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DeepBeepMeep
2025-03-02 04:05:49 +01:00
parent 6797c48002
commit 18940291d4
17 changed files with 1964 additions and 729 deletions
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35
gradio/t2v_14B_singleGPU.py
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@@ -46,7 +46,7 @@ def t2v_generation(txt2vid_prompt, resolution, sd_steps, guide_scale,
guide_scale=guide_scale,
n_prompt=n_prompt,
seed=seed,
offload_model=True)
offload_model=False)
cache_video(
tensor=video[None],
@@ -177,28 +177,39 @@ if __name__ == '__main__':
args = _parse_args()
print("Step1: Init prompt_expander...", end='', flush=True)
if args.prompt_extend_method == "dashscope":
prompt_expander = DashScopePromptExpander(
model_name=args.prompt_extend_model, is_vl=False)
elif args.prompt_extend_method == "local_qwen":
prompt_expander = QwenPromptExpander(
model_name=args.prompt_extend_model, is_vl=False, device=0)
else:
raise NotImplementedError(
f"Unsupport prompt_extend_method: {args.prompt_extend_method}")
print("done", flush=True)
prompt_expander = None
# if args.prompt_extend_method == "dashscope":
# prompt_expander = DashScopePromptExpander(
# model_name=args.prompt_extend_model, is_vl=False)
# elif args.prompt_extend_method == "local_qwen":
# prompt_expander = QwenPromptExpander(
# model_name=args.prompt_extend_model, is_vl=False, device=0)
# else:
# raise NotImplementedError(
# f"Unsupport prompt_extend_method: {args.prompt_extend_method}")
# print("done", flush=True)
from mmgp import offload
print("Step2: Init 14B t2v model...", end='', flush=True)
cfg = WAN_CONFIGS['t2v-14B']
# cfg = WAN_CONFIGS['t2v-1.3B']
wan_t2v = wan.WanT2V(
config=cfg,
checkpoint_dir=args.ckpt_dir,
checkpoint_dir="../ckpts",
device_id=0,
rank=0,
t5_fsdp=False,
dit_fsdp=False,
use_usp=False,
)
pipe = {"transformer": wan_t2v.model, "text_encoder" : wan_t2v.text_encoder.model, "vae": wan_t2v.vae.model } #
# offload.profile(pipe, profile_no=4, budgets = {"transformer":100, "*":3000}, verboseLevel=2, quantizeTransformer = False, compile = "transformer") #
offload.profile(pipe, profile_no=4, budgets = {"transformer":100, "*":3000}, verboseLevel=2, quantizeTransformer = False) #
# offload.profile(pipe, profile_no=4, budgets = {"transformer":3000, "*":3000}, verboseLevel=2, quantizeTransformer = False)
print("done", flush=True)
demo = gradio_interface()