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righted a wrong
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deepbeepmeep
parent
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commit
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@ -20,6 +20,14 @@ WanGP supports the Wan (and derived models), Hunyuan Video and LTV Video models
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**Follow DeepBeepMeep on Twitter/X to get the Latest News**: https://x.com/deepbeepmeep
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## 🔥 Latest Updates :
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### August 8 2025: WanGP v7.72 - Qwen Rebirth
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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.
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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)
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Hopefully this new release solves as well the Sage/Sage2 blacscreen on some GPUs.
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### August 6 2025: WanGP v7.71 - Picky, picky
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This release comes with two new models :
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682
i2v_inference.py
682
i2v_inference.py
@ -1,682 +0,0 @@
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import os
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import time
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import argparse
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import json
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import torch
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import traceback
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import gc
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import random
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# These imports rely on your existing code structure
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# They must match the location of your WAN code, etc.
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import wan
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from wan.configs import MAX_AREA_CONFIGS, WAN_CONFIGS
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from wan.modules.attention import get_attention_modes
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from wan.utils.utils import cache_video
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from mmgp import offload, safetensors2, profile_type
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try:
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import triton
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except ImportError:
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pass
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DATA_DIR = "ckpts"
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# --------------------------------------------------
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# HELPER FUNCTIONS
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# --------------------------------------------------
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def sanitize_file_name(file_name):
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"""Clean up file name from special chars."""
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return (
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file_name.replace("/", "")
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.replace("\\", "")
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.replace(":", "")
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.replace("|", "")
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.replace("?", "")
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.replace("<", "")
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.replace(">", "")
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.replace('"', "")
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)
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def extract_preset(lset_name, lora_dir, loras):
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"""
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Load a .lset JSON that lists the LoRA files to apply, plus multipliers
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and possibly a suggested prompt prefix.
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"""
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lset_name = sanitize_file_name(lset_name)
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if not lset_name.endswith(".lset"):
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lset_name_filename = os.path.join(lora_dir, lset_name + ".lset")
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else:
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lset_name_filename = os.path.join(lora_dir, lset_name)
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if not os.path.isfile(lset_name_filename):
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raise ValueError(f"Preset '{lset_name}' not found in {lora_dir}")
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with open(lset_name_filename, "r", encoding="utf-8") as reader:
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text = reader.read()
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lset = json.loads(text)
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loras_choices_files = lset["loras"]
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loras_choices = []
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missing_loras = []
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for lora_file in loras_choices_files:
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# Build absolute path and see if it is in loras
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full_lora_path = os.path.join(lora_dir, lora_file)
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if full_lora_path in loras:
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idx = loras.index(full_lora_path)
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loras_choices.append(str(idx))
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else:
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missing_loras.append(lora_file)
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if len(missing_loras) > 0:
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missing_list = ", ".join(missing_loras)
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raise ValueError(f"Missing LoRA files for preset: {missing_list}")
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loras_mult_choices = lset["loras_mult"]
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prompt_prefix = lset.get("prompt", "")
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full_prompt = lset.get("full_prompt", False)
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return loras_choices, loras_mult_choices, prompt_prefix, full_prompt
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def get_attention_mode(args_attention, installed_modes):
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"""
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Decide which attention mode to use: either the user choice or auto fallback.
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"""
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if args_attention == "auto":
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for candidate in ["sage2", "sage", "sdpa"]:
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if candidate in installed_modes:
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return candidate
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return "sdpa" # last fallback
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elif args_attention in installed_modes:
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return args_attention
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else:
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raise ValueError(
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f"Requested attention mode '{args_attention}' not installed. "
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f"Installed modes: {installed_modes}"
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)
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def load_i2v_model(model_filename, text_encoder_filename, is_720p):
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"""
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Load the i2v model with a specific size config and text encoder.
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"""
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if is_720p:
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print("Loading 14B-720p i2v model ...")
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cfg = WAN_CONFIGS['i2v-14B']
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wan_model = wan.WanI2V(
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config=cfg,
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checkpoint_dir=DATA_DIR,
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model_filename=model_filename,
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text_encoder_filename=text_encoder_filename
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)
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else:
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print("Loading 14B-480p i2v model ...")
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cfg = WAN_CONFIGS['i2v-14B']
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wan_model = wan.WanI2V(
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config=cfg,
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checkpoint_dir=DATA_DIR,
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model_filename=model_filename,
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text_encoder_filename=text_encoder_filename
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)
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# Pipe structure
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pipe = {
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"transformer": wan_model.model,
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"text_encoder": wan_model.text_encoder.model,
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"text_encoder_2": wan_model.clip.model,
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"vae": wan_model.vae.model
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}
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return wan_model, pipe
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def setup_loras(pipe, lora_dir, lora_preset, num_inference_steps):
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"""
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Load loras from a directory, optionally apply a preset.
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"""
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from pathlib import Path
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import glob
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if not lora_dir or not Path(lora_dir).is_dir():
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print("No valid --lora-dir provided or directory doesn't exist, skipping LoRA setup.")
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return [], [], [], "", "", False
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# Gather LoRA files
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loras = sorted(
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glob.glob(os.path.join(lora_dir, "*.sft"))
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+ glob.glob(os.path.join(lora_dir, "*.safetensors"))
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)
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loras_names = [Path(x).stem for x in loras]
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# Offload them with no activation
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offload.load_loras_into_model(pipe["transformer"], loras, activate_all_loras=False)
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# If user gave a preset, apply it
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default_loras_choices = []
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default_loras_multis_str = ""
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default_prompt_prefix = ""
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preset_applied_full_prompt = False
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if lora_preset:
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loras_choices, loras_mult, prefix, full_prompt = extract_preset(lora_preset, lora_dir, loras)
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default_loras_choices = loras_choices
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# If user stored loras_mult as a list or string in JSON, unify that to str
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if isinstance(loras_mult, list):
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# Just store them in a single line
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default_loras_multis_str = " ".join([str(x) for x in loras_mult])
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else:
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default_loras_multis_str = str(loras_mult)
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default_prompt_prefix = prefix
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preset_applied_full_prompt = full_prompt
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return (
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loras,
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loras_names,
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default_loras_choices,
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default_loras_multis_str,
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default_prompt_prefix,
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preset_applied_full_prompt
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)
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def parse_loras_and_activate(
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transformer,
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loras,
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loras_choices,
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loras_mult_str,
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num_inference_steps
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):
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"""
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Activate the chosen LoRAs with multipliers over the pipeline's transformer.
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Supports stepwise expansions (like "0.5,0.8" for partial steps).
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"""
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if not loras or not loras_choices:
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# no LoRAs selected
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return
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# Handle multipliers
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def is_float_or_comma_list(x):
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"""
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Example: "0.5", or "0.8,1.0", etc. is valid.
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"""
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if not x:
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return False
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for chunk in x.split(","):
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try:
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float(chunk.strip())
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except ValueError:
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return False
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return True
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# Convert multiline or spaced lines to a single list
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lines = [
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line.strip()
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for line in loras_mult_str.replace("\r", "\n").split("\n")
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if line.strip() and not line.strip().startswith("#")
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]
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# Now combine them by space
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joined_line = " ".join(lines) # "1.0 2.0,3.0"
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if not joined_line.strip():
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multipliers = []
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else:
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multipliers = joined_line.split(" ")
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# Expand each item
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final_multipliers = []
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for mult in multipliers:
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mult = mult.strip()
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if not mult:
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continue
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if is_float_or_comma_list(mult):
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# Could be "0.7" or "0.5,0.6"
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if "," in mult:
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# expand over steps
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chunk_vals = [float(x.strip()) for x in mult.split(",")]
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expanded = expand_list_over_steps(chunk_vals, num_inference_steps)
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final_multipliers.append(expanded)
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else:
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final_multipliers.append(float(mult))
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else:
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raise ValueError(f"Invalid LoRA multiplier: '{mult}'")
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# If fewer multipliers than chosen LoRAs => pad with 1.0
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needed = len(loras_choices) - len(final_multipliers)
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if needed > 0:
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final_multipliers += [1.0]*needed
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# Actually activate them
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offload.activate_loras(transformer, loras_choices, final_multipliers)
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def expand_list_over_steps(short_list, num_steps):
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"""
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If user gave (0.5, 0.8) for example, expand them over `num_steps`.
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The expansion is simply linear slice across steps.
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"""
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result = []
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inc = len(short_list) / float(num_steps)
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idxf = 0.0
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for _ in range(num_steps):
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value = short_list[int(idxf)]
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result.append(value)
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idxf += inc
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return result
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def download_models_if_needed(transformer_filename_i2v, text_encoder_filename, local_folder=DATA_DIR):
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"""
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Checks if all required WAN 2.1 i2v files exist locally under 'ckpts/'.
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If not, downloads them from a Hugging Face Hub repo.
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Adjust the 'repo_id' and needed files as appropriate.
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"""
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import os
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from pathlib import Path
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try:
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from huggingface_hub import hf_hub_download, snapshot_download
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except ImportError as e:
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raise ImportError(
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"huggingface_hub is required for automatic model download. "
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"Please install it via `pip install huggingface_hub`."
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) from e
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# Identify just the filename portion for each path
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def basename(path_str):
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return os.path.basename(path_str)
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repo_id = "DeepBeepMeep/Wan2.1"
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target_root = local_folder
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# You can customize this list as needed for i2v usage.
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# At minimum you need:
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# 1) The requested i2v transformer file
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# 2) The requested text encoder file
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# 3) VAE file
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# 4) The open-clip xlm-roberta-large weights
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#
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# If your i2v config references additional files, add them here.
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needed_files = [
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"Wan2.1_VAE.pth",
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"models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth",
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basename(text_encoder_filename),
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basename(transformer_filename_i2v),
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]
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# The original script also downloads an entire "xlm-roberta-large" folder
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# via snapshot_download. If you require that for your pipeline,
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# you can add it here, for example:
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subfolder_name = "xlm-roberta-large"
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if not Path(os.path.join(target_root, subfolder_name)).exists():
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snapshot_download(repo_id=repo_id, allow_patterns=subfolder_name + "/*", local_dir=target_root)
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for filename in needed_files:
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local_path = os.path.join(target_root, filename)
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if not os.path.isfile(local_path):
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print(f"File '{filename}' not found locally. Downloading from {repo_id} ...")
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hf_hub_download(
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repo_id=repo_id,
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filename=filename,
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local_dir=target_root
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)
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else:
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# Already present
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pass
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print("All required i2v files are present.")
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# --------------------------------------------------
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# ARGUMENT PARSER
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# --------------------------------------------------
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Image-to-Video inference using WAN 2.1 i2v"
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)
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# Model + Tools
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parser.add_argument(
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"--quantize-transformer",
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action="store_true",
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help="Use on-the-fly transformer quantization"
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)
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parser.add_argument(
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"--compile",
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action="store_true",
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help="Enable PyTorch 2.0 compile for the transformer"
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)
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parser.add_argument(
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"--attention",
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type=str,
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default="auto",
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help="Which attention to use: auto, sdpa, sage, sage2, flash"
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)
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parser.add_argument(
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"--profile",
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type=int,
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default=4,
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help="Memory usage profile number [1..5]; see original script or use 2 if you have low VRAM"
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)
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parser.add_argument(
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"--preload",
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type=int,
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default=0,
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help="Megabytes of the diffusion model to preload in VRAM (only used in some profiles)"
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)
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parser.add_argument(
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"--verbose",
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type=int,
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default=1,
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help="Verbosity level [0..5]"
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)
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# i2v Model
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parser.add_argument(
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"--transformer-file",
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type=str,
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default=f"{DATA_DIR}/wan2.1_image2video_480p_14B_quanto_int8.safetensors",
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help="Which i2v model to load"
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)
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parser.add_argument(
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"--text-encoder-file",
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type=str,
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default=f"{DATA_DIR}/models_t5_umt5-xxl-enc-quanto_int8.safetensors",
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help="Which text encoder to use"
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)
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# LoRA
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parser.add_argument(
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"--lora-dir",
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type=str,
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default="",
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help="Path to a directory containing i2v LoRAs"
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)
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parser.add_argument(
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"--lora-preset",
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type=str,
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default="",
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help="A .lset preset name in the lora_dir to auto-apply"
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)
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# Generation Options
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parser.add_argument("--prompt", type=str, default=None, required=True, help="Prompt for generation")
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parser.add_argument("--negative-prompt", type=str, default="", help="Negative prompt")
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parser.add_argument("--resolution", type=str, default="832x480", help="WxH")
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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.")
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parser.add_argument("--steps", type=int, default=30, help="Number of denoising steps.")
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parser.add_argument("--guidance-scale", type=float, default=5.0, help="Classifier-free guidance scale")
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parser.add_argument("--flow-shift", type=float, default=3.0, help="Flow shift parameter. Generally 3.0 for 480p, 5.0 for 720p.")
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parser.add_argument("--riflex", action="store_true", help="Enable RIFLEx for longer videos")
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parser.add_argument("--teacache", type=float, default=0.25, help="TeaCache multiplier, e.g. 0.5, 2.0, etc.")
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parser.add_argument("--teacache-start", type=float, default=0.1, help="Teacache start step percentage [0..100]")
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parser.add_argument("--seed", type=int, default=-1, help="Random seed. -1 means random each time.")
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parser.add_argument("--slg-layers", type=str, default=None, help="Which layers to use for skip layer guidance")
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parser.add_argument("--slg-start", type=float, default=0.0, help="Percentage in to start SLG")
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parser.add_argument("--slg-end", type=float, default=1.0, help="Percentage in to end SLG")
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# LoRA usage
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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.")
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parser.add_argument("--loras-mult", type=str, default="", help="Multipliers for each chosen LoRA. Example: '1.0 1.2,1.3' etc.")
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# Input
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parser.add_argument(
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"--input-image",
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type=str,
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default=None,
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required=True,
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help="Path to an input image (or multiple)."
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)
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parser.add_argument(
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"--output-file",
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type=str,
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default="output.mp4",
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help="Where to save the resulting video."
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)
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return parser.parse_args()
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# --------------------------------------------------
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# MAIN
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# --------------------------------------------------
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def main():
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args = parse_args()
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# Setup environment
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offload.default_verboseLevel = args.verbose
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installed_attn_modes = get_attention_modes()
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||||
|
||||
# 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()
|
||||
@ -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)
|
||||
|
||||
noise_pred = self.transformer(
|
||||
hidden_states=latents,
|
||||
timestep=timestep / 1000,
|
||||
guidance=guidance,
|
||||
encoder_hidden_states_mask=prompt_embeds_mask,
|
||||
encoder_hidden_states=prompt_embeds,
|
||||
img_shapes=img_shapes,
|
||||
txt_seq_lens=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(
|
||||
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=negative_prompt_embeds_mask,
|
||||
encoder_hidden_states=negative_prompt_embeds,
|
||||
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=negative_prompt_embeds_mask.sum(dim=1).tolist(),
|
||||
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_list=[prompt_embeds_mask],
|
||||
encoder_hidden_states_list=[prompt_embeds],
|
||||
img_shapes=img_shapes,
|
||||
txt_seq_lens_list=[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 noise_pred == None: return None
|
||||
|
||||
if do_true_cfg:
|
||||
neg_noise_pred = self.transformer(
|
||||
hidden_states=latents,
|
||||
timestep=timestep / 1000,
|
||||
guidance=guidance,
|
||||
encoder_hidden_states_mask_list=[negative_prompt_embeds_mask],
|
||||
encoder_hidden_states_list=[negative_prompt_embeds],
|
||||
img_shapes=img_shapes,
|
||||
txt_seq_lens_list=[negative_prompt_embeds_mask.sum(dim=1).tolist()],
|
||||
attention_kwargs=self.attention_kwargs,
|
||||
**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]
|
||||
|
||||
@ -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({
|
||||
|
||||
@ -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
|
||||
@ -94,7 +96,7 @@ class model_factory():
|
||||
"4:3": (1472, 1140),
|
||||
"3:4": (1140, 1472)
|
||||
}
|
||||
|
||||
|
||||
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]
|
||||
@ -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
|
||||
|
||||
@ -512,53 +512,30 @@ 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).
|
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return_dict (`bool`, *optional*, defaults to `True`):
|
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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()
|
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lora_scale = attention_kwargs.pop("scale", 1.0)
|
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else:
|
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lora_scale = 1.0
|
||||
|
||||
|
||||
hidden_states = self.img_in(hidden_states)
|
||||
|
||||
timestep = timestep.to(hidden_states.dtype)
|
||||
encoder_hidden_states = self.txt_norm(encoder_hidden_states)
|
||||
encoder_hidden_states = self.txt_in(encoder_hidden_states)
|
||||
|
||||
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
|
||||
|
||||
@ -568,27 +545,30 @@ 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(
|
||||
hidden_states=hidden_states,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_hidden_states_mask=encoder_hidden_states_mask,
|
||||
temb=temb,
|
||||
image_rotary_emb=image_rotary_emb,
|
||||
joint_attention_kwargs=attention_kwargs,
|
||||
)
|
||||
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,
|
||||
temb=temb,
|
||||
image_rotary_emb=image_rotary_emb,
|
||||
joint_attention_kwargs=attention_kwargs,
|
||||
)
|
||||
|
||||
# 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
|
||||
|
||||
@ -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)
|
||||
|
||||
@ -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
2
wgp.py
@ -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
|
||||
|
||||
Loading…
Reference in New Issue
Block a user