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docs/gradio/Progress.md

@@ -0,0 +1,115 @@
+Progress
+========
+
+    gradio.Progress(···)
+
+### Description[![](https://raw.githubusercontent.com/gradio-app/gradio/main/js/_website/src/lib/assets/img/anchor.svg)](#description)
+
+The Progress class provides a custom progress tracker that is used in a function signature. To attach a Progress tracker to a function, simply add a parameter right after the input parameters that has a default value set to a `gradio.Progress()` instance. The Progress tracker can then be updated in the function by calling the Progress object or using the `tqdm` method on an Iterable.
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+### Example Usage[![](https://raw.githubusercontent.com/gradio-app/gradio/main/js/_website/src/lib/assets/img/anchor.svg)](#example-usage)
+
+    import gradio as gr
+    import time
+    def my_function(x, progress=gr.Progress()):
+        progress(0, desc="Starting...")
+        time.sleep(1)
+        for i in progress.tqdm(range(100)):
+            time.sleep(0.1)
+        return x
+    gr.Interface(my_function, gr.Textbox(), gr.Textbox()).queue().launch()
+
+### Initialization[![](https://raw.githubusercontent.com/gradio-app/gradio/main/js/_website/src/lib/assets/img/anchor.svg)](#initialization)
+
+Parameters ▼
+
+[🔗](#param-progress-track-tqdm)
+
+    track_tqdm: bool
+
+default `= False`
+
+If True, the Progress object will track any tqdm.tqdm iterations with the tqdm library in the function.
+
+### Methods[![](https://raw.githubusercontent.com/gradio-app/gradio/main/js/_website/src/lib/assets/img/anchor.svg)](#methods)
+
+### \_\_call\_\_ [![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20(Commercial%20License)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)](#progress-__call__)
+
+    gradio.Progress.__call__(progress, ···)
+
+#### Description [![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20(Commercial%20License)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)](#__call__-description)
+
+Updates progress tracker with progress and message text.
+
+Parameters ▼
+
+[🔗](#param-__call__-progress)
+
+    progress: float | tuple[int, int | None] | None
+
+If float, should be between 0 and 1 representing completion. If Tuple, first number represents steps completed, and second value represents total steps or None if unknown. If None, hides progress bar.
+
+[🔗](#param-__call__-desc)
+
+    desc: str | None
+
+default `= None`
+
+description to display.
+
+[🔗](#param-__call__-total)
+
+    total: int | float | None
+
+default `= None`
+
+estimated total number of steps.
+
+[🔗](#param-__call__-unit)
+
+    unit: str
+
+default `= "steps"`
+
+unit of iterations.
+
+### tqdm [![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20(Commercial%20License)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)](#progress-tqdm)
+
+    gradio.Progress.tqdm(iterable, ···)
+
+#### Description [![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20(Commercial%20License)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)](#tqdm-description)
+
+Attaches progress tracker to iterable, like tqdm.
+
+Parameters ▼
+
+[🔗](#param-tqdm-iterable)
+
+    iterable: Iterable | None
+
+iterable to attach progress tracker to.
+
+[🔗](#param-tqdm-desc)
+
+    desc: str | None
+
+default `= None`
+
+description to display.
+
+[🔗](#param-tqdm-total)
+
+    total: int | float | None
+
+default `= None`
+
+estimated total number of steps.
+
+[🔗](#param-tqdm-unit)
+
+    unit: str
+
+default `= "steps"`
+
+unit of iterations.

vms/config.py

@@ -318,17 +318,17 @@ SD_16_9_H = 576  # 8*72
 SD_9_16_W = 576  # 8*72
 SD_9_16_H = 1024 # 8*128
 
-# MD (720p)
-MD_16_9_W = 1280 # 8*160
-MD_16_9_H = 720  # 8*90
-MD_9_16_W = 720  # 8*90
-MD_9_16_H = 1280 # 8*160
-
-# HD (1080p)
-HD_16_9_W = 1920 # 8*240
-HD_16_9_H = 1080 # 8*135
-HD_9_16_W = 1080 # 8*135
-HD_9_16_H = 1920 # 8*240
+# HD (720p)
+HD_16_9_W = 1280 # 8*160
+HD_16_9_H = 720  # 8*90
+HD_9_16_W = 720  # 8*90
+HD_9_16_H = 1280 # 8*160
+
+# FHD (1080p)
+FHD_16_9_W = 1920 # 8*240
+FHD_16_9_H = 1080 # 8*135
+FHD_9_16_W = 1080 # 8*135
+FHD_9_16_H = 1920 # 8*240
 
 # QHD (2K)
 QHD_16_9_W = 2160 # 8*270
@@ -383,6 +383,11 @@ NB_FRAMES_353 = 8 * 44 + 1  # 352 + 1
 NB_FRAMES_369 = 8 * 46 + 1  # 368 + 1
 NB_FRAMES_385 = 8 * 48 + 1  # 384 + 1
 NB_FRAMES_401 = 8 * 50 + 1  # 400 + 1
+NB_FRAMES_417 = 8 * 52 + 1  # 416 + 1
+NB_FRAMES_433 = 8 * 54 + 1  # 432 + 1
+NB_FRAMES_449 = 8 * 56 + 1  # 448 + 1
+NB_FRAMES_465 = 8 * 58 + 1  # 464 + 1
+NB_FRAMES_481 = 8 * 60 + 1  # 480 + 1
 
 # ------ HOW BUCKETS WORK:----------
 # Basically, to train or fine-tune a video model with Finetrainers, we need to specify all the possible accepted videos lengths AND size combinations (buckets), in the form: (BUCKET_CONFIGURATION_1, BUCKET_CONFIGURATION_2, ..., BUCKET_CONFIGURATION_N)
@@ -424,39 +429,111 @@ SD_TRAINING_BUCKETS = [
     (NB_FRAMES_257, SD_16_9_H, SD_16_9_W), # 256 + 1
     (NB_FRAMES_265, SD_16_9_H, SD_16_9_W), # 264 + 1
     (NB_FRAMES_273, SD_16_9_H, SD_16_9_W), # 272 + 1
+    (NB_FRAMES_289, SD_16_9_H, SD_16_9_W), # 288 + 1
+    (NB_FRAMES_305, SD_16_9_H, SD_16_9_W), # 304 + 1
+    (NB_FRAMES_321, SD_16_9_H, SD_16_9_W), # 320 + 1
+    (NB_FRAMES_337, SD_16_9_H, SD_16_9_W), # 336 + 1
+    (NB_FRAMES_353, SD_16_9_H, SD_16_9_W), # 352 + 1
+    (NB_FRAMES_369, SD_16_9_H, SD_16_9_W), # 368 + 1
+    (NB_FRAMES_385, SD_16_9_H, SD_16_9_W), # 384 + 1
+    (NB_FRAMES_401, SD_16_9_H, SD_16_9_W), # 400 + 1
+    (NB_FRAMES_417, SD_16_9_H, SD_16_9_W), # 416 + 1
+    (NB_FRAMES_433, SD_16_9_H, SD_16_9_W), # 432 + 1
+    (NB_FRAMES_449, SD_16_9_H, SD_16_9_W), # 448 + 1
+    (NB_FRAMES_465, SD_16_9_H, SD_16_9_W), # 464 + 1
+    (NB_FRAMES_481, SD_16_9_H, SD_16_9_W), # 480 + 1
 ]
 
 # For 1280x720 images and videos (from 1 frame up to 272)
-MD_TRAINING_BUCKETS = [
-    (NB_FRAMES_1,   MD_16_9_H, MD_16_9_W), # 1
-    (NB_FRAMES_9,   MD_16_9_H, MD_16_9_W), # 8 + 1
-    (NB_FRAMES_17,  MD_16_9_H, MD_16_9_W), # 16 + 1
-    (NB_FRAMES_33,  MD_16_9_H, MD_16_9_W), # 32 + 1
-    (NB_FRAMES_49,  MD_16_9_H, MD_16_9_W), # 48 + 1
-    (NB_FRAMES_65,  MD_16_9_H, MD_16_9_W), # 64 + 1
-    (NB_FRAMES_73,  MD_16_9_H, MD_16_9_W), # 72 + 1
-    (NB_FRAMES_81,  MD_16_9_H, MD_16_9_W), # 80 + 1
-    (NB_FRAMES_89,  MD_16_9_H, MD_16_9_W), # 88 + 1
-    (NB_FRAMES_97,  MD_16_9_H, MD_16_9_W), # 96 + 1
-    (NB_FRAMES_105, MD_16_9_H, MD_16_9_W), # 104 + 1
-    (NB_FRAMES_113, MD_16_9_H, MD_16_9_W), # 112 + 1
-    (NB_FRAMES_121, MD_16_9_H, MD_16_9_W), # 121 + 1
-    (NB_FRAMES_129, MD_16_9_H, MD_16_9_W), # 128 + 1
-    (NB_FRAMES_137, MD_16_9_H, MD_16_9_W), # 136 + 1
-    (NB_FRAMES_145, MD_16_9_H, MD_16_9_W), # 144 + 1
-    (NB_FRAMES_161, MD_16_9_H, MD_16_9_W), # 160 + 1
-    (NB_FRAMES_177, MD_16_9_H, MD_16_9_W), # 176 + 1
-    (NB_FRAMES_193, MD_16_9_H, MD_16_9_W), # 192 + 1
-    (NB_FRAMES_201, MD_16_9_H, MD_16_9_W), # 200 + 1
-    (NB_FRAMES_209, MD_16_9_H, MD_16_9_W), # 208 + 1
-    (NB_FRAMES_217, MD_16_9_H, MD_16_9_W), # 216 + 1
-    (NB_FRAMES_225, MD_16_9_H, MD_16_9_W), # 224 + 1
-    (NB_FRAMES_233, MD_16_9_H, MD_16_9_W), # 232 + 1
-    (NB_FRAMES_241, MD_16_9_H, MD_16_9_W), # 240 + 1
-    (NB_FRAMES_249, MD_16_9_H, MD_16_9_W), # 248 + 1
-    (NB_FRAMES_257, MD_16_9_H, MD_16_9_W), # 256 + 1
-    (NB_FRAMES_265, MD_16_9_H, MD_16_9_W), # 264 + 1
-    (NB_FRAMES_273, MD_16_9_H, MD_16_9_W), # 272 + 1
+HD_TRAINING_BUCKETS = [
+    (NB_FRAMES_1,   HD_16_9_H, HD_16_9_W), # 1
+    (NB_FRAMES_9,   HD_16_9_H, HD_16_9_W), # 8 + 1
+    (NB_FRAMES_17,  HD_16_9_H, HD_16_9_W), # 16 + 1
+    (NB_FRAMES_33,  HD_16_9_H, HD_16_9_W), # 32 + 1
+    (NB_FRAMES_49,  HD_16_9_H, HD_16_9_W), # 48 + 1
+    (NB_FRAMES_65,  HD_16_9_H, HD_16_9_W), # 64 + 1
+    (NB_FRAMES_73,  HD_16_9_H, HD_16_9_W), # 72 + 1
+    (NB_FRAMES_81,  HD_16_9_H, HD_16_9_W), # 80 + 1
+    (NB_FRAMES_89,  HD_16_9_H, HD_16_9_W), # 88 + 1
+    (NB_FRAMES_97,  HD_16_9_H, HD_16_9_W), # 96 + 1
+    (NB_FRAMES_105, HD_16_9_H, HD_16_9_W), # 104 + 1
+    (NB_FRAMES_113, HD_16_9_H, HD_16_9_W), # 112 + 1
+    (NB_FRAMES_121, HD_16_9_H, HD_16_9_W), # 121 + 1
+    (NB_FRAMES_129, HD_16_9_H, HD_16_9_W), # 128 + 1
+    (NB_FRAMES_137, HD_16_9_H, HD_16_9_W), # 136 + 1
+    (NB_FRAMES_145, HD_16_9_H, HD_16_9_W), # 144 + 1
+    (NB_FRAMES_161, HD_16_9_H, HD_16_9_W), # 160 + 1
+    (NB_FRAMES_177, HD_16_9_H, HD_16_9_W), # 176 + 1
+    (NB_FRAMES_193, HD_16_9_H, HD_16_9_W), # 192 + 1
+    (NB_FRAMES_201, HD_16_9_H, HD_16_9_W), # 200 + 1
+    (NB_FRAMES_209, HD_16_9_H, HD_16_9_W), # 208 + 1
+    (NB_FRAMES_217, HD_16_9_H, HD_16_9_W), # 216 + 1
+    (NB_FRAMES_225, HD_16_9_H, HD_16_9_W), # 224 + 1
+    (NB_FRAMES_233, HD_16_9_H, HD_16_9_W), # 232 + 1
+    (NB_FRAMES_241, HD_16_9_H, HD_16_9_W), # 240 + 1
+    (NB_FRAMES_249, HD_16_9_H, HD_16_9_W), # 248 + 1
+    (NB_FRAMES_257, HD_16_9_H, HD_16_9_W), # 256 + 1
+    (NB_FRAMES_265, HD_16_9_H, HD_16_9_W), # 264 + 1
+    (NB_FRAMES_273, HD_16_9_H, HD_16_9_W), # 272 + 1
+    (NB_FRAMES_289, HD_16_9_H, HD_16_9_W), # 288 + 1
+    (NB_FRAMES_305, HD_16_9_H, HD_16_9_W), # 304 + 1
+    (NB_FRAMES_321, HD_16_9_H, HD_16_9_W), # 320 + 1
+    (NB_FRAMES_337, HD_16_9_H, HD_16_9_W), # 336 + 1
+    (NB_FRAMES_353, HD_16_9_H, HD_16_9_W), # 352 + 1
+    (NB_FRAMES_369, HD_16_9_H, HD_16_9_W), # 368 + 1
+    (NB_FRAMES_385, HD_16_9_H, HD_16_9_W), # 384 + 1
+    (NB_FRAMES_401, HD_16_9_H, HD_16_9_W), # 400 + 1
+    (NB_FRAMES_417, HD_16_9_H, HD_16_9_W), # 416 + 1
+    (NB_FRAMES_433, HD_16_9_H, HD_16_9_W), # 432 + 1
+    (NB_FRAMES_449, HD_16_9_H, HD_16_9_W), # 448 + 1
+    (NB_FRAMES_465, HD_16_9_H, HD_16_9_W), # 464 + 1
+    (NB_FRAMES_481, HD_16_9_H, HD_16_9_W), # 480 + 1
+]
+
+# For 1920x1080 images and videos (from 1 frame up to 272)
+FHD_TRAINING_BUCKETS = [
+    (NB_FRAMES_1,   FHD_16_9_H, FHD_16_9_W), # 1
+    (NB_FRAMES_9,   FHD_16_9_H, FHD_16_9_W), # 8 + 1
+    (NB_FRAMES_17,  FHD_16_9_H, FHD_16_9_W), # 16 + 1
+    (NB_FRAMES_33,  FHD_16_9_H, FHD_16_9_W), # 32 + 1
+    (NB_FRAMES_49,  FHD_16_9_H, FHD_16_9_W), # 48 + 1
+    (NB_FRAMES_65,  FHD_16_9_H, FHD_16_9_W), # 64 + 1
+    (NB_FRAMES_73,  FHD_16_9_H, FHD_16_9_W), # 72 + 1
+    (NB_FRAMES_81,  FHD_16_9_H, FHD_16_9_W), # 80 + 1
+    (NB_FRAMES_89,  FHD_16_9_H, FHD_16_9_W), # 88 + 1
+    (NB_FRAMES_97,  FHD_16_9_H, FHD_16_9_W), # 96 + 1
+    (NB_FRAMES_105, FHD_16_9_H, FHD_16_9_W), # 104 + 1
+    (NB_FRAMES_113, FHD_16_9_H, FHD_16_9_W), # 112 + 1
+    (NB_FRAMES_121, FHD_16_9_H, FHD_16_9_W), # 121 + 1
+    (NB_FRAMES_129, FHD_16_9_H, FHD_16_9_W), # 128 + 1
+    (NB_FRAMES_137, FHD_16_9_H, FHD_16_9_W), # 136 + 1
+    (NB_FRAMES_145, FHD_16_9_H, FHD_16_9_W), # 144 + 1
+    (NB_FRAMES_161, FHD_16_9_H, FHD_16_9_W), # 160 + 1
+    (NB_FRAMES_177, FHD_16_9_H, FHD_16_9_W), # 176 + 1
+    (NB_FRAMES_193, FHD_16_9_H, FHD_16_9_W), # 192 + 1
+    (NB_FRAMES_201, FHD_16_9_H, FHD_16_9_W), # 200 + 1
+    (NB_FRAMES_209, FHD_16_9_H, FHD_16_9_W), # 208 + 1
+    (NB_FRAMES_217, FHD_16_9_H, FHD_16_9_W), # 216 + 1
+    (NB_FRAMES_225, FHD_16_9_H, FHD_16_9_W), # 224 + 1
+    (NB_FRAMES_233, FHD_16_9_H, FHD_16_9_W), # 232 + 1
+    (NB_FRAMES_241, FHD_16_9_H, FHD_16_9_W), # 240 + 1
+    (NB_FRAMES_249, FHD_16_9_H, FHD_16_9_W), # 248 + 1
+    (NB_FRAMES_257, FHD_16_9_H, FHD_16_9_W), # 256 + 1
+    (NB_FRAMES_265, FHD_16_9_H, FHD_16_9_W), # 264 + 1
+    (NB_FRAMES_273, FHD_16_9_H, FHD_16_9_W), # 272 + 1
+    (NB_FRAMES_289, FHD_16_9_H, FHD_16_9_W), # 288 + 1
+    (NB_FRAMES_305, FHD_16_9_H, FHD_16_9_W), # 304 + 1
+    (NB_FRAMES_321, FHD_16_9_H, FHD_16_9_W), # 320 + 1
+    (NB_FRAMES_337, FHD_16_9_H, FHD_16_9_W), # 336 + 1
+    (NB_FRAMES_353, FHD_16_9_H, FHD_16_9_W), # 352 + 1
+    (NB_FRAMES_369, FHD_16_9_H, FHD_16_9_W), # 368 + 1
+    (NB_FRAMES_385, FHD_16_9_H, FHD_16_9_W), # 384 + 1
+    (NB_FRAMES_401, FHD_16_9_H, FHD_16_9_W), # 400 + 1
+    (NB_FRAMES_417, FHD_16_9_H, FHD_16_9_W), # 416 + 1
+    (NB_FRAMES_433, FHD_16_9_H, FHD_16_9_W), # 432 + 1
+    (NB_FRAMES_449, FHD_16_9_H, FHD_16_9_W), # 448 + 1
+    (NB_FRAMES_465, FHD_16_9_H, FHD_16_9_W), # 464 + 1
+    (NB_FRAMES_481, FHD_16_9_H, FHD_16_9_W), # 480 + 1
 ]
 
 
@@ -466,7 +543,8 @@ MD_TRAINING_BUCKETS = [
 # Resolution buckets for different models
 RESOLUTION_OPTIONS = {
     "SD (1024x576)": "SD_TRAINING_BUCKETS",
-    "HD (1280x720)": "MD_TRAINING_BUCKETS"
+    "HD (1280x720)": "HD_TRAINING_BUCKETS",
+    "FHD (1920x1080)": "FHD_TRAINING_BUCKETS"
 }
 
 # Default parameters for Hunyuan Video

vms/ui/app_ui.py

@@ -9,7 +9,7 @@ from typing import Any, Optional, Dict, List, Union, Tuple
 
 from vms.config import (
     STORAGE_PATH, VIDEOS_TO_SPLIT_PATH, STAGING_PATH,
-    MODEL_TYPES, SD_TRAINING_BUCKETS, MD_TRAINING_BUCKETS, TRAINING_TYPES, MODEL_VERSIONS,
+    MODEL_TYPES, SD_TRAINING_BUCKETS, HD_TRAINING_BUCKETS, FHD_TRAINING_BUCKETS, TRAINING_TYPES, MODEL_VERSIONS,
     RESOLUTION_OPTIONS,
     DEFAULT_NB_TRAINING_STEPS, DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS,
     DEFAULT_BATCH_SIZE, DEFAULT_CAPTION_DROPOUT_P,

vms/ui/models/tabs/training_tab.py

@@ -88,9 +88,8 @@ class TrainingTab(BaseTab):
                             gr.Markdown(model.model_display_name or "Unknown")
                         
                         with gr.Column(scale=2, min_width=20):
-                            progress_text = f"Step {model.current_step}/{model.total_steps}"
+                            progress_text = f"Step {model.current_step}/{model.total_steps} ({model.training_progress:.1f}%)"
                             gr.Markdown(progress_text)
-                            gr.Progress(value=model.training_progress/100)
                         
                         with gr.Column(scale=2, min_width=20):
                             with gr.Row():

vms/ui/project/services/training.py

@@ -22,7 +22,7 @@ from typing import Any, Optional, Dict, List, Union, Tuple
 from huggingface_hub import upload_folder, create_repo
 
 from vms.config import (
-    TrainingConfig, RESOLUTION_OPTIONS, SD_TRAINING_BUCKETS, MD_TRAINING_BUCKETS,
+    TrainingConfig, RESOLUTION_OPTIONS, SD_TRAINING_BUCKETS, HD_TRAINING_BUCKETS, FHD_TRAINING_BUCKETS,
     STORAGE_PATH, HF_API_TOKEN, 
     MODEL_TYPES, TRAINING_TYPES, MODEL_VERSIONS,
     DEFAULT_NB_TRAINING_STEPS, DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS,
@@ -659,8 +659,10 @@ class TrainingService:
             # Determine which buckets to use based on the selected resolution
             if training_buckets_name == "SD_TRAINING_BUCKETS":
                 training_buckets = SD_TRAINING_BUCKETS
-            elif training_buckets_name == "MD_TRAINING_BUCKETS":
-                training_buckets = MD_TRAINING_BUCKETS
+            elif training_buckets_name == "HD_TRAINING_BUCKETS":
+                training_buckets = HD_TRAINING_BUCKETS
+            elif training_buckets_name == "FHD_TRAINING_BUCKETS":
+                training_buckets = FHD_TRAINING_BUCKETS
             else:
                 training_buckets = SD_TRAINING_BUCKETS  # Default fallback
             

vms/ui/project/tabs/train_tab.py

@@ -13,7 +13,7 @@ from pathlib import Path
 from vms.utils import BaseTab
 from vms.config import (
     ASK_USER_TO_DUPLICATE_SPACE,
-    SD_TRAINING_BUCKETS, MD_TRAINING_BUCKETS,
+    SD_TRAINING_BUCKETS, HD_TRAINING_BUCKETS, FHD_TRAINING_BUCKETS,
     RESOLUTION_OPTIONS,
     TRAINING_TYPES, MODEL_TYPES, MODEL_VERSIONS,
     DEFAULT_NB_TRAINING_STEPS, DEFAULT_SAVE_CHECKPOINT_EVERY_N_STEPS,