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8
README.md
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@ -20,6 +20,14 @@ WanGP supports the Wan (and derived models), Hunyuan Video and LTV Video models
**Follow DeepBeepMeep on Twitter/X to get the Latest News**: https://x.com/deepbeepmeep
## 🔥 Latest Updates :
### August 8 2025: WanGP v7.72 - Qwen Rebirth
Ever wondered what impact not using Guidance has on a model that expects it ? Just look at Qween Image in WanGP 7.71 whose outputs were erratic. Somehow I had convinced myself that Qwen was a distilled model. In fact Qwen was dying for a negative prompt. And in WanGP 7.72 there is at last one for him.
As Qwen is not so picky after all I have added also quantized text encoder which reduces the RAM requirements of Qwen by 10 GB (the text encoder quantized version produced garbage before)
Hopefully this new release solves as well the Sage/Sage2 blacscreen on some GPUs.
### August 6 2025: WanGP v7.71 - Picky, picky
This release comes with two new models :

682
i2v_inference.py
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@ -1,682 +0,0 @@
import os
import time
import argparse
import json
import torch
import traceback
import gc
import random
# These imports rely on your existing code structure
# They must match the location of your WAN code, etc.
import wan
from wan.configs import MAX_AREA_CONFIGS, WAN_CONFIGS
from wan.modules.attention import get_attention_modes
from wan.utils.utils import cache_video
from mmgp import offload, safetensors2, profile_type
try:
import triton
except ImportError:
pass
DATA_DIR = "ckpts"
# --------------------------------------------------
# HELPER FUNCTIONS
# --------------------------------------------------
def sanitize_file_name(file_name):
"""Clean up file name from special chars."""
return (
file_name.replace("/", "")
.replace("\\", "")
.replace(":", "")
.replace("|", "")
.replace("?", "")
.replace("<", "")
.replace(">", "")
.replace('"', "")
)
def extract_preset(lset_name, lora_dir, loras):
"""
Load a .lset JSON that lists the LoRA files to apply, plus multipliers
and possibly a suggested prompt prefix.
"""
lset_name = sanitize_file_name(lset_name)
if not lset_name.endswith(".lset"):
lset_name_filename = os.path.join(lora_dir, lset_name + ".lset")
else:
lset_name_filename = os.path.join(lora_dir, lset_name)
if not os.path.isfile(lset_name_filename):
raise ValueError(f"Preset '{lset_name}' not found in {lora_dir}")
with open(lset_name_filename, "r", encoding="utf-8") as reader:
text = reader.read()
lset = json.loads(text)
loras_choices_files = lset["loras"]
loras_choices = []
missing_loras = []
for lora_file in loras_choices_files:
# Build absolute path and see if it is in loras
full_lora_path = os.path.join(lora_dir, lora_file)
if full_lora_path in loras:
idx = loras.index(full_lora_path)
loras_choices.append(str(idx))
else:
missing_loras.append(lora_file)
if len(missing_loras) > 0:
missing_list = ", ".join(missing_loras)
raise ValueError(f"Missing LoRA files for preset: {missing_list}")
loras_mult_choices = lset["loras_mult"]
prompt_prefix = lset.get("prompt", "")
full_prompt = lset.get("full_prompt", False)
return loras_choices, loras_mult_choices, prompt_prefix, full_prompt
def get_attention_mode(args_attention, installed_modes):
"""
Decide which attention mode to use: either the user choice or auto fallback.
"""
if args_attention == "auto":
for candidate in ["sage2", "sage", "sdpa"]:
if candidate in installed_modes:
return candidate
return "sdpa" # last fallback
elif args_attention in installed_modes:
return args_attention
else:
raise ValueError(
f"Requested attention mode '{args_attention}' not installed. "
f"Installed modes: {installed_modes}"
)
def load_i2v_model(model_filename, text_encoder_filename, is_720p):
"""
Load the i2v model with a specific size config and text encoder.
"""
if is_720p:
print("Loading 14B-720p i2v model ...")
cfg = WAN_CONFIGS['i2v-14B']
wan_model = wan.WanI2V(
config=cfg,
checkpoint_dir=DATA_DIR,
model_filename=model_filename,
text_encoder_filename=text_encoder_filename
)
else:
print("Loading 14B-480p i2v model ...")
cfg = WAN_CONFIGS['i2v-14B']
wan_model = wan.WanI2V(
config=cfg,
checkpoint_dir=DATA_DIR,
model_filename=model_filename,
text_encoder_filename=text_encoder_filename
)
# Pipe structure
pipe = {
"transformer": wan_model.model,
"text_encoder": wan_model.text_encoder.model,
"text_encoder_2": wan_model.clip.model,
"vae": wan_model.vae.model
}
return wan_model, pipe
def setup_loras(pipe, lora_dir, lora_preset, num_inference_steps):
"""
Load loras from a directory, optionally apply a preset.
"""
from pathlib import Path
import glob
if not lora_dir or not Path(lora_dir).is_dir():
print("No valid --lora-dir provided or directory doesn't exist, skipping LoRA setup.")
return [], [], [], "", "", False
# Gather LoRA files
loras = sorted(
glob.glob(os.path.join(lora_dir, "*.sft"))
+ glob.glob(os.path.join(lora_dir, "*.safetensors"))
)
loras_names = [Path(x).stem for x in loras]
# Offload them with no activation
offload.load_loras_into_model(pipe["transformer"], loras, activate_all_loras=False)
# If user gave a preset, apply it
default_loras_choices = []
default_loras_multis_str = ""
default_prompt_prefix = ""
preset_applied_full_prompt = False
if lora_preset:
loras_choices, loras_mult, prefix, full_prompt = extract_preset(lora_preset, lora_dir, loras)
default_loras_choices = loras_choices
# If user stored loras_mult as a list or string in JSON, unify that to str
if isinstance(loras_mult, list):
# Just store them in a single line
default_loras_multis_str = " ".join([str(x) for x in loras_mult])
else:
default_loras_multis_str = str(loras_mult)
default_prompt_prefix = prefix
preset_applied_full_prompt = full_prompt
return (
loras,
loras_names,
default_loras_choices,
default_loras_multis_str,
default_prompt_prefix,
preset_applied_full_prompt
)
def parse_loras_and_activate(
transformer,
loras,
loras_choices,
loras_mult_str,
num_inference_steps
):
"""
Activate the chosen LoRAs with multipliers over the pipeline's transformer.
Supports stepwise expansions (like "0.5,0.8" for partial steps).
"""
if not loras or not loras_choices:
# no LoRAs selected
return
# Handle multipliers
def is_float_or_comma_list(x):
"""
Example: "0.5", or "0.8,1.0", etc. is valid.
"""
if not x:
return False
for chunk in x.split(","):
try:
float(chunk.strip())
except ValueError:
return False
return True
# Convert multiline or spaced lines to a single list
lines = [
line.strip()
for line in loras_mult_str.replace("\r", "\n").split("\n")
if line.strip() and not line.strip().startswith("#")
]
# Now combine them by space
joined_line = " ".join(lines) # "1.0 2.0,3.0"
if not joined_line.strip():
multipliers = []
else:
multipliers = joined_line.split(" ")
# Expand each item
final_multipliers = []
for mult in multipliers:
mult = mult.strip()
if not mult:
continue
if is_float_or_comma_list(mult):
# Could be "0.7" or "0.5,0.6"
if "," in mult:
# expand over steps
chunk_vals = [float(x.strip()) for x in mult.split(",")]
expanded = expand_list_over_steps(chunk_vals, num_inference_steps)
final_multipliers.append(expanded)
else:
final_multipliers.append(float(mult))
else:
raise ValueError(f"Invalid LoRA multiplier: '{mult}'")
# If fewer multipliers than chosen LoRAs => pad with 1.0
needed = len(loras_choices) - len(final_multipliers)
if needed > 0:
final_multipliers += [1.0]*needed
# Actually activate them
offload.activate_loras(transformer, loras_choices, final_multipliers)
def expand_list_over_steps(short_list, num_steps):
"""
If user gave (0.5, 0.8) for example, expand them over `num_steps`.
The expansion is simply linear slice across steps.
"""
result = []
inc = len(short_list) / float(num_steps)
idxf = 0.0
for _ in range(num_steps):
value = short_list[int(idxf)]
result.append(value)
idxf += inc
return result
def download_models_if_needed(transformer_filename_i2v, text_encoder_filename, local_folder=DATA_DIR):
"""
Checks if all required WAN 2.1 i2v files exist locally under 'ckpts/'.
If not, downloads them from a Hugging Face Hub repo.
Adjust the 'repo_id' and needed files as appropriate.
"""
import os
from pathlib import Path
try:
from huggingface_hub import hf_hub_download, snapshot_download
except ImportError as e:
raise ImportError(
"huggingface_hub is required for automatic model download. "
"Please install it via `pip install huggingface_hub`."
) from e
# Identify just the filename portion for each path
def basename(path_str):
return os.path.basename(path_str)
repo_id = "DeepBeepMeep/Wan2.1"
target_root = local_folder
# You can customize this list as needed for i2v usage.
# At minimum you need:
# 1) The requested i2v transformer file
# 2) The requested text encoder file
# 3) VAE file
# 4) The open-clip xlm-roberta-large weights
#
# If your i2v config references additional files, add them here.
needed_files = [
"Wan2.1_VAE.pth",
"models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth",
basename(text_encoder_filename),
basename(transformer_filename_i2v),
]
# The original script also downloads an entire "xlm-roberta-large" folder
# via snapshot_download. If you require that for your pipeline,
# you can add it here, for example:
subfolder_name = "xlm-roberta-large"
if not Path(os.path.join(target_root, subfolder_name)).exists():
snapshot_download(repo_id=repo_id, allow_patterns=subfolder_name + "/*", local_dir=target_root)
for filename in needed_files:
local_path = os.path.join(target_root, filename)
if not os.path.isfile(local_path):
print(f"File '{filename}' not found locally. Downloading from {repo_id} ...")
hf_hub_download(
repo_id=repo_id,
filename=filename,
local_dir=target_root
)
else:
# Already present
pass
print("All required i2v files are present.")
# --------------------------------------------------
# ARGUMENT PARSER
# --------------------------------------------------
def parse_args():
parser = argparse.ArgumentParser(
description="Image-to-Video inference using WAN 2.1 i2v"
)
# Model + Tools
parser.add_argument(
"--quantize-transformer",
action="store_true",
help="Use on-the-fly transformer quantization"
)
parser.add_argument(
"--compile",
action="store_true",
help="Enable PyTorch 2.0 compile for the transformer"
)
parser.add_argument(
"--attention",
type=str,
default="auto",
help="Which attention to use: auto, sdpa, sage, sage2, flash"
)
parser.add_argument(
"--profile",
type=int,
default=4,
help="Memory usage profile number [1..5]; see original script or use 2 if you have low VRAM"
)
parser.add_argument(
"--preload",
type=int,
default=0,
help="Megabytes of the diffusion model to preload in VRAM (only used in some profiles)"
)
parser.add_argument(
"--verbose",
type=int,
default=1,
help="Verbosity level [0..5]"
)
# i2v Model
parser.add_argument(
"--transformer-file",
type=str,
default=f"{DATA_DIR}/wan2.1_image2video_480p_14B_quanto_int8.safetensors",
help="Which i2v model to load"
)
parser.add_argument(
"--text-encoder-file",
type=str,
default=f"{DATA_DIR}/models_t5_umt5-xxl-enc-quanto_int8.safetensors",
help="Which text encoder to use"
)
# LoRA
parser.add_argument(
"--lora-dir",
type=str,
default="",
help="Path to a directory containing i2v LoRAs"
)
parser.add_argument(
"--lora-preset",
type=str,
default="",
help="A .lset preset name in the lora_dir to auto-apply"
)
# Generation Options
parser.add_argument("--prompt", type=str, default=None, required=True, help="Prompt for generation")
parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt")
parser.add_argument("--resolution", type=str, default="832x480", help="WxH")
parser.add_argument("--frames", type=int, default=64, help="Number of frames (16=1s if fps=16). Must be multiple of 4 +/- 1 in WAN.")
parser.add_argument("--steps", type=int, default=30, help="Number of denoising steps.")
parser.add_argument("--guidance-scale", type=float, default=5.0, help="Classifier-free guidance scale")
parser.add_argument("--flow-shift", type=float, default=3.0, help="Flow shift parameter. Generally 3.0 for 480p, 5.0 for 720p.")
parser.add_argument("--riflex", action="store_true", help="Enable RIFLEx for longer videos")
parser.add_argument("--teacache", type=float, default=0.25, help="TeaCache multiplier, e.g. 0.5, 2.0, etc.")
parser.add_argument("--teacache-start", type=float, default=0.1, help="Teacache start step percentage [0..100]")
parser.add_argument("--seed", type=int, default=-1, help="Random seed. -1 means random each time.")
parser.add_argument("--slg-layers", type=str, default=None, help="Which layers to use for skip layer guidance")
parser.add_argument("--slg-start", type=float, default=0.0, help="Percentage in to start SLG")
parser.add_argument("--slg-end", type=float, default=1.0, help="Percentage in to end SLG")
# LoRA usage
parser.add_argument("--loras-choices", type=str, default="", help="Comma-separated list of chosen LoRA indices or preset names to load. Usually you only use the preset.")
parser.add_argument("--loras-mult", type=str, default="", help="Multipliers for each chosen LoRA. Example: '1.0 1.2,1.3' etc.")
# Input
parser.add_argument(
"--input-image",
type=str,
default=None,
required=True,
help="Path to an input image (or multiple)."
)
parser.add_argument(
"--output-file",
type=str,
default="output.mp4",
help="Where to save the resulting video."
)
return parser.parse_args()
# --------------------------------------------------
# MAIN
# --------------------------------------------------
def main():
args = parse_args()
# Setup environment
offload.default_verboseLevel = args.verbose
installed_attn_modes = get_attention_modes()
# Decide attention
chosen_attention = get_attention_mode(args.attention, installed_attn_modes)
offload.shared_state["_attention"] = chosen_attention
# Determine i2v resolution format
if "720" in args.transformer_file:
is_720p = True
else:
is_720p = False
# Make sure we have the needed models locally
download_models_if_needed(args.transformer_file, args.text_encoder_file)
# Load i2v
wan_model, pipe = load_i2v_model(
model_filename=args.transformer_file,
text_encoder_filename=args.text_encoder_file,
is_720p=is_720p
)
wan_model._interrupt = False
# Offload / profile
# e.g. for your script: offload.profile(pipe, profile_no=args.profile, compile=..., quantizeTransformer=...)
# pass the budgets if you want, etc.
kwargs = {}
if args.profile == 2 or args.profile == 4:
# preload is in MB
if args.preload == 0:
budgets = {"transformer": 100, "text_encoder": 100, "*": 1000}
else:
budgets = {"transformer": args.preload, "text_encoder": 100, "*": 1000}
kwargs["budgets"] = budgets
elif args.profile == 3:
kwargs["budgets"] = {"*": "70%"}
compile_choice = "transformer" if args.compile else ""
# Create the offload object
offloadobj = offload.profile(
pipe,
profile_no=args.profile,
compile=compile_choice,
quantizeTransformer=args.quantize_transformer,
**kwargs
)
# If user wants to use LoRAs
(
loras,
loras_names,
default_loras_choices,
default_loras_multis_str,
preset_prompt_prefix,
preset_full_prompt
) = setup_loras(pipe, args.lora_dir, args.lora_preset, args.steps)
# Combine user prompt with preset prompt if the preset indicates so
if preset_prompt_prefix:
if preset_full_prompt:
# Full override
user_prompt = preset_prompt_prefix
else:
# Just prefix
user_prompt = preset_prompt_prefix + "\n" + args.prompt
else:
user_prompt = args.prompt
# Actually parse user LoRA choices if they did not rely purely on the preset
if args.loras_choices:
# If user gave e.g. "0,1", we treat that as new additions
lora_choice_list = [x.strip() for x in args.loras_choices.split(",")]
else:
# Use the defaults from the preset
lora_choice_list = default_loras_choices
# Activate them
parse_loras_and_activate(
pipe["transformer"], loras, lora_choice_list, args.loras_mult or default_loras_multis_str, args.steps
)
# Negative prompt
negative_prompt = args.negative_prompt or ""
# Sanity check resolution
if "*" in args.resolution.lower():
print("ERROR: resolution must be e.g. 832x480 not '832*480'. Fixing it.")
resolution_str = args.resolution.lower().replace("*", "x")
else:
resolution_str = args.resolution
try:
width, height = [int(x) for x in resolution_str.split("x")]
except:
raise ValueError(f"Invalid resolution: '{resolution_str}'")
# Parse slg_layers from comma-separated string to a Python list of ints (or None if not provided)
if args.slg_layers:
slg_list = [int(x) for x in args.slg_layers.split(",")]
else:
slg_list = None
# Additional checks (from your original code).
if "480p" in args.transformer_file:
# Then we cannot exceed certain area for 480p model
if width * height > 832*480:
raise ValueError("You must use the 720p i2v model to generate bigger than 832x480.")
# etc.
# Handle random seed
if args.seed < 0:
args.seed = random.randint(0, 999999999)
print(f"Using seed={args.seed}")
# Setup tea cache if needed
trans = wan_model.model
trans.enable_cache = (args.teacache > 0)
if trans.enable_cache:
if "480p" in args.transformer_file:
# example from your code
trans.coefficients = [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01]
elif "720p" in args.transformer_file:
trans.coefficients = [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683]
else:
raise ValueError("Teacache not supported for this model variant")
# Attempt generation
print("Starting generation ...")
start_time = time.time()
# Read the input image
if not os.path.isfile(args.input_image):
raise ValueError(f"Input image does not exist: {args.input_image}")
from PIL import Image
input_img = Image.open(args.input_image).convert("RGB")
# Possibly load more than one image if you want "multiple images" – but here we'll just do single for demonstration
# Define the generation call
# - frames => must be multiple of 4 plus 1 as per original script's note, e.g. 81, 65, ...
# You can correct to that if needed:
frame_count = (args.frames // 4)*4 + 1 # ensures it's 4*N+1
# RIFLEx
enable_riflex = args.riflex
# If teacache => reset counters
if trans.enable_cache:
trans.teacache_counter = 0
trans.cache_multiplier = args.teacache
trans.cache_start_step = int(args.teacache_start * args.steps / 100.0)
trans.num_steps = args.steps
trans.cache_skipped_steps = 0
trans.previous_residual_uncond = None
trans.previous_residual_cond = None
# VAE Tiling
device_mem_capacity = torch.cuda.get_device_properties(0).total_memory / 1048576
if device_mem_capacity >= 28000: # 81 frames 720p requires about 28 GB VRAM
use_vae_config = 1
elif device_mem_capacity >= 8000:
use_vae_config = 2
else:
use_vae_config = 3
if use_vae_config == 1:
VAE_tile_size = 0
elif use_vae_config == 2:
VAE_tile_size = 256
else:
VAE_tile_size = 128
print('Using VAE tile size of', VAE_tile_size)
# Actually run the i2v generation
try:
sample_frames = wan_model.generate(
input_prompt = user_prompt,
image_start = input_img,
frame_num=frame_count,
width=width,
height=height,
# max_area=MAX_AREA_CONFIGS[f"{width}*{height}"], # or you can pass your custom
shift=args.flow_shift,
sampling_steps=args.steps,
guide_scale=args.guidance_scale,
n_prompt=negative_prompt,
seed=args.seed,
offload_model=False,
callback=None, # or define your own callback if you want
enable_RIFLEx=enable_riflex,
VAE_tile_size=VAE_tile_size,
joint_pass=slg_list is None, # set if you want a small speed improvement without SLG
slg_layers=slg_list,
slg_start=args.slg_start,
slg_end=args.slg_end,
)
except Exception as e:
offloadobj.unload_all()
gc.collect()
torch.cuda.empty_cache()
err_str = f"Generation failed with error: {e}"
# Attempt to detect OOM errors
s = str(e).lower()
if any(keyword in s for keyword in ["memory", "cuda", "alloc"]):
raise RuntimeError("Likely out-of-VRAM or out-of-RAM error. " + err_str)
else:
traceback.print_exc()
raise RuntimeError(err_str)
# After generation
offloadobj.unload_all()
gc.collect()
torch.cuda.empty_cache()
if sample_frames is None:
raise RuntimeError("No frames were returned (maybe generation was aborted or failed).")
# If teacache was used, we can see how many steps were skipped
if trans.enable_cache:
print(f"TeaCache skipped steps: {trans.teacache_skipped_steps} / {args.steps}")
# Save result
sample_frames = sample_frames.cpu() # shape = c, t, h, w => [3, T, H, W]
os.makedirs(os.path.dirname(args.output_file) or ".", exist_ok=True)
# Use the provided helper from your code to store the MP4
# By default, you used cache_video(tensor=..., save_file=..., fps=16, ...)
# or you can do your own. We'll do the same for consistency:
cache_video(
tensor=sample_frames[None], # shape => [1, c, T, H, W]
save_file=args.output_file,
fps=16,
nrow=1,
normalize=True,
value_range=(-1, 1)
)
end_time = time.time()
elapsed_s = end_time - start_time
print(f"Done! Output written to {args.output_file}. Generation time: {elapsed_s:.1f} seconds.")
if __name__ == "__main__":
main()

35
models/qwen/pipeline_qwenimage.py
View File

@ -458,6 +458,7 @@ class QwenImagePipeline(): #DiffusionPipeline
callback=None,
pipeline=None,
loras_slists=None,
joint_pass= True,
):
r"""
Function invoked when calling the pipeline for generation.
@ -656,43 +657,55 @@ class QwenImagePipeline(): #DiffusionPipeline
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latents.shape[0]).to(latents.dtype)
if do_true_cfg and joint_pass:
noise_pred, neg_noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
encoder_hidden_states_mask_list=[prompt_embeds_mask,negative_prompt_embeds_mask],
encoder_hidden_states_list=[prompt_embeds, negative_prompt_embeds],
img_shapes=img_shapes,
txt_seq_lens_list=[prompt_embeds_mask.sum(dim=1).tolist(),negative_prompt_embeds_mask.sum(dim=1).tolist()],
attention_kwargs=self.attention_kwargs,
**kwargs
)
if noise_pred == None: return None
else:
noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
encoder_hidden_states_mask=prompt_embeds_mask,
encoder_hidden_states=prompt_embeds,
encoder_hidden_states_mask_list=[prompt_embeds_mask],
encoder_hidden_states_list=[prompt_embeds],
img_shapes=img_shapes,
txt_seq_lens=prompt_embeds_mask.sum(dim=1).tolist(),
txt_seq_lens_list=[prompt_embeds_mask.sum(dim=1).tolist()],
attention_kwargs=self.attention_kwargs,
return_dict=False,
**kwargs
)[0]
if noise_pred == None: return None
if do_true_cfg:
# with self.transformer.cache_context("uncond"):
neg_noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
encoder_hidden_states_mask=negative_prompt_embeds_mask,
encoder_hidden_states=negative_prompt_embeds,
encoder_hidden_states_mask_list=[negative_prompt_embeds_mask],
encoder_hidden_states_list=[negative_prompt_embeds],
img_shapes=img_shapes,
txt_seq_lens=negative_prompt_embeds_mask.sum(dim=1).tolist(),
txt_seq_lens_list=[negative_prompt_embeds_mask.sum(dim=1).tolist()],
attention_kwargs=self.attention_kwargs,
return_dict=False,
**kwargs
)[0]
if neg_noise_pred == None: return None
if do_true_cfg:
comb_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
if comb_pred == None: return None
cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True)
noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True)
noise_pred = comb_pred * (cond_norm / noise_norm)
neg_noise_pred = None
# compute the previous noisy sample x_t -> x_t-1
latents_dtype = latents.dtype
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]

6
models/qwen/qwen_handler.py
View File

@ -2,7 +2,7 @@ import torch
def get_qwen_text_encoder_filename(text_encoder_quantization):
text_encoder_filename = "ckpts/Qwen2.5-VL-7B-Instruct/Qwen2.5-VL-7B-Instruct_bf16.safetensors"
if text_encoder_quantization =="int8" and False:
if text_encoder_quantization =="int8":
text_encoder_filename = text_encoder_filename.replace("bf16", "quanto_bf16_int8")
return text_encoder_filename
@ -11,10 +11,8 @@ class family_handler():
def query_model_def(base_model_type, model_def):
model_def_output = {
"image_outputs" : True,
"no_negative_prompt" : True,
}
model_def_output["embedded_guidance"] = True
return model_def_output
@ -69,7 +67,7 @@ class family_handler():
@staticmethod
def update_default_settings(base_model_type, model_def, ui_defaults):
ui_defaults.update({
"embedded_guidance": 4,
"guidance_scale": 4,
})
if model_def.get("reference_image", False):
ui_defaults.update({

7
models/qwen/qwen_main.py
View File

@ -73,6 +73,7 @@ class model_factory():
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,
width= 832,
@ -84,6 +85,7 @@ class model_factory():
batch_size = 1,
video_prompt_type = "",
VAE_tile_size = None,
joint_pass = True,
**bbargs
):
# Generate with different aspect ratios
@ -102,8 +104,12 @@ class model_factory():
# 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 = self.pipeline(
prompt=input_prompt,
negative_prompt=n_prompt,
width=width,
height=height,
num_inference_steps=sampling_steps,
@ -112,6 +118,7 @@ class model_factory():
callback = callback,
pipeline=self,
loras_slists=loras_slists,
joint_pass = joint_pass,
generator=torch.Generator(device="cuda").manual_seed(seed)
)
if image is None: return None

64
models/qwen/transformer_qwenimage.py
View File

@ -512,52 +512,29 @@ class QwenImageTransformer2DModel(nn.Module):
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor = None,
encoder_hidden_states_mask: torch.Tensor = None,
encoder_hidden_states_list = None,
encoder_hidden_states_mask_list = None,
timestep: torch.LongTensor = None,
img_shapes: Optional[List[Tuple[int, int, int]]] = None,
txt_seq_lens: Optional[List[int]] = None,
txt_seq_lens_list = None,
guidance: torch.Tensor = None, # TODO: this should probably be removed
attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
callback= None,
pipeline =None,
) -> Union[torch.Tensor, Transformer2DModelOutput]:
"""
The [`QwenTransformer2DModel`] forward method.
Args:
hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
Input `hidden_states`.
encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
encoder_hidden_states_mask (`torch.Tensor` of shape `(batch_size, text_sequence_length)`):
Mask of the input conditions.
timestep ( `torch.LongTensor`):
Used to indicate denoising step.
attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
tuple.
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
if attention_kwargs is not None:
attention_kwargs = attention_kwargs.copy()
lora_scale = attention_kwargs.pop("scale", 1.0)
else:
lora_scale = 1.0
hidden_states = self.img_in(hidden_states)
timestep = timestep.to(hidden_states.dtype)
hidden_states_list = [hidden_states if i == 0 else hidden_states.clone() for i, _ in enumerate(encoder_hidden_states_list)]
new_encoder_hidden_states_list = []
for encoder_hidden_states in encoder_hidden_states_list:
encoder_hidden_states = self.txt_norm(encoder_hidden_states)
encoder_hidden_states = self.txt_in(encoder_hidden_states)
new_encoder_hidden_states_list.append(encoder_hidden_states)
encoder_hidden_states_list = new_encoder_hidden_states_list
new_encoder_hidden_states_list = encoder_hidden_states = None
if guidance is not None:
guidance = guidance.to(hidden_states.dtype) * 1000
@ -568,14 +545,17 @@ class QwenImageTransformer2DModel(nn.Module):
else self.time_text_embed(timestep, guidance, hidden_states)
)
image_rotary_emb = self.pos_embed(img_shapes, txt_seq_lens, device=hidden_states.device)
image_rotary_emb_list = [ self.pos_embed(img_shapes, txt_seq_lens, device=hidden_states.device) for txt_seq_lens in txt_seq_lens_list]
hidden_states = None
for index_block, block in enumerate(self.transformer_blocks):
if callback != None:
callback(-1, None, False, True)
if pipeline._interrupt:
return [None]
encoder_hidden_states, hidden_states = block(
return [None] * len(hidden_states_list)
for hidden_states, encoder_hidden_states, encoder_hidden_states_mask, image_rotary_emb in zip(hidden_states_list, encoder_hidden_states_list, encoder_hidden_states_mask_list, image_rotary_emb_list):
encoder_hidden_states[...], hidden_states[...] = block(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
encoder_hidden_states_mask=encoder_hidden_states_mask,
@ -585,10 +565,10 @@ class QwenImageTransformer2DModel(nn.Module):
)
# Use only the image part (hidden_states) from the dual-stream blocks
hidden_states = self.norm_out(hidden_states, temb)
output = self.proj_out(hidden_states)
output_list = []
for i in range(len(hidden_states_list)):
hidden_states = self.norm_out(hidden_states_list[i], temb)
hidden_states_list[i] = None
output_list.append(self.proj_out(hidden_states))
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output)
return output_list

2
postprocessing/mmaudio/data/av_utils.py
View File

@ -131,7 +131,7 @@ from pathlib import Path
import torch
def remux_with_audio(video_path: Path, output_path: Path, audio: torch.Tensor, sampling_rate: int):
from wan.utils.utils import extract_audio_tracks, combine_video_with_audio_tracks, cleanup_temp_audio_files
from shared.utils.utils import extract_audio_tracks, combine_video_with_audio_tracks, cleanup_temp_audio_files
with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as f:
temp_path = Path(f.name)

2
shared/utils/vace_preprocessor.py
View File

@ -184,7 +184,7 @@ class VaceVideoProcessor(object):
def _get_frameid_bbox_adjust_last(self, fps, video_frames_count, canvas_height, canvas_width, h, w, fit_into_canvas, crop_box, rng, max_frames= 0, start_frame =0):
from wan.utils.utils import resample
from shared.utils.utils import resample
target_fps = self.max_fps

2
wgp.py
View File

@ -50,7 +50,7 @@ AUTOSAVE_FILENAME = "queue.zip"
PROMPT_VARS_MAX = 10
target_mmgp_version = "3.5.7"
WanGP_version = "7.71"
WanGP_version = "7.72"
settings_version = 2.23
max_source_video_frames = 3000
prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer = None, None, None, None