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.gitattributes

@@ -111,3 +111,6 @@ ACE_plus/flashenv/lib/python3.10/site-packages/pip/_vendor/distlib/w64-arm.exe f
 ACE_plus/flashenv/lib/python3.10/site-packages/pip/_vendor/distlib/t64-arm.exe filter=lfs diff=lfs merge=lfs -text
 ACE_plus/flashenv/lib/python3.10/site-packages/pip/_vendor/distlib/t64.exe filter=lfs diff=lfs merge=lfs -text
 ACE_plus/flashenv/lib/python3.10/site-packages/pip/_vendor/distlib/w64.exe filter=lfs diff=lfs merge=lfs -text
+ACE_plus/flashenv/lib/python3.10/site-packages/pip/_vendor/rich/__pycache__/_emoji_codes.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+ACE_plus/flashenv/lib/python3.10/site-packages/pip/_vendor/pkg_resources/__pycache__/__init__.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+ACE_plus/flashenv/lib/python3.10/site-packages/pkg_resources/__pycache__/__init__.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text

ACE_plus/.github/workflows/publish.yml

@@ -0,0 +1,28 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+      - master
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'ali-vilab' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          submodules: true
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

ACE_plus/examples/examples.py

@@ -0,0 +1,233 @@
+all_examples = [
+            {
+                "input_image": None,
+                "input_mask": None,
+                "input_reference_image": "assets/samples/portrait/human_1.jpg",
+                "save_path": "examples/outputs/portrait_human_1.jpg",
+                "instruction": "Maintain the facial features, A girl is wearing a neat police uniform and sporting a badge. She is smiling with a friendly and confident demeanor. The background is blurred, featuring a cartoon logo.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 4194866942,
+                "repainting_scale": 1.0,
+                "task_type": "portrait",
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": None,
+                "input_mask": None,
+                "input_reference_image": "assets/samples/subject/subject_1.jpg",
+                "save_path": "examples/outputs/subject_subject_1.jpg",
+                "instruction": "Display the logo in a minimalist style printed in white on a matte black ceramic coffee mug, alongside a steaming cup of coffee on a cozy cafe table.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 2935362780,
+                "repainting_scale": 1.0,
+                "task_type": "subject",
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": "assets/samples/local/local_1.webp",
+                "input_mask":  "assets/samples/local/local_1_m.webp",
+                "input_reference_image": None,
+                "save_path": "examples/outputs/local_local_1.jpg",
+                "instruction": "By referencing the mask, restore a partial image from the doodle {image} that aligns with the textual explanation: \"1 white old owl\".",
+                "output_h": -1,
+                "output_w": -1,
+                "seed": 1159797084,
+                "repainting_scale": 0.5,
+                "task_type": "local_editing",
+                "edit_type": "contour_repainting"
+            },
+            {
+                "input_image": "assets/samples/application/photo_editing/1_1_edit.png",
+                "input_mask": "assets/samples/application/photo_editing/1_1_m.png",
+                "input_reference_image": "assets/samples/application/photo_editing/1_ref.png",
+                "save_path": "examples/outputs/photo_editing_1.jpg",
+                "instruction": "The item is put on the ground.",
+                "output_h": -1,
+                "output_w": -1,
+                "seed": 2072028954,
+                "repainting_scale": 1.0,
+                "task_type": "subject",
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": "assets/samples/application/logo_paste/1_1_edit.png",
+                "input_mask": "assets/samples/application/logo_paste/1_1_m.png",
+                "input_reference_image": "assets/samples/application/logo_paste/1_ref.png",
+                "save_path": "examples/outputs/logo_paste_1.jpg",
+                "instruction": "The logo is printed on the headphones.",
+                "output_h": -1,
+                "output_w": -1,
+                "seed": 934582264,
+                "repainting_scale": 1.0,
+                "task_type": "subject",
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": "assets/samples/application/movie_poster/1_1_edit.png",
+                "input_mask": "assets/samples/application/movie_poster/1_1_m.png",
+                "input_reference_image": "assets/samples/application/movie_poster/1_ref.png",
+                "save_path": "examples/outputs/movie_poster_1.jpg",
+                "instruction": "The man is facing the camera and is smiling.",
+                "output_h": -1,
+                "output_w": -1,
+                "seed": 988183236,
+                "repainting_scale": 1.0,
+                "task_type": "portrait",
+                "edit_type": "repainting"
+            }
+
+        ]
+
+fft_examples =  [
+            {
+                "input_image": None,
+                "input_mask": None,
+                "input_reference_image": "./assets/samples/portrait/human_1.jpg",
+                "save_path": "examples/outputs/portrait_human_1.jpg",
+                "instruction": "Maintain the facial features, A girl is wearing a neat police uniform and sporting a badge. She is smiling with a friendly and confident demeanor. The background is blurred, featuring a cartoon logo.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 10000000,
+                "repainting_scale": 1.0,
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": None,
+                "input_mask": None,
+                "input_reference_image": "./assets/samples/subject/subject_1.jpg",
+                "save_path": "examples/outputs/subject_subject_1.jpg",
+                "instruction": "Display the logo in a minimalist style printed in white on a matte black ceramic coffee mug, alongside a steaming cup of coffee on a cozy cafe table.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 10000000,
+                "repainting_scale": 1.0,
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": "./assets/samples/application/photo_editing/1_2_edit.jpg",
+                "input_mask": "./assets/samples/application/photo_editing/1_2_m.webp",
+                "input_reference_image": "./assets/samples/application/photo_editing/1_ref.png",
+                "save_path": "examples/outputs/photo_editing_1.jpg",
+                "instruction": "The item is put on the table.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 8006019,
+                "repainting_scale": 1.0,
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": "./assets/samples/application/logo_paste/1_1_edit.png",
+                "input_mask": "./assets/samples/application/logo_paste/1_1_m.png",
+                "input_reference_image": "assets/samples/application/logo_paste/1_ref.png",
+                "save_path": "examples/outputs/logo_paste_1.jpg",
+                "instruction": "The logo is printed on the headphones.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 934582264,
+                "repainting_scale": 1.0,
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": "./assets/samples/application/try_on/1_1_edit.png",
+                "input_mask": "./assets/samples/application/try_on/1_1_m.png",
+                "input_reference_image": "assets/samples/application/try_on/1_ref.png",
+                "save_path": "examples/outputs/try_on_1.jpg",
+                "instruction": "The woman dresses this skirt.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 934582264,
+                "repainting_scale": 1.0,
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": "./assets/samples/portrait/human_1.jpg",
+                "input_mask": "assets/samples/application/movie_poster/1_2_m.webp",
+                "input_reference_image": "assets/samples/application/movie_poster/1_ref.png",
+                "save_path": "examples/outputs/movie_poster_1.jpg",
+                "instruction": "{image}, the man faces the camera.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 3999647,
+                "repainting_scale": 1.0,
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": "./assets/samples/application/sr/sr_tiger.png",
+                "input_mask": "./assets/samples/application/sr/sr_tiger_m.webp",
+                "input_reference_image": None,
+                "save_path": "examples/outputs/mario_recolorizing_1.jpg",
+                "instruction": "{image} features a close-up of a young, furry tiger cub on a rock. The tiger, which appears to be quite young, has distinctive orange, "
+                               "black, and white striped fur, typical of tigers. The cub's eyes have a bright and curious expression, and its ears are perked up, "
+                               "indicating alertness. The cub seems to be in the act of climbing or resting on the rock. The background is a blurred grassland with trees, "
+                               "but the focus is on the cub, which is vividly colored while the rest of the image is in grayscale, drawing attention to the tiger's details."
+                               " The photo captures a moment in the wild, depicting the charming and tenacious nature of this young tiger,"
+                               " as well as its typical interaction with the environment.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 199999,
+                "repainting_scale": 0.0,
+                "edit_type": "no_preprocess"
+            },
+            {
+                "input_image": "./assets/samples/application/photo_editing/1_ref.png",
+                "input_mask": "./assets/samples/application/photo_editing/1_1_orm.webp",
+                "input_reference_image": None,
+                "save_path": "examples/outputs/mario_repainting_1.jpg",
+                "instruction": "a blue hand",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 63401,
+                "repainting_scale": 1.0,
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": "./assets/samples/application/photo_editing/1_ref.png",
+                "input_mask": "./assets/samples/application/photo_editing/1_1_rm.webp",
+                "input_reference_image": None,
+                "save_path": "examples/outputs/mario_repainting_2.jpg",
+                "instruction": "Mechanical  hands like a robot",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 59107,
+                "repainting_scale": 1.0,
+                "edit_type": "repainting"
+            },
+            {
+                "input_image": "./assets/samples/control/1_1.webp",
+                "input_mask": "./assets/samples/control/1_1_m.webp",
+                "input_reference_image": None,
+                "save_path": "examples/outputs/control_recolorizing.jpg",
+                "instruction": "{image} Beautiful female portrait, Robot with smooth White transparent carbon shell, rococo detailing, Natural lighting, Highly detailed, Cinematic, 4K.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 9652101,
+                "repainting_scale": 0.0,
+                "edit_type": "recolorizing"
+            },
+            {
+                "input_image": "./assets/samples/control/1_1.webp",
+                "input_mask": "./assets/samples/control/1_1_m.webp",
+                "input_reference_image": None,
+                "save_path": "examples/outputs/control_depth.jpg",
+                "instruction": "{image} Beautiful female portrait, Robot with smooth White transparent carbon shell, rococo detailing, Natural lighting, Highly detailed, Cinematic, 4K.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 14979476,
+                "repainting_scale": 0.0,
+                "edit_type": "depth_repainting"
+            },
+            {
+                "input_image": "./assets/samples/control/1_1.webp",
+                "input_mask": "./assets/samples/control/1_1_m.webp",
+                "input_reference_image": None,
+                "save_path": "examples/outputs/control_contour.jpg",
+                "instruction": "{image} Beautiful female portrait, Robot with smooth White transparent carbon shell, rococo detailing, Natural lighting, Highly detailed, Cinematic, 4K.",
+                "output_h": 1024,
+                "output_w": 1024,
+                "seed": 4227292472,
+                "repainting_scale": 0.0,
+                "edit_type": "contour_repainting"
+            }
+        ]

ACE_plus/examples/exp_example/20250704031317/checkpoints/ldm_step-100/README.md

@@ -0,0 +1,10 @@
+## Training procedure
+
+### Framework versions
+
+
+- SWIFT 3.4.0
+### Base model information
+
+
+- BaseModel Class LatentDiffusionACEPlus

ACE_plus/examples/exp_example/20250704031317/checkpoints/ldm_step-100/configuration.json

@@ -0,0 +1 @@
+{}

ACE_plus/examples/exp_example/20250704031317/std_log.txt

@@ -0,0 +1,581 @@
+scepter [INFO] 2025-07-04 03:13:19,003 [File: logger.py Function: init_logger at line 85]  Running task with log file: /home/Ubuntu/Downloads/Unmodel/ACE_plus/./examples/exp_example/20250704031317/std_log.txt
+scepter [WARNING] 2025-07-04 03:13:19,094 [File: import_utils.py Function: import_module at line 325]  ('DATASETS', 'ACEPlusDataset') not found in ast index file
+scepter [INFO] 2025-07-04 03:13:19,095 [File: ace_plus_dataset.py Function: read_data_list at line 151]  subject has 12 samples.
+scepter [INFO] 2025-07-04 03:13:19,096 [File: registry.py Function: __init__ at line 185]  Built dataloader with len 9223372036854775807
+scepter [WARNING] 2025-07-04 03:13:19,096 [File: import_utils.py Function: import_module at line 325]  ('DATASETS', 'ACEPlusDataset') not found in ast index file
+scepter [INFO] 2025-07-04 03:13:19,096 [File: ace_plus_dataset.py Function: read_data_list at line 151]  subject has 12 samples.
+scepter [INFO] 2025-07-04 03:13:19,096 [File: registry.py Function: __init__ at line 185]  Built dataloader with len 12
+scepter [INFO] 2025-07-04 03:14:34,962 [File: flux.py Function: load_pretrained_model at line 450]  Restored from /home/Ubuntu/Downloads/Unmodel/Reference_models/flux1-fill-dev.safetensors with 0 missing and 0 unexpected keys
+scepter [INFO] 2025-07-04 03:14:34,987 [File: ace_plus_ldm.py Function: construct_network at line 62]  all parameters:11.90B
+scepter [INFO] 2025-07-04 03:14:35,816 [File: ae_module.py Function: construct_model at line 76]  AE Module XFORMERS_IS_AVAILBLE : True
+scepter [INFO] 2025-07-04 03:14:36,509 [File: ae_kl.py Function: init_from_ckpt at line 400]  Restored from /home/Ubuntu/Downloads/Unmodel/Reference_models/ae.safetensors with 0 missing and 0 unexpected keys
+scepter [INFO] 2025-07-04 03:14:45,062 [File: diffusion_solver.py Function: add_tuner at line 788]  [('base_model.model.double_blocks.0.img_mod.lin.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.double_blocks.0.img_mod.lin.lora_B.0_SwiftLoRA.weight', torch.Size([18432, 64])), ('base_model.model.double_blocks.0.img_attn.qkv.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.double_blocks.0.img_attn.qkv.lora_B.0_SwiftLoRA.weight', torch.Size([9216, 64])), ('base_model.model.double_blocks.0.img_attn.proj.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.double_blocks.0.img_attn.proj.lora_B.0_SwiftLoRA.weight', torch.Size([3072, 64])), ('base_model.model.double_blocks.0.img_mlp.0.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.double_blocks.0.img_mlp.0.lora_B.0_SwiftLoRA.weight', torch.Size([12288, 64])), ('base_model.model.double_blocks.0.img_mlp.2.lora_A.0_SwiftLoRA.weight', torch.Size([64, 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('base_model.model.single_blocks.31.modulation.lin.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.31.modulation.lin.lora_B.0_SwiftLoRA.weight', torch.Size([9216, 64])), ('base_model.model.single_blocks.32.linear1.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.32.linear1.lora_B.0_SwiftLoRA.weight', torch.Size([21504, 64])), ('base_model.model.single_blocks.32.linear2.lora_A.0_SwiftLoRA.weight', torch.Size([64, 15360])), ('base_model.model.single_blocks.32.linear2.lora_B.0_SwiftLoRA.weight', torch.Size([3072, 64])), ('base_model.model.single_blocks.32.modulation.lin.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.32.modulation.lin.lora_B.0_SwiftLoRA.weight', torch.Size([9216, 64])), ('base_model.model.single_blocks.33.linear1.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.33.linear1.lora_B.0_SwiftLoRA.weight', torch.Size([21504, 64])), ('base_model.model.single_blocks.33.linear2.lora_A.0_SwiftLoRA.weight', torch.Size([64, 15360])), ('base_model.model.single_blocks.33.linear2.lora_B.0_SwiftLoRA.weight', torch.Size([3072, 64])), ('base_model.model.single_blocks.33.modulation.lin.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.33.modulation.lin.lora_B.0_SwiftLoRA.weight', torch.Size([9216, 64])), ('base_model.model.single_blocks.34.linear1.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.34.linear1.lora_B.0_SwiftLoRA.weight', torch.Size([21504, 64])), ('base_model.model.single_blocks.34.linear2.lora_A.0_SwiftLoRA.weight', torch.Size([64, 15360])), ('base_model.model.single_blocks.34.linear2.lora_B.0_SwiftLoRA.weight', torch.Size([3072, 64])), ('base_model.model.single_blocks.34.modulation.lin.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.34.modulation.lin.lora_B.0_SwiftLoRA.weight', torch.Size([9216, 64])), ('base_model.model.single_blocks.35.linear1.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.35.linear1.lora_B.0_SwiftLoRA.weight', torch.Size([21504, 64])), ('base_model.model.single_blocks.35.linear2.lora_A.0_SwiftLoRA.weight', torch.Size([64, 15360])), ('base_model.model.single_blocks.35.linear2.lora_B.0_SwiftLoRA.weight', torch.Size([3072, 64])), ('base_model.model.single_blocks.35.modulation.lin.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.35.modulation.lin.lora_B.0_SwiftLoRA.weight', torch.Size([9216, 64])), ('base_model.model.single_blocks.36.linear1.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.36.linear1.lora_B.0_SwiftLoRA.weight', torch.Size([21504, 64])), ('base_model.model.single_blocks.36.linear2.lora_A.0_SwiftLoRA.weight', torch.Size([64, 15360])), ('base_model.model.single_blocks.36.linear2.lora_B.0_SwiftLoRA.weight', torch.Size([3072, 64])), ('base_model.model.single_blocks.36.modulation.lin.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.36.modulation.lin.lora_B.0_SwiftLoRA.weight', torch.Size([9216, 64])), ('base_model.model.single_blocks.37.linear1.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.37.linear1.lora_B.0_SwiftLoRA.weight', torch.Size([21504, 64])), ('base_model.model.single_blocks.37.linear2.lora_A.0_SwiftLoRA.weight', torch.Size([64, 15360])), ('base_model.model.single_blocks.37.linear2.lora_B.0_SwiftLoRA.weight', torch.Size([3072, 64])), ('base_model.model.single_blocks.37.modulation.lin.lora_A.0_SwiftLoRA.weight', torch.Size([64, 3072])), ('base_model.model.single_blocks.37.modulation.lin.lora_B.0_SwiftLoRA.weight', torch.Size([9216, 64]))]
+scepter [INFO] 2025-07-04 03:14:45,092 [File: diffusion_solver.py Function: print_model_params_status at line 996]  Load trainable params 306315264 / 17178094051 = 1.78%, train part: {'model.double_blocks': 171835392, 'model.single_blocks': 134479872}.
+scepter [INFO] 2025-07-04 03:14:45,092 [File: diffusion_solver.py Function: print_model_params_status at line 1000]  Load frozen params 16871778787 / 17178094051 = 98.22%, frozen part: {'model': 11902587968, 'first_stage_model': 83819683, 'cond_stage_model': 4885371136}.
+scepter [INFO] 2025-07-04 03:14:54,914 [File: diffusion_solver.py Function: set_up at line 230]  SwiftModel(
+  (base_model): LatentDiffusionACEPlus LatentDiffusionACEPlus(
+    (model): FluxMRModiACEPlus FluxMRModiACEPlus(
+      (pe_embedder): EmbedND()
+      (img_in): Linear(in_features=448, out_features=3072, bias=True)
+      (time_in): MLPEmbedder(
+        (in_layer): Linear(in_features=256, out_features=3072, bias=True)
+        (silu): SiLU()
+        (out_layer): Linear(in_features=3072, out_features=3072, bias=True)
+      )
+      (vector_in): MLPEmbedder(
+        (in_layer): Linear(in_features=768, out_features=3072, bias=True)
+        (silu): SiLU()
+        (out_layer): Linear(in_features=3072, out_features=3072, bias=True)
+      )
+      (guidance_in): MLPEmbedder(
+        (in_layer): Linear(in_features=256, out_features=3072, bias=True)
+        (silu): SiLU()
+        (out_layer): Linear(in_features=3072, out_features=3072, bias=True)
+      )
+      (txt_in): Linear(in_features=4096, out_features=3072, bias=True)
+      (double_blocks): ModuleList(
+        (0-18): 19 x DoubleStreamBlock(
+          (img_mod): Modulation(
+            (lin): lora.Linear(
+              (base_layer): Linear(in_features=3072, out_features=18432, bias=True)
+              (lora_dropout): ModuleDict(
+                (0_SwiftLoRA): Identity()
+              )
+              (lora_A): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=64, out_features=18432, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+          )
+          (img_norm1): LayerNorm((3072,), eps=1e-06, elementwise_affine=False)
+          (img_attn): SelfAttention(
+            (qkv): lora.Linear(
+              (base_layer): Linear(in_features=3072, out_features=9216, bias=True)
+              (lora_dropout): ModuleDict(
+                (0_SwiftLoRA): Identity()
+              )
+              (lora_A): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=64, out_features=9216, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+            (norm): QKNorm(
+              (query_norm): RMSNorm()
+              (key_norm): RMSNorm()
+            )
+            (proj): lora.Linear(
+              (base_layer): Linear(in_features=3072, out_features=3072, bias=True)
+              (lora_dropout): ModuleDict(
+                (0_SwiftLoRA): Identity()
+              )
+              (lora_A): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=64, out_features=3072, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+          )
+          (img_norm2): LayerNorm((3072,), eps=1e-06, elementwise_affine=False)
+          (img_mlp): Sequential(
+            (0): lora.Linear(
+              (base_layer): Linear(in_features=3072, out_features=12288, bias=True)
+              (lora_dropout): ModuleDict(
+                (0_SwiftLoRA): Identity()
+              )
+              (lora_A): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=64, out_features=12288, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+            (1): GELU(approximate='tanh')
+            (2): lora.Linear(
+              (base_layer): Linear(in_features=12288, out_features=3072, bias=True)
+              (lora_dropout): ModuleDict(
+                (0_SwiftLoRA): Identity()
+              )
+              (lora_A): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=12288, out_features=64, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=64, out_features=3072, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+          )
+          (txt_mod): Modulation(
+            (lin): lora.Linear(
+              (base_layer): Linear(in_features=3072, out_features=18432, bias=True)
+              (lora_dropout): ModuleDict(
+                (0_SwiftLoRA): Identity()
+              )
+              (lora_A): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=64, out_features=18432, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+          )
+          (txt_norm1): LayerNorm((3072,), eps=1e-06, elementwise_affine=False)
+          (txt_attn): SelfAttention(
+            (qkv): lora.Linear(
+              (base_layer): Linear(in_features=3072, out_features=9216, bias=True)
+              (lora_dropout): ModuleDict(
+                (0_SwiftLoRA): Identity()
+              )
+              (lora_A): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=64, out_features=9216, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+            (norm): QKNorm(
+              (query_norm): RMSNorm()
+              (key_norm): RMSNorm()
+            )
+            (proj): lora.Linear(
+              (base_layer): Linear(in_features=3072, out_features=3072, bias=True)
+              (lora_dropout): ModuleDict(
+                (0_SwiftLoRA): Identity()
+              )
+              (lora_A): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=64, out_features=3072, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+          )
+          (txt_norm2): LayerNorm((3072,), eps=1e-06, elementwise_affine=False)
+          (txt_mlp): Sequential(
+            (0): lora.Linear(
+              (base_layer): Linear(in_features=3072, out_features=12288, bias=True)
+              (lora_dropout): ModuleDict(
+                (0_SwiftLoRA): Identity()
+              )
+              (lora_A): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=64, out_features=12288, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+            (1): GELU(approximate='tanh')
+            (2): lora.Linear(
+              (base_layer): Linear(in_features=12288, out_features=3072, bias=True)
+              (lora_dropout): ModuleDict(
+                (0_SwiftLoRA): Identity()
+              )
+              (lora_A): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=12288, out_features=64, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=64, out_features=3072, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+          )
+        )
+      )
+      (single_blocks): ModuleList(
+        (0-37): 38 x SingleStreamBlock(
+          (linear1): lora.Linear(
+            (base_layer): Linear(in_features=3072, out_features=21504, bias=True)
+            (lora_dropout): ModuleDict(
+              (0_SwiftLoRA): Identity()
+            )
+            (lora_A): ModuleDict(
+              (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (0_SwiftLoRA): Linear(in_features=64, out_features=21504, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+            (lora_magnitude_vector): ModuleDict()
+          )
+          (linear2): lora.Linear(
+            (base_layer): Linear(in_features=15360, out_features=3072, bias=True)
+            (lora_dropout): ModuleDict(
+              (0_SwiftLoRA): Identity()
+            )
+            (lora_A): ModuleDict(
+              (0_SwiftLoRA): Linear(in_features=15360, out_features=64, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (0_SwiftLoRA): Linear(in_features=64, out_features=3072, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+            (lora_magnitude_vector): ModuleDict()
+          )
+          (norm): QKNorm(
+            (query_norm): RMSNorm()
+            (key_norm): RMSNorm()
+          )
+          (pre_norm): LayerNorm((3072,), eps=1e-06, elementwise_affine=False)
+          (mlp_act): GELU(approximate='tanh')
+          (modulation): Modulation(
+            (lin): lora.Linear(
+              (base_layer): Linear(in_features=3072, out_features=9216, bias=True)
+              (lora_dropout): ModuleDict(
+                (0_SwiftLoRA): Identity()
+              )
+              (lora_A): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=3072, out_features=64, bias=False)
+              )
+              (lora_B): ModuleDict(
+                (0_SwiftLoRA): Linear(in_features=64, out_features=9216, bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+          )
+        )
+      )
+      (final_layer): LastLayer(
+        (norm_final): LayerNorm((3072,), eps=1e-06, elementwise_affine=False)
+        (linear): Linear(in_features=3072, out_features=64, bias=True)
+        (adaLN_modulation): Sequential(
+          (0): SiLU()
+          (1): Linear(in_features=3072, out_features=6144, bias=True)
+        )
+      )
+    )
+    (first_stage_model): AutoencoderKLFlux AutoencoderKLFlux(
+      (encoder): Encoder Encoder(
+        (conv_in): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        (down): ModuleList(
+          (0): Module(
+            (block): ModuleList(
+              (0-1): 2 x ResnetBlock(
+                (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
+                (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+                (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              )
+            )
+            (attn): ModuleList()
+            (downsample): Downsample(
+              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))
+            )
+          )
+          (1): Module(
+            (block): ModuleList(
+              (0): ResnetBlock(
+                (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
+                (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+                (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (nin_shortcut): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
+              )
+              (1): ResnetBlock(
+                (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
+                (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+                (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              )
+            )
+            (attn): ModuleList()
+            (downsample): Downsample(
+              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2))
+            )
+          )
+          (2): Module(
+            (block): ModuleList(
+              (0): ResnetBlock(
+                (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
+                (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+                (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (nin_shortcut): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))
+              )
+              (1): ResnetBlock(
+                (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
+                (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+                (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              )
+            )
+            (attn): ModuleList()
+            (downsample): Downsample(
+              (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2))
+            )
+          )
+          (3): Module(
+            (block): ModuleList(
+              (0-1): 2 x ResnetBlock(
+                (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
+                (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+                (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              )
+            )
+            (attn): ModuleList()
+          )
+        )
+        (mid): Module(
+          (block_1): ResnetBlock(
+            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
+            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
+            (dropout): Dropout(p=0.0, inplace=False)
+            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          )
+          (attn_1): MemoryEfficientAttention(
+            (norm): GroupNorm(32, 512, eps=1e-06, affine=True)
+            (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
+            (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
+            (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
+            (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (block_2): ResnetBlock(
+            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
+            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
+            (dropout): Dropout(p=0.0, inplace=False)
+            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          )
+        )
+        (norm_out): GroupNorm(32, 512, eps=1e-06, affine=True)
+        (conv_out): Conv2d(512, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+      )
+      (decoder): Decoder Decoder(
+        (conv_in): Conv2d(16, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        (mid): Module(
+          (block_1): ResnetBlock(
+            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
+            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
+            (dropout): Dropout(p=0.0, inplace=False)
+            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          )
+          (attn_1): MemoryEfficientAttention(
+            (norm): GroupNorm(32, 512, eps=1e-06, affine=True)
+            (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
+            (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
+            (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
+            (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
+          )
+          (block_2): ResnetBlock(
+            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
+            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
+            (dropout): Dropout(p=0.0, inplace=False)
+            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+          )
+        )
+        (up): ModuleList(
+          (0): Module(
+            (block): ModuleList(
+              (0): ResnetBlock(
+                (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
+                (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+                (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (nin_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
+              )
+              (1-2): 2 x ResnetBlock(
+                (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
+                (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+                (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              )
+            )
+            (attn): ModuleList()
+          )
+          (1): Module(
+            (block): ModuleList(
+              (0): ResnetBlock(
+                (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
+                (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+                (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (nin_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
+              )
+              (1-2): 2 x ResnetBlock(
+                (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
+                (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+                (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              )
+            )
+            (attn): ModuleList()
+            (upsample): Upsample(
+              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            )
+          )
+          (2-3): 2 x Module(
+            (block): ModuleList(
+              (0-2): 3 x ResnetBlock(
+                (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
+                (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+                (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
+                (dropout): Dropout(p=0.0, inplace=False)
+                (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+              )
+            )
+            (attn): ModuleList()
+            (upsample): Upsample(
+              (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+            )
+          )
+        )
+        (norm_out): GroupNorm(32, 128, eps=1e-06, affine=True)
+        (conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+      )
+      (conv1): Identity()
+      (conv2): Identity()
+    )
+    (cond_stage_model): T5ACEPlusClipFluxEmbedder T5ACEPlusClipFluxEmbedder(
+      (t5_model): ACEHFEmbedder ACEHFEmbedder(
+        (hf_module): T5EncoderModel(
+          (shared): Embedding(32128, 4096)
+          (encoder): T5Stack(
+            (embed_tokens): Embedding(32128, 4096)
+            (block): ModuleList(
+              (0): T5Block(
+                (layer): ModuleList(
+                  (0): T5LayerSelfAttention(
+                    (SelfAttention): T5Attention(
+                      (q): Linear(in_features=4096, out_features=4096, bias=False)
+                      (k): Linear(in_features=4096, out_features=4096, bias=False)
+                      (v): Linear(in_features=4096, out_features=4096, bias=False)
+                      (o): Linear(in_features=4096, out_features=4096, bias=False)
+                      (relative_attention_bias): Embedding(32, 64)
+                    )
+                    (layer_norm): T5LayerNorm()
+                    (dropout): Dropout(p=0.1, inplace=False)
+                  )
+                  (1): T5LayerFF(
+                    (DenseReluDense): T5DenseGatedActDense(
+                      (wi_0): Linear(in_features=4096, out_features=10240, bias=False)
+                      (wi_1): Linear(in_features=4096, out_features=10240, bias=False)
+                      (wo): Linear(in_features=10240, out_features=4096, bias=False)
+                      (dropout): Dropout(p=0.1, inplace=False)
+                      (act): NewGELUActivation()
+                    )
+                    (layer_norm): T5LayerNorm()
+                    (dropout): Dropout(p=0.1, inplace=False)
+                  )
+                )
+              )
+              (1-23): 23 x T5Block(
+                (layer): ModuleList(
+                  (0): T5LayerSelfAttention(
+                    (SelfAttention): T5Attention(
+                      (q): Linear(in_features=4096, out_features=4096, bias=False)
+                      (k): Linear(in_features=4096, out_features=4096, bias=False)
+                      (v): Linear(in_features=4096, out_features=4096, bias=False)
+                      (o): Linear(in_features=4096, out_features=4096, bias=False)
+                    )
+                    (layer_norm): T5LayerNorm()
+                    (dropout): Dropout(p=0.1, inplace=False)
+                  )
+                  (1): T5LayerFF(
+                    (DenseReluDense): T5DenseGatedActDense(
+                      (wi_0): Linear(in_features=4096, out_features=10240, bias=False)
+                      (wi_1): Linear(in_features=4096, out_features=10240, bias=False)
+                      (wo): Linear(in_features=10240, out_features=4096, bias=False)
+                      (dropout): Dropout(p=0.1, inplace=False)
+                      (act): NewGELUActivation()
+                    )
+                    (layer_norm): T5LayerNorm()
+                    (dropout): Dropout(p=0.1, inplace=False)
+                  )
+                )
+              )
+            )
+            (final_layer_norm): T5LayerNorm()
+            (dropout): Dropout(p=0.1, inplace=False)
+          )
+        )
+      )
+      (clip_model): ACEHFEmbedder ACEHFEmbedder(
+        (hf_module): CLIPTextModel(
+          (text_model): CLIPTextTransformer(
+            (embeddings): CLIPTextEmbeddings(
+              (token_embedding): Embedding(49408, 768)
+              (position_embedding): Embedding(77, 768)
+            )
+            (encoder): CLIPEncoder(
+              (layers): ModuleList(
+                (0-11): 12 x CLIPEncoderLayer(
+                  (self_attn): CLIPSdpaAttention(
+                    (k_proj): Linear(in_features=768, out_features=768, bias=True)
+                    (v_proj): Linear(in_features=768, out_features=768, bias=True)
+                    (q_proj): Linear(in_features=768, out_features=768, bias=True)
+                    (out_proj): Linear(in_features=768, out_features=768, bias=True)
+                  )
+                  (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                  (mlp): CLIPMLP(
+                    (activation_fn): QuickGELUActivation()
+                    (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                    (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                  )
+                  (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                )
+              )
+            )
+            (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+          )
+        )
+      )
+    )
+  )
+)
+scepter [INFO] 2025-07-04 03:14:54,956 [File: log.py Function: before_solve at line 260]  Tensorboard: save to ./examples/exp_example/20250704031317/tensorboard
+scepter [INFO] 2025-07-04 03:26:47,746 [File: log.py Function: _print_iter_log at line 71]  Stage [train] iter: [20/100000], data_time: 29.7654(29.7654), time: 35.6384(35.6384), loss: 0.1792(0.1792), throughput: 23616/day, all_throughput: 20, pg0_lr: 0.001000, scale: 1.000000, [13mins 28secs 0.02%(46days 18hours 54mins 17secs)]
+scepter [INFO] 2025-07-04 03:38:34,905 [File: log.py Function: _print_iter_log at line 71]  Stage [train] iter: [40/100000], data_time: 29.6163(29.6909), time: 35.3580(35.4982), loss: 0.1530(0.1661), throughput: 23655/day, all_throughput: 40, pg0_lr: 0.001000, scale: 1.000000, [25mins 15secs 0.04%(43days 20hours 13mins 35secs)]
+scepter [INFO] 2025-07-04 03:44:27,364 [File: checkpoint.py Function: after_iter at line 109]  Saving checkpoint after 50 steps
+scepter [INFO] 2025-07-04 03:50:16,375 [File: log.py Function: _print_iter_log at line 71]  Stage [train] iter: [60/100000], data_time: 29.3468(29.5762), time: 35.0735(35.3566), loss: 0.1463(0.1595), throughput: 23738/day, all_throughput: 60, pg0_lr: 0.001000, scale: 1.000000, [36mins 57secs 0.06%(42days 17hours 54mins 12secs)]
+scepter [INFO] 2025-07-04 04:02:09,647 [File: log.py Function: _print_iter_log at line 71]  Stage [train] iter: [80/100000], data_time: 29.9454(29.6685), time: 35.6637(35.4334), loss: 0.1684(0.1617), throughput: 23803/day, all_throughput: 80, pg0_lr: 0.001000, scale: 1.000000, [48mins 50secs 0.08%(42days 8hours 44mins 22secs)]
+scepter [INFO] 2025-07-04 04:14:04,519 [File: log.py Function: _print_iter_log at line 71]  Stage [train] iter: [100/100000], data_time: 29.8545(29.7057), time: 35.7436(35.4954), loss: 0.1803(0.1654), throughput: 23744/day, all_throughput: 100, pg0_lr: 0.001000, scale: 1.000000, [1hours 45secs 0.10%(42days 3hours 36mins 20secs)]
+scepter [INFO] 2025-07-04 04:14:04,521 [File: checkpoint.py Function: after_iter at line 109]  Saving checkpoint after 100 steps

ACE_plus/examples/exp_example/20250704031317/train.yaml

@@ -0,0 +1,288 @@
+ENV:
+  BACKEND: nccl
+  SEED: 1999
+SOLVER:
+  # NAME DESCRIPTION:  TYPE:  default: 'LatentUfitSolver'
+  NAME: FormalACEPlusSolver
+  # MAX_STEPS DESCRIPTION: The total steps for training. TYPE: int default: 100000
+  MAX_STEPS: 100000
+  # USE_AMP DESCRIPTION: Use amp to surpport mix precision or not, default is False. TYPE: bool default: False
+  USE_AMP: True
+  # DTYPE DESCRIPTION: The precision for training. TYPE: str default: 'float32'
+  DTYPE: bfloat16
+  ENABLE_GRADSCALER: False
+  # USE_FAIRSCALE DESCRIPTION: Use fairscale as the backend of ddp, default False. TYPE: bool default: False
+  USE_FAIRSCALE: False
+  USE_ORIG_PARAMS: True
+  USE_FSDP: True  # lora use ddp(USE_FSDP=False), else use fsdp(USE_FSDP=True)
+  # LOAD_MODEL_ONLY DESCRIPTION: Only load the model rather than the optimizer and schedule, default is False. TYPE: bool default: False
+  LOAD_MODEL_ONLY: False
+  # RESUME_FROM DESCRIPTION: Resume from some state of training! TYPE: str default: ''
+  RESUME_FROM:
+  # WORK_DIR DESCRIPTION: Save dir of the training log or model. TYPE: str default: ''
+  WORK_DIR: ./examples/exp_example/
+  # LOG_FILE DESCRIPTION: Save log path. TYPE: str default: ''
+  LOG_FILE: std_log.txt
+  # LOG_TRAIN_NUM DESCRIPTION: The number samples used to log in training phase. TYPE: int default: -1
+  LOG_TRAIN_NUM: 16
+  # FSDP_REDUCE_DTYPE DESCRIPTION: The dtype of reduce in FSDP. TYPE: str default: 'float16'
+  FSDP_REDUCE_DTYPE: float32
+  # FSDP_BUFFER_DTYPE DESCRIPTION: The dtype of buffer in FSDP. TYPE: str default: 'float16'
+  FSDP_BUFFER_DTYPE: float32
+  # FSDP_SHARD_MODULES DESCRIPTION: The modules to be sharded in FSDP. TYPE: list default: ['model']
+  FSDP_SHARD_MODULES:
+    - MODULE: 'model.model'
+      FSDP_GROUP: [ 'single_blocks', 'double_blocks']
+    - MODULE: 'cond_stage_model.t5_model.hf_module.encoder'
+      FSDP_GROUP: [ 'block' ] #
+  SAVE_MODULES: [ 'model'] #
+  TRAIN_MODULES: ['model']
+
+  #
+  FILE_SYSTEM:
+    - NAME: HuggingfaceFs
+      TEMP_DIR: ./cache
+    - NAME: ModelscopeFs
+      TEMP_DIR: ./cache
+  #
+  MODEL:
+    NAME: LatentDiffusionACEPlus
+    PARAMETERIZATION: rf
+    TIMESTEPS: 1000
+    GUIDE_SCALE: 1.0
+    PRETRAINED_MODEL:
+    IGNORE_KEYS: [ ]
+    USE_EMA: False
+    EVAL_EMA: False
+    SIZE_FACTOR: 8
+    DIFFUSION:
+      NAME: DiffusionFluxRF
+      PREDICTION_TYPE: raw
+      NOISE_NORM: True
+      # NOISE_SCHEDULER DESCRIPTION:  TYPE:  default: ''
+      NOISE_SCHEDULER:
+        NAME: FlowMatchFluxShiftScheduler
+        SHIFT: False
+        PRE_T_SAMPLE: True
+        PRE_T_SAMPLE_FOLD: 1
+        SIGMOID_SCALE: 1
+        BASE_SHIFT: 0.5
+        MAX_SHIFT: 1.15
+      SAMPLER_SCHEDULER:
+        NAME: FlowMatchFluxShiftScheduler
+        SHIFT: True
+        PRE_T_SAMPLE: False
+        SIGMOID_SCALE: 1
+        BASE_SHIFT: 0.5
+        MAX_SHIFT: 1.15
+
+    #
+    DIFFUSION_MODEL:
+      # NAME DESCRIPTION:  TYPE:  default: 'Flux'
+      NAME: FluxMRModiACEPlus
+      PRETRAINED_MODEL: /home/Ubuntu/Downloads/Unmodel/Reference_models/flux1-fill-dev.safetensors
+      # IN_CHANNELS DESCRIPTION: model's input channels. TYPE: int default: 64
+      IN_CHANNELS: 448
+      # OUT_CHANNELS DESCRIPTION: model's input channels. TYPE: int default: 64
+      OUT_CHANNELS: 64
+      # HIDDEN_SIZE DESCRIPTION: model's hidden size. TYPE: int default: 1024
+      HIDDEN_SIZE: 3072
+      REDUX_DIM: 1152
+      # NUM_HEADS DESCRIPTION: number of heads in the transformer. TYPE: int default: 16
+      NUM_HEADS: 24
+      # AXES_DIM DESCRIPTION: dimensions of the axes of the positional encoding. TYPE: list default: [16, 56, 56]
+      AXES_DIM: [ 16, 56, 56 ]
+      # THETA DESCRIPTION: theta for positional encoding. TYPE: int default: 10000
+      THETA: 10000
+      # VEC_IN_DIM DESCRIPTION: dimension of the vector input. TYPE: int default: 768
+      VEC_IN_DIM: 768
+      # GUIDANCE_EMBED DESCRIPTION: whether to use guidance embedding. TYPE: bool default: False
+      GUIDANCE_EMBED: True
+      # CONTEXT_IN_DIM DESCRIPTION: dimension of the context input. TYPE: int default: 4096
+      CONTEXT_IN_DIM: 4096
+      # MLP_RATIO DESCRIPTION: ratio of mlp hidden size to hidden size. TYPE: float default: 4.0
+      MLP_RATIO: 4.0
+      # QKV_BIAS DESCRIPTION: whether to use bias in qkv projection. TYPE: bool default: True
+      QKV_BIAS: True
+      # DEPTH DESCRIPTION: number of transformer blocks. TYPE: int default: 19
+      DEPTH: 19
+      # DEPTH_SINGLE_BLOCKS DESCRIPTION: number of transformer blocks in the single stream block. TYPE: int default: 38
+      DEPTH_SINGLE_BLOCKS: 38
+      # ATTN_BACKEND：setting 'flash_attn' to use flash_attn2, if the version of pytorch > 2.4.0, using 'pytorch' to use pytorch's implementation
+      ATTN_BACKEND: flash_attn
+      # USE_GRAD_CHECKPOINT: setting gc to true can decrease the memory usage.
+      USE_GRAD_CHECKPOINT: True
+
+    #
+    FIRST_STAGE_MODEL:
+      NAME: AutoencoderKLFlux
+      EMBED_DIM: 16
+      PRETRAINED_MODEL: /home/Ubuntu/Downloads/Unmodel/Reference_models/ae.safetensors
+      IGNORE_KEYS: [ ]
+      BATCH_SIZE: 8
+      USE_CONV: False
+      SCALE_FACTOR: 0.3611
+      SHIFT_FACTOR: 0.1159
+      #
+      ENCODER:
+        NAME: Encoder
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 16
+        DOUBLE_Z: True
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+      #
+      DECODER:
+        NAME: Decoder
+        CH: 128
+        OUT_CH: 3
+        NUM_RES_BLOCKS: 2
+        IN_CHANNELS: 3
+        ATTN_RESOLUTIONS: [ ]
+        CH_MULT: [ 1, 2, 4, 4 ]
+        Z_CHANNELS: 16
+        DROPOUT: 0.0
+        RESAMP_WITH_CONV: True
+        GIVE_PRE_END: False
+        TANH_OUT: False
+    #
+    COND_STAGE_MODEL:
+      # NAME DESCRIPTION:  TYPE:  default: 'T5PlusClipFluxEmbedder'
+      NAME: T5ACEPlusClipFluxEmbedder
+      # T5_MODEL DESCRIPTION:  TYPE:  default: ''
+      T5_MODEL:
+        # NAME DESCRIPTION:  TYPE:  default: 'HFEmbedder'
+        NAME: ACEHFEmbedder
+        # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
+        HF_MODEL_CLS: T5EncoderModel
+        # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
+        MODEL_PATH: /home/Ubuntu/Downloads/Unmodel/Reference_models/t5_xxl/
+        # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
+        HF_TOKENIZER_CLS: T5Tokenizer
+        # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
+        TOKENIZER_PATH: /home/Ubuntu/Downloads/Unmodel/Reference_models/t5_xxl/
+        ADDED_IDENTIFIER: [ '<img>','{image}', '{caption}', '{mask}', '{ref_image}', '{image1}', '{image2}', '{image3}', '{image4}', '{image5}', '{image6}', '{image7}', '{image8}', '{image9}' ]
+        # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
+        MAX_LENGTH: 512
+        # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
+        OUTPUT_KEY: last_hidden_state
+        # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
+        D_TYPE: bfloat16
+        # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
+        BATCH_INFER: False
+        CLEAN: whitespace
+      # CLIP_MODEL DESCRIPTION:  TYPE:  default: ''
+      CLIP_MODEL:
+        # NAME DESCRIPTION:  TYPE:  default: 'HFEmbedder'
+        NAME: ACEHFEmbedder
+        # HF_MODEL_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
+        HF_MODEL_CLS: CLIPTextModel
+        # MODEL_PATH DESCRIPTION: model folder path TYPE: NoneType default: None
+        MODEL_PATH: /home/Ubuntu/Downloads/Unmodel/Reference_models/clip_l/
+        # HF_TOKENIZER_CLS DESCRIPTION: huggingface cls in transfomer TYPE: NoneType default: None
+        HF_TOKENIZER_CLS: CLIPTokenizer
+        # TOKENIZER_PATH DESCRIPTION: tokenizer folder path TYPE: NoneType default: None
+        TOKENIZER_PATH: /home/Ubuntu/Downloads/Unmodel/Reference_models/clip_l/
+        # MAX_LENGTH DESCRIPTION: max length of input TYPE: int default: 77
+        MAX_LENGTH: 77
+        # OUTPUT_KEY DESCRIPTION: output key TYPE: str default: 'last_hidden_state'
+        OUTPUT_KEY: pooler_output
+        # D_TYPE DESCRIPTION: dtype TYPE: str default: 'bfloat16'
+        D_TYPE: bfloat16
+        # BATCH_INFER DESCRIPTION: batch infer TYPE: bool default: False
+        BATCH_INFER: True
+        CLEAN: whitespace
+  TUNER:
+    # THE LORA PARAMETERS
+    - NAME: SwiftLoRA
+      R: 64
+      LORA_ALPHA: 64
+      LORA_DROPOUT: 0.0
+      BIAS: "none"
+      TARGET_MODULES: "(model.double_blocks.*(.qkv|.img_mlp.0|.img_mlp.2|.txt_mlp.0|.txt_mlp.2|.proj|.img_mod.lin|.txt_mod.lin))|(model.single_blocks.*(.linear1|.linear2|.modulation.lin))$"
+      #
+  SAMPLE_ARGS:
+    SAMPLE_STEPS: 28
+    SAMPLER: flow_euler
+    SEED: 42
+    IMAGE_SIZE: [ 2048, 2048 ]
+    #IMAGE_SIZE: [ 1024, 1024 ]
+    GUIDE_SCALE: 50
+
+  LR_SCHEDULER:
+    NAME: StepAnnealingLR
+    WARMUP_STEPS: 0
+    TOTAL_STEPS: 100000
+    DECAY_MODE: 'cosine'
+  #
+  OPTIMIZER:
+    NAME: AdamW
+    LEARNING_RATE: 1e-3
+    BETAS: [ 0.9, 0.999 ]
+    EPS: 1e-6
+    WEIGHT_DECAY: 1e-2
+    AMSGRAD: False
+  #
+  TRAIN_DATA:
+    NAME: ACEPlusDataset
+    MODE: train
+    DATA_LIST: data/train.csv
+    DELIMITER: "#;#"
+    MODIFY_MODE: True
+    # input_image, input_mask, input_reference_image, target_image, instruction, task_type
+    FIELDS: ["edit_image", "edit_mask", "ref_image", "target_image", "prompt", "data_type"]
+    PATH_PREFIX: ""
+    EDIT_TYPE_LIST: []
+    MAX_SEQ_LEN: 4096
+   # MAX_SEQ_LEN: 2048 -Vijay
+    D: 16
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    NUM_WORKERS: 4
+    SAMPLER:
+      NAME: LoopSampler
+
+  EVAL_DATA:
+    NAME: ACEPlusDataset
+    MODE: eval
+    DATA_LIST: data/train.csv
+    DELIMITER: "#;#"
+    MODIFY_MODE: True
+    # input_image, input_mask, input_reference_image, target_image, instruction, task_type
+    FIELDS: [ "edit_image", "edit_mask", "ref_image", "target_image", "prompt", "data_type" ]
+    PATH_PREFIX: ""
+    EDIT_TYPE_LIST: [ ]
+    MAX_SEQ_LEN: 4096
+   # MAX_SEQ_LEN: 2048 -Vijay
+    D: 16
+    PIN_MEMORY: True
+    BATCH_SIZE: 1
+    NUM_WORKERS: 4
+
+  TRAIN_HOOKS:
+    - NAME: ACEBackwardHook
+      GRADIENT_CLIP: 1.0
+      PRIORITY: 10
+    - NAME: LogHook
+      LOG_INTERVAL: 20
+    - NAME: ACECheckpointHook
+      INTERVAL: 50
+      #INTERVAL: 250 --Vijay
+      PRIORITY: 200
+      DISABLE_SNAPSHOT: True
+    - NAME: ProbeDataHook
+      PROB_INTERVAL: 10
+      #PROB_INTERVAL: 50 -Vijay
+      PRIORITY: 0
+    - NAME: TensorboardLogHook
+      LOG_INTERVAL: 50
+  EVAL_HOOKS:
+    - NAME: ProbeDataHook
+      PROB_INTERVAL: 10
+      #PROB_INTERVAL: 50 -Vijay
+      PRIORITY: 0

ACE_plus/flashenv/bin/Activate.ps1

@@ -0,0 +1,247 @@
+<#
+.Synopsis
+Activate a Python virtual environment for the current PowerShell session.
+
+.Description
+Pushes the python executable for a virtual environment to the front of the
+$Env:PATH environment variable and sets the prompt to signify that you are
+in a Python virtual environment. Makes use of the command line switches as
+well as the `pyvenv.cfg` file values present in the virtual environment.
+
+.Parameter VenvDir
+Path to the directory that contains the virtual environment to activate. The
+default value for this is the parent of the directory that the Activate.ps1
+script is located within.
+
+.Parameter Prompt
+The prompt prefix to display when this virtual environment is activated. By
+default, this prompt is the name of the virtual environment folder (VenvDir)
+surrounded by parentheses and followed by a single space (ie. '(.venv) ').
+
+.Example
+Activate.ps1
+Activates the Python virtual environment that contains the Activate.ps1 script.
+
+.Example
+Activate.ps1 -Verbose
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and shows extra information about the activation as it executes.
+
+.Example
+Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv
+Activates the Python virtual environment located in the specified location.
+
+.Example
+Activate.ps1 -Prompt "MyPython"
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and prefixes the current prompt with the specified string (surrounded in
+parentheses) while the virtual environment is active.
+
+.Notes
+On Windows, it may be required to enable this Activate.ps1 script by setting the
+execution policy for the user. You can do this by issuing the following PowerShell
+command:
+
+PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+
+For more information on Execution Policies: 
+https://go.microsoft.com/fwlink/?LinkID=135170
+
+#>
+Param(
+    [Parameter(Mandatory = $false)]
+    [String]
+    $VenvDir,
+    [Parameter(Mandatory = $false)]
+    [String]
+    $Prompt
+)
+
+<# Function declarations --------------------------------------------------- #>
+
+<#
+.Synopsis
+Remove all shell session elements added by the Activate script, including the
+addition of the virtual environment's Python executable from the beginning of
+the PATH variable.
+
+.Parameter NonDestructive
+If present, do not remove this function from the global namespace for the
+session.
+
+#>
+function global:deactivate ([switch]$NonDestructive) {
+    # Revert to original values
+
+    # The prior prompt:
+    if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) {
+        Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt
+        Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT
+    }
+
+    # The prior PYTHONHOME:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME
+        Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME
+    }
+
+    # The prior PATH:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH
+        Remove-Item -Path Env:_OLD_VIRTUAL_PATH
+    }
+
+    # Just remove the VIRTUAL_ENV altogether:
+    if (Test-Path -Path Env:VIRTUAL_ENV) {
+        Remove-Item -Path env:VIRTUAL_ENV
+    }
+
+    # Just remove VIRTUAL_ENV_PROMPT altogether.
+    if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) {
+        Remove-Item -Path env:VIRTUAL_ENV_PROMPT
+    }
+
+    # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether:
+    if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) {
+        Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force
+    }
+
+    # Leave deactivate function in the global namespace if requested:
+    if (-not $NonDestructive) {
+        Remove-Item -Path function:deactivate
+    }
+}
+
+<#
+.Description
+Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the
+given folder, and returns them in a map.
+
+For each line in the pyvenv.cfg file, if that line can be parsed into exactly
+two strings separated by `=` (with any amount of whitespace surrounding the =)
+then it is considered a `key = value` line. The left hand string is the key,
+the right hand is the value.
+
+If the value starts with a `'` or a `"` then the first and last character is
+stripped from the value before being captured.
+
+.Parameter ConfigDir
+Path to the directory that contains the `pyvenv.cfg` file.
+#>
+function Get-PyVenvConfig(
+    [String]
+    $ConfigDir
+) {
+    Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg"
+
+    # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue).
+    $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue
+
+    # An empty map will be returned if no config file is found.
+    $pyvenvConfig = @{ }
+
+    if ($pyvenvConfigPath) {
+
+        Write-Verbose "File exists, parse `key = value` lines"
+        $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath
+
+        $pyvenvConfigContent | ForEach-Object {
+            $keyval = $PSItem -split "\s*=\s*", 2
+            if ($keyval[0] -and $keyval[1]) {
+                $val = $keyval[1]
+
+                # Remove extraneous quotations around a string value.
+                if ("'""".Contains($val.Substring(0, 1))) {
+                    $val = $val.Substring(1, $val.Length - 2)
+                }
+
+                $pyvenvConfig[$keyval[0]] = $val
+                Write-Verbose "Adding Key: '$($keyval[0])'='$val'"
+            }
+        }
+    }
+    return $pyvenvConfig
+}
+
+
+<# Begin Activate script --------------------------------------------------- #>
+
+# Determine the containing directory of this script
+$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition
+$VenvExecDir = Get-Item -Path $VenvExecPath
+
+Write-Verbose "Activation script is located in path: '$VenvExecPath'"
+Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)"
+Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)"
+
+# Set values required in priority: CmdLine, ConfigFile, Default
+# First, get the location of the virtual environment, it might not be
+# VenvExecDir if specified on the command line.
+if ($VenvDir) {
+    Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values"
+}
+else {
+    Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir."
+    $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/")
+    Write-Verbose "VenvDir=$VenvDir"
+}
+
+# Next, read the `pyvenv.cfg` file to determine any required value such
+# as `prompt`.
+$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir
+
+# Next, set the prompt from the command line, or the config file, or
+# just use the name of the virtual environment folder.
+if ($Prompt) {
+    Write-Verbose "Prompt specified as argument, using '$Prompt'"
+}
+else {
+    Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value"
+    if ($pyvenvCfg -and $pyvenvCfg['prompt']) {
+        Write-Verbose "  Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'"
+        $Prompt = $pyvenvCfg['prompt'];
+    }
+    else {
+        Write-Verbose "  Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)"
+        Write-Verbose "  Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'"
+        $Prompt = Split-Path -Path $venvDir -Leaf
+    }
+}
+
+Write-Verbose "Prompt = '$Prompt'"
+Write-Verbose "VenvDir='$VenvDir'"
+
+# Deactivate any currently active virtual environment, but leave the
+# deactivate function in place.
+deactivate -nondestructive
+
+# Now set the environment variable VIRTUAL_ENV, used by many tools to determine
+# that there is an activated venv.
+$env:VIRTUAL_ENV = $VenvDir
+
+if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) {
+
+    Write-Verbose "Setting prompt to '$Prompt'"
+
+    # Set the prompt to include the env name
+    # Make sure _OLD_VIRTUAL_PROMPT is global
+    function global:_OLD_VIRTUAL_PROMPT { "" }
+    Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT
+    New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt
+
+    function global:prompt {
+        Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) "
+        _OLD_VIRTUAL_PROMPT
+    }
+    $env:VIRTUAL_ENV_PROMPT = $Prompt
+}
+
+# Clear PYTHONHOME
+if (Test-Path -Path Env:PYTHONHOME) {
+    Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME
+    Remove-Item -Path Env:PYTHONHOME
+}
+
+# Add the venv to the PATH
+Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH
+$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH"

ACE_plus/flashenv/bin/activate

@@ -0,0 +1,69 @@
+# This file must be used with "source bin/activate" *from bash*
+# you cannot run it directly
+
+deactivate () {
+    # reset old environment variables
+    if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then
+        PATH="${_OLD_VIRTUAL_PATH:-}"
+        export PATH
+        unset _OLD_VIRTUAL_PATH
+    fi
+    if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then
+        PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}"
+        export PYTHONHOME
+        unset _OLD_VIRTUAL_PYTHONHOME
+    fi
+
+    # This should detect bash and zsh, which have a hash command that must
+    # be called to get it to forget past commands.  Without forgetting
+    # past commands the $PATH changes we made may not be respected
+    if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
+        hash -r 2> /dev/null
+    fi
+
+    if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then
+        PS1="${_OLD_VIRTUAL_PS1:-}"
+        export PS1
+        unset _OLD_VIRTUAL_PS1
+    fi
+
+    unset VIRTUAL_ENV
+    unset VIRTUAL_ENV_PROMPT
+    if [ ! "${1:-}" = "nondestructive" ] ; then
+    # Self destruct!
+        unset -f deactivate
+    fi
+}
+
+# unset irrelevant variables
+deactivate nondestructive
+
+VIRTUAL_ENV=/home/Ubuntu/Downloads/Unmodel/ACE_plus/flashenv
+export VIRTUAL_ENV
+
+_OLD_VIRTUAL_PATH="$PATH"
+PATH="$VIRTUAL_ENV/"bin":$PATH"
+export PATH
+
+# unset PYTHONHOME if set
+# this will fail if PYTHONHOME is set to the empty string (which is bad anyway)
+# could use `if (set -u; : $PYTHONHOME) ;` in bash
+if [ -n "${PYTHONHOME:-}" ] ; then
+    _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}"
+    unset PYTHONHOME
+fi
+
+if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then
+    _OLD_VIRTUAL_PS1="${PS1:-}"
+    PS1='(flashenv) '"${PS1:-}"
+    export PS1
+    VIRTUAL_ENV_PROMPT='(flashenv) '
+    export VIRTUAL_ENV_PROMPT
+fi
+
+# This should detect bash and zsh, which have a hash command that must
+# be called to get it to forget past commands.  Without forgetting
+# past commands the $PATH changes we made may not be respected
+if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
+    hash -r 2> /dev/null
+fi

ACE_plus/flashenv/bin/activate.csh

@@ -0,0 +1,26 @@
+# This file must be used with "source bin/activate.csh" *from csh*.
+# You cannot run it directly.
+# Created by Davide Di Blasi <davidedb@gmail.com>.
+# Ported to Python 3.3 venv by Andrew Svetlov <andrew.svetlov@gmail.com>
+
+alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; unsetenv VIRTUAL_ENV_PROMPT; test "\!:*" != "nondestructive" && unalias deactivate'
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+setenv VIRTUAL_ENV /home/Ubuntu/Downloads/Unmodel/ACE_plus/flashenv
+
+set _OLD_VIRTUAL_PATH="$PATH"
+setenv PATH "$VIRTUAL_ENV/"bin":$PATH"
+
+
+set _OLD_VIRTUAL_PROMPT="$prompt"
+
+if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then
+    set prompt = '(flashenv) '"$prompt"
+    setenv VIRTUAL_ENV_PROMPT '(flashenv) '
+endif
+
+alias pydoc python -m pydoc
+
+rehash

ACE_plus/flashenv/bin/activate.fish

@@ -0,0 +1,69 @@
+# This file must be used with "source <venv>/bin/activate.fish" *from fish*
+# (https://fishshell.com/); you cannot run it directly.
+
+function deactivate  -d "Exit virtual environment and return to normal shell environment"
+    # reset old environment variables
+    if test -n "$_OLD_VIRTUAL_PATH"
+        set -gx PATH $_OLD_VIRTUAL_PATH
+        set -e _OLD_VIRTUAL_PATH
+    end
+    if test -n "$_OLD_VIRTUAL_PYTHONHOME"
+        set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME
+        set -e _OLD_VIRTUAL_PYTHONHOME
+    end
+
+    if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
+        set -e _OLD_FISH_PROMPT_OVERRIDE
+        # prevents error when using nested fish instances (Issue #93858)
+        if functions -q _old_fish_prompt
+            functions -e fish_prompt
+            functions -c _old_fish_prompt fish_prompt
+            functions -e _old_fish_prompt
+        end
+    end
+
+    set -e VIRTUAL_ENV
+    set -e VIRTUAL_ENV_PROMPT
+    if test "$argv[1]" != "nondestructive"
+        # Self-destruct!
+        functions -e deactivate
+    end
+end
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+set -gx VIRTUAL_ENV /home/Ubuntu/Downloads/Unmodel/ACE_plus/flashenv
+
+set -gx _OLD_VIRTUAL_PATH $PATH
+set -gx PATH "$VIRTUAL_ENV/"bin $PATH
+
+# Unset PYTHONHOME if set.
+if set -q PYTHONHOME
+    set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
+    set -e PYTHONHOME
+end
+
+if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
+    # fish uses a function instead of an env var to generate the prompt.
+
+    # Save the current fish_prompt function as the function _old_fish_prompt.
+    functions -c fish_prompt _old_fish_prompt
+
+    # With the original prompt function renamed, we can override with our own.
+    function fish_prompt
+        # Save the return status of the last command.
+        set -l old_status $status
+
+        # Output the venv prompt; color taken from the blue of the Python logo.
+        printf "%s%s%s" (set_color 4B8BBE) '(flashenv) ' (set_color normal)
+
+        # Restore the return status of the previous command.
+        echo "exit $old_status" | .
+        # Output the original/"old" prompt.
+        _old_fish_prompt
+    end
+
+    set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
+    set -gx VIRTUAL_ENV_PROMPT '(flashenv) '
+end

ACE_plus/flashenv/bin/pip

@@ -0,0 +1,8 @@
+#!/home/Ubuntu/Downloads/Unmodel/ACE_plus/flashenv/bin/python
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ACE_plus/flashenv/bin/pip3

@@ -0,0 +1,8 @@
+#!/home/Ubuntu/Downloads/Unmodel/ACE_plus/flashenv/bin/python
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ACE_plus/flashenv/bin/pip3.10

@@ -0,0 +1,8 @@
+#!/home/Ubuntu/Downloads/Unmodel/ACE_plus/flashenv/bin/python
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

ACE_plus/flashenv/lib/python3.10/site-packages/pip/_vendor/pkg_resources/__pycache__/__init__.cpython-310.pyc

@@ -0,0 +1,3 @@
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+oid sha256:d989275a5e9b8d252af087830d5418e0ef537178965262190e7d6be772601c45
+size 113951

ACE_plus/flashenv/lib/python3.10/site-packages/pip/_vendor/rich/__pycache__/_emoji_codes.cpython-310.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a112fd0d52e2fc5e903d0546415911f83afb711cc1ac9379ef7ca5326c431c5
+size 360065