Merge pull request #27 from ModelTC/dev_convert

Support convert weight to diffusers.

lightx2v/utils/quant_utils.py

 import torch
 from qtorch.quant import float_quantize
+from loguru import logger
 
 
 class BaseQuantizer(object):
@@ -168,23 +169,23 @@ if __name__ == "__main__":
     weight = torch.randn(4096, 4096, dtype=torch.bfloat16).cuda()
     quantizer = IntegerQuantizer(4, False, "per_group", group_size=128)
     q_weight = quantizer.fake_quant_tensor(weight)
-    print(weight)
-    print(q_weight)
-    print(f"cosine = {torch.cosine_similarity(weight.view(1, -1).to(torch.float64), q_weight.view(1, -1).to(torch.float64))}")
+    logger.info(weight)
+    logger.info(q_weight)
+    logger.info(f"cosine = {torch.cosine_similarity(weight.view(1, -1).to(torch.float64), q_weight.view(1, -1).to(torch.float64))}")
 
     realq_weight, scales, zeros = quantizer.real_quant_tensor(weight)
-    print(f"realq_weight = {realq_weight}, {realq_weight.shape}")
-    print(f"scales = {scales}, {scales.shape}")
-    print(f"zeros = {zeros}, {zeros.shape}")
+    logger.info(f"realq_weight = {realq_weight}, {realq_weight.shape}")
+    logger.info(f"scales = {scales}, {scales.shape}")
+    logger.info(f"zeros = {zeros}, {zeros.shape}")
 
     weight = torch.randn(8192, 4096, dtype=torch.bfloat16).cuda()
     quantizer = FloatQuantizer("e4m3", True, "per_channel")
     q_weight = quantizer.fake_quant_tensor(weight)
-    print(weight)
-    print(q_weight)
-    print(f"cosine = {torch.cosine_similarity(weight.view(1, -1).to(torch.float64), q_weight.view(1, -1).to(torch.float64))}")
+    logger.info(weight)
+    logger.info(q_weight)
+    logger.info(f"cosine = {torch.cosine_similarity(weight.view(1, -1).to(torch.float64), q_weight.view(1, -1).to(torch.float64))}")
 
     realq_weight, scales, zeros = quantizer.real_quant_tensor(weight)
-    print(f"realq_weight = {realq_weight}, {realq_weight.shape}")
-    print(f"scales = {scales}, {scales.shape}")
-    print(f"zeros = {zeros}")
+    logger.info(f"realq_weight = {realq_weight}, {realq_weight.shape}")
+    logger.info(f"scales = {scales}, {scales.shape}")
+    logger.info(f"zeros = {zeros}")

lightx2v/utils/utils.py

 import os
 from einops import rearrange
+from loguru import logger
 
 import torch
 import torchvision
@@ -81,5 +82,5 @@ def cache_video(
             error = e
             continue
     else:
-        print(f"cache_video failed, error: {error}", flush=True)
+        logger.info(f"cache_video failed, error: {error}", flush=True)
         return None

scripts/post.py

 import requests
+from loguru import logger
 
 
 url = "http://localhost:8000/v1/local/video/generate"
@@ -10,8 +11,8 @@ message = {
     "save_video_path": "./output_lightx2v_wan_t2v_ap4.mp4",  # It is best to set it to an absolute path.
 }
 
-print(f"message: {message}")
+logger.info(f"message: {message}")
 
 response = requests.post(url, json=message)
 
-print(f"response: {response.json()}")
+logger.info(f"response: {response.json()}")