Update app.py

app.py

@@ -4,8 +4,6 @@ import numpy as np
 import tempfile
 import os
 from torchvision import transforms
-from safetensors import safe_open
-from diffusers.models.autoencoders import AutoencoderKLLTXVideo
 
 from diffusers import LTXLatentUpsamplePipeline
 #from pipeline_ltx_condition_control import LTXConditionPipeline, LTXVideoCondition
@@ -19,13 +17,13 @@ import cv2
 import shutil
 import glob
 from pathlib import Path
-import json
+
 import warnings
 import logging
 warnings.filterwarnings("ignore", category=UserWarning)
 warnings.filterwarnings("ignore", category=FutureWarning)
 warnings.filterwarnings("ignore", message=".*")
-from huggingface_hub import hf_hub_download, logging as ll
+from huggingface_hub import logging as ll
 ll.set_verbosity_error()
 ll.set_verbosity_warning()
 ll.set_verbosity_info()
@@ -39,66 +37,14 @@ dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Carregamento das pipelines
-#pipeline = LTXConditionPipeline.from_pretrained(
-#    "Lightricks/LTX-Video-0.9.8-13B-distilled",
-#    offload_state_dict=False,
-#    torch_dtype=torch.bfloat16,
-#    cache_dir=os.getenv("HF_HOME_CACHE"),
-#    token=os.getenv("HF_TOKEN"),
-#)
-
-from huggingface_hub import hf_hub_download
-from safetensors.torch import load_file as safe_load
-
-# Baixa exatamente a variante desejada do repo oficial:
-weight_path = hf_hub_download(
-    repo_id="Lightricks/LTX-Video",
-    filename="ltxv-13b-0.9.8-distilled-fp8.safetensors",
-    revision=os.getenv("LTXV_REVISION", "8984fa25007f376c1a299016d0957a37a2f797bb")
+pipeline = LTXConditionPipeline.from_pretrained(
+    "Lightricks/LTX-Video-0.9.8-13B-distilled",
+    offload_state_dict=False,
+    torch_dtype=torch.bfloat16,
+    cache_dir=os.getenv("HF_HOME_CACHE"),
+    token=os.getenv("HF_TOKEN"),
 )
 
-
-
-
-
-if True:
-    if True:
-        with safe_open(weight_path, framework="pt") as f:
-            metadata = f.metadata() or {}
-            config_str = metadata.get("config", "{}")
-            configs = json.loads(config_str)
-            allowed_inference_steps = configs.get("allowed_inference_steps")
-
-        # 2. Carrega os Componentes Individuais (todos na CPU)
-        #    O `.from_pretrained(ckpt_path)` é inteligente e carrega os pesos corretos do arquivo .safetensors.
-        logging.info("Carregando VAE...")
-        #vae = AutoencoderKLLTXVideo.from_pretrained(weight_path).to("cpu")
-
-        logging.info("Carregando Transformer...")
-        #transformer = Transformer3DModel.from_pretrained(weight_path).to("cpu")
-
-        logging.info("Carregando Scheduler...")
-        #scheduler = RectifiedFlowScheduler.from_pretrained(weight_path)
-
-        logging.info("Carregando Text Encoder e Tokenizer...")
-        #text_encoder_path = "PixArt-alpha/PixArt-XL-2-1024-MS" #self.config["text_encoder_model_name_or_path"]
-        #text_encoder = T5EncoderModel.from_pretrained(text_encoder_path, subfolder="text_encoder").to("cpu")
-        #tokenizer = T5Tokenizer.from_pretrained(text_encoder_path, subfolder="tokenizer")
-
-        #patchifier = SymmetricPatchifier(patch_size=1)
-
-        # 3. Define a precisão dos modelos (ainda na CPU, será aplicado na GPU depois)
-         
-        # 4. Monta o objeto do Pipeline com os componentes carregados
-        
-        pipeline = LTXConditionPipeline.from_pretrained(weight_path, cache_dir=os.getenv("HF_HOME_CACHE"), torch_dtype=dtype)
-
-
-# Carrega o state_dict e aplica no transformer já criado pelo model_index:
-state = safe_load(weight_path)
-pipeline.transformer.load_state_dict(state, strict=True)
-
-
 pipe_upsample = LTXLatentUpsamplePipeline.from_pretrained(
     "Lightricks/ltxv-spatial-upscaler-0.9.7",
     cache_dir=os.getenv("HF_HOME_CACHE"),