name = model_def.get("flux-model", "flux-dev")
|
|
# self.name= "flux-dev-kontext"
|
|
# self.name= "flux-dev"
|
|
# self.name= "flux-schnell"
|
|
source = model_def.get("source", None)
|
|
self.model = load_flow_model(self.name, model_filename[0] if source is None else source, torch_device)
|
|
|
|
self.vae = load_ae(self.name, device=torch_device)
|
|
|
|
siglip_processor = siglip_model = feature_embedder = None
|
|
if self.name == 'flux-dev-uso':
|
|
siglip_path = "ckpts/siglip-so400m-patch14-384"
|
|
siglip_processor = SiglipImageProcessor.from_pretrained(siglip_path)
|
|
siglip_model = SiglipVisionModel.from_pretrained(siglip_path)
|
|
siglip_model.eval().to("cpu")
|
|
if len(model_filename) > 1:
|
|
from .modules.layers import SigLIPMultiFeatProjModel
|
|
feature_embedder = SigLIPMultiFeatProjModel(
|
|
siglip_token_nums=729,
|
|
style_token_nums=64,
|
|
siglip_token_dims=1152,
|
|
hidden_size=3072, #self.hidden_size,
|
|
context_layer_norm=True,
|
|
)
|
|
offload.load_model_data(feature_embedder, model_filename[1])
|
|
self.vision_encoder = siglip_model
|
|
self.vision_encoder_processor = siglip_processor
|
|
self.feature_embedder = feature_embedder
|
|
|
|
# offload.change_dtype(self.model, dtype, True)
|
|
# offload.save_model(self.model, "flux-dev.safetensors")
|
|
|
|
if not source is None:
|
|
from wgp import save_model
|
|
save_model(self.model, model_type, dtype, None)
|
|
|
|
if save_quantized:
|
|
from wgp import save_quantized_model
|
|
save_quantized_model(self.model, model_type, model_filename[0], dtype, None)
|
|
|
|
split_linear_modules_map = get_linear_split_map()
|
|
self.model.split_linear_modules_map = split_linear_modules_map
|
|
offload.split_linear_modules(self.model, split_linear_modules_map )
|
|
|
|
|
|
def generate(
|
|
self,
|
|
seed: int | None = None,
|
|
input_prompt: str = "replace the logo with the text 'Black Forest Labs'",
|
|
n_prompt: str = None,
|
|
sampling_steps: int = 20,
|
|
input_ref_images = None,
|
|
width= 832,
|
|
height=480,
|
|
embedded_guidance_scale: float = 2.5,
|
|
guide_scale = 2.5,
|
|
fit_into_canvas = None,
|
|
callback = None,
|
|
loras_slists = None,
|
|
batch_size = 1,
|
|
video_prompt_type = "",
|
|
joint_pass = False,
|
|
image_refs_relative_size = 100,
|
|
**bbargs
|
|
):
|
|
if self._interrupt:
|
|
return None
|
|
if self.guidance_max_phases < 1: guide_scale = 1
|
|
if n_prompt is None or len(n_prompt) == 0: n_prompt = "low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"
|
|
device="cuda"
|
|
flux_dev_uso = self.name in ['flux-dev-uso']
|
|
image_stiching = not self.name in ['flux-dev-uso'] #and False
|
|
# image_refs_relative_size = 100
|
|
crop = False
|
|
input_ref_images = [] if input_ref_images is None else input_ref_images[:]
|
|
ref_style_imgs = []
|
|
if "I" in video_prompt_type and len(input_ref_images) > 0:
|
|
if flux_dev_uso :
|
|
if "J" in video_prompt_type:
|
|
ref_style_imgs = input_ref_images
|
|
input_ref_images = []
|
|
elif len(input_ref_images) > 1 :
|
|
ref_style_imgs = input_ref_images[-1:]
|
|
input_ref_images = input_ref_images[:-1]
|
|
if image_stiching:
|
|
# 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)
|
|
# actual rescale will happen in prepare_kontext
|
|
for new_img in input_ref_images[1:]:
|
|
stiched = stitch_images(stiched, new_img)
|
|
input_ref_images = [stiched]
|
|
else:
|
|
first_ref = 0
|
|
if "K" in video_prompt_type:
|
|
# image latents tiling method
|
|
w, h = input_ref_images[0].size
|
|
if crop :
|
|
img = convert_image_to_tensor(input_ref_images[0])
|
|
img = resize_and_centercrop_image(img, height, width)
|
|
input_ref_images[0] = convert_tensor_to_image(img)
|
|
else:
|
|
height, width = calculate_new_dimensions(height, width, h, w, fit_into_canvas)
|
|
input_ref_images[0] = input_ref_images[0].resize((width, height), resample=Image.Resampling.LANCZOS)
|
|
first_ref = 1
|
|
|
|
for i in range(first_ref,len(input_ref_images)):
|
|
w, h = input_ref_images[i].size
|
|
if crop:
|
|
img = convert_image_to_tensor(input_ref_images[i])
|
|
img = resize_and_centercrop_image(img, int(height*image_refs_relative_size/100), int(width*image_refs_relative_size/100))
|
|
input_ref_images[i] = convert_tensor_to_image(img)
|
|
else:
|
|
image_height, image_width = calculate_new_dimensions(int(height*image_refs_relative_size/100), int(width*image_refs_relative_size/100), h, w, fit_into_canvas)
|
|
input_ref_images[i] = input_ref_images[i].resize((image_width, image_height), resample=Image.Resampling.LANCZOS)
|
|
else:
|
|
input_ref_images = None
|
|
|
|
if flux_dev_uso :
|
|
inp, height, width = prepare_multi_ip(
|
|
ae=self.vae,
|
|
img_cond_list=input_ref_images,
|
|
target_width=width,
|
|
target_height=height,
|
|
bs=batch_size,
|
|
seed=seed,
|
|
device=device,
|
|
)
|
|
else:
|
|
inp, height, width = prepare_kontext(
|
|
ae=self.vae,
|
|
img_cond_list=input_ref_images,
|
|
target_width=width,
|
|
target_height=height,
|
|
bs=batch_size,
|
|
seed=seed,
|
|
device=device,
|
|
)
|
|
|
|
inp.update(prepare_prompt(self.t5, self.clip, batch_size, input_prompt))
|
|
if guide_scale != 1:
|
|
inp.update(prepare_prompt(self.t5, self.clip, batch_size, n_prompt, neg = True, device=device))
|
|
|
|
timesteps = get_schedule(sampling_steps, inp["img"].shape[1], shift=(self.name != "flux-schnell"))
|
|
|
|
ref_style_imgs = [self.vision_encoder_processor(img, return_tensors="pt").to(self.device) for img in ref_style_imgs]
|
|
if self.feature_embedder is not None and ref_style_imgs is not None and len(ref_style_imgs) > 0 and self.vision_encoder is not None:
|
|
# processing style feat into textural hidden space
|
|
siglip_embedding = [self.vision_encoder(**emb, output_hidden_states=True) for emb in ref_style_imgs]
|
|
siglip_embedding = torch.cat([self.feature_embedder(emb) for emb in siglip_embedding], dim=1)
|
|
siglip_embedding_ids = torch.zeros( siglip_embedding.shape[0], siglip_embedding.shape[1], 3 ).to(device)
|
|
inp["siglip_embedding"] = siglip_embedding
|
|
inp["siglip_embedding_ids"] = siglip_embedding_ids
|
|
|
|
def unpack_latent(x):
|
|
return unpack(x.float(), height, width)
|
|
|
|
# denoise initial noise
|
|
x = denoise(self.model, **inp, timesteps=timesteps, guidance=embedded_guidance_scale, real_guidance_scale =guide_scale, callback=callback, pipeline=self, loras_slists= loras_slists, unpack_latent = unpack_latent, joint_pass = joint_pass)
|
|
if x==None: return None
|
|
# decode latents to pixel space
|
|
x = unpack_latent(x)
|
|
with torch.autocast(device_type=device, dtype=torch.bfloat16):
|
|
x = self.vae.decode(x)
|
|
|
|
x = x.clamp(-1, 1)
|
|
x = x.transpose(0, 1)
|
|
return x
|
|
|