' instead of "

hyvideo/constants.py

@@ -1,90 +1,90 @@
-import os
-import torch
-
-__all__ = [
-    "C_SCALE",
-    "PROMPT_TEMPLATE",
-    "MODEL_BASE",
-    "PRECISIONS",
-    "NORMALIZATION_TYPE",
-    "ACTIVATION_TYPE",
-    "VAE_PATH",
-    "TEXT_ENCODER_PATH",
-    "TOKENIZER_PATH",
-    "TEXT_PROJECTION",
-    "DATA_TYPE",
-    "NEGATIVE_PROMPT",
-]
-
-PRECISION_TO_TYPE = {
-    'fp32': torch.float32,
-    'fp16': torch.float16,
-    'bf16': torch.bfloat16,
-}
-
-# =================== Constant Values =====================
-# Computation scale factor, 1P = 1_000_000_000_000_000. Tensorboard will display the value in PetaFLOPS to avoid
-# overflow error when tensorboard logging values.
-C_SCALE = 1_000_000_000_000_000
-
-# When using decoder-only models, we must provide a prompt template to instruct the text encoder
-# on how to generate the text.
-# --------------------------------------------------------------------
-PROMPT_TEMPLATE_ENCODE = (
-    "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, "
-    "quantity, text, spatial relationships of the objects and background:<|eot_id|>"
-    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
-) 
-PROMPT_TEMPLATE_ENCODE_VIDEO = (
-    "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "
-    "1. The main content and theme of the video."
-    "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
-    "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
-    "4. background environment, light, style and atmosphere."
-    "5. camera angles, movements, and transitions used in the video:<|eot_id|>"
-    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
-)  
-
-NEGATIVE_PROMPT = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion"
-
-PROMPT_TEMPLATE = {
-    "dit-llm-encode": {
-        "template": PROMPT_TEMPLATE_ENCODE,
-        "crop_start": 36,
-    },
-    "dit-llm-encode-video": {
-        "template": PROMPT_TEMPLATE_ENCODE_VIDEO,
-        "crop_start": 95,
-    },
-}
-
-# ======================= Model ======================
-PRECISIONS = {"fp32", "fp16", "bf16"}
-NORMALIZATION_TYPE = {"layer", "rms"}
-ACTIVATION_TYPE = {"relu", "silu", "gelu", "gelu_tanh"}
-
-# =================== Model Path =====================
-MODEL_BASE = os.getenv("MODEL_BASE", "./ckpts")
-
-# =================== Data =======================
-DATA_TYPE = {"image", "video", "image_video"}
-
-# 3D VAE
-VAE_PATH = {"884-16c-hy": f"{MODEL_BASE}/hunyuan-video-t2v-720p/vae"}
-
-# Text Encoder
-TEXT_ENCODER_PATH = {
-    "clipL": f"{MODEL_BASE}/text_encoder_2",
-    "llm": f"{MODEL_BASE}/text_encoder",
-}
-
-# Tokenizer
-TOKENIZER_PATH = {
-    "clipL": f"{MODEL_BASE}/text_encoder_2",
-    "llm": f"{MODEL_BASE}/text_encoder",
-}
-
-TEXT_PROJECTION = {
-    "linear",  # Default, an nn.Linear() layer
-    "single_refiner",  # Single TokenRefiner. Refer to LI-DiT
-}
+import os
+import torch
+
+__all__ = [
+    "C_SCALE",
+    "PROMPT_TEMPLATE",
+    "MODEL_BASE",
+    "PRECISIONS",
+    "NORMALIZATION_TYPE",
+    "ACTIVATION_TYPE",
+    "VAE_PATH",
+    "TEXT_ENCODER_PATH",
+    "TOKENIZER_PATH",
+    "TEXT_PROJECTION",
+    "DATA_TYPE",
+    "NEGATIVE_PROMPT",
+]
+
+PRECISION_TO_TYPE = {
+    "fp32": torch.float32,
+    "fp16": torch.float16,
+    "bf16": torch.bfloat16,
+}
+
+# =================== Constant Values =====================
+# Computation scale factor, 1P = 1_000_000_000_000_000. Tensorboard will display the value in PetaFLOPS to avoid
+# overflow error when tensorboard logging values.
+C_SCALE = 1_000_000_000_000_000
+
+# When using decoder-only models, we must provide a prompt template to instruct the text encoder
+# on how to generate the text.
+# --------------------------------------------------------------------
+PROMPT_TEMPLATE_ENCODE = (
+    "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, "
+    "quantity, text, spatial relationships of the objects and background:<|eot_id|>"
+    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
+) 
+PROMPT_TEMPLATE_ENCODE_VIDEO = (
+    "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "
+    "1. The main content and theme of the video."
+    "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
+    "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
+    "4. background environment, light, style and atmosphere."
+    "5. camera angles, movements, and transitions used in the video:<|eot_id|>"
+    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
+)  
+
+NEGATIVE_PROMPT = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion"
+
+PROMPT_TEMPLATE = {
+    "dit-llm-encode": {
+        "template": PROMPT_TEMPLATE_ENCODE,
+        "crop_start": 36,
+    },
+    "dit-llm-encode-video": {
+        "template": PROMPT_TEMPLATE_ENCODE_VIDEO,
+        "crop_start": 95,
+    },
+}
+
+# ======================= Model ======================
+PRECISIONS = {"fp32", "fp16", "bf16"}
+NORMALIZATION_TYPE = {"layer", "rms"}
+ACTIVATION_TYPE = {"relu", "silu", "gelu", "gelu_tanh"}
+
+# =================== Model Path =====================
+MODEL_BASE = os.getenv("MODEL_BASE", "./ckpts")
+
+# =================== Data =======================
+DATA_TYPE = {"image", "video", "image_video"}
+
+# 3D VAE
+VAE_PATH = {"884-16c-hy": f"{MODEL_BASE}/hunyuan-video-t2v-720p/vae"}
+
+# Text Encoder
+TEXT_ENCODER_PATH = {
+    "clipL": f"{MODEL_BASE}/text_encoder_2",
+    "llm": f"{MODEL_BASE}/text_encoder",
+}
+
+# Tokenizer
+TOKENIZER_PATH = {
+    "clipL": f"{MODEL_BASE}/text_encoder_2",
+    "llm": f"{MODEL_BASE}/text_encoder",
+}
+
+TEXT_PROJECTION = {
+    "linear",  # Default, an nn.Linear() layer
+    "single_refiner",  # Single TokenRefiner. Refer to LI-DiT
+}