Image Annotation Moderation and FLUX Model Integration

autoupdate_validator_steps.sh

@@ -7,4 +7,5 @@
 echo $CONDA_PREFIX
 $CONDA_PREFIX/bin/pip install -e .
 $CONDA_PREFIX/bin/python bitmind/download_data.py
+$CONDA_PREFIX/bin/python verify_validator_models.py
 echo "Autoupdate steps complete :)"

bitmind/constants.py

@@ -1,14 +1,16 @@
 import os
+import torch
 
 
-WANDB_PROJECT = 'bitmind-subnet'
+WANDB_PROJECT = 'bitmind'
 WANDB_ENTITY = 'bitmindai'
 
 DATASET_META = {
     "real": [
         {"path": "bitmind/open-images-v7", "create_splits": False},
         {"path": "bitmind/ffhq-256", "create_splits": False},
-        {"path": "bitmind/celeb-a-hq", "create_splits": False}
+        {"path": "bitmind/celeb-a-hq", "create_splits": False},
+        {"path": "bitmind/MS-COCO-unique-256", "create_splits": False}
     ],
     "fake": [
         {"path": "bitmind/realvis-xl", "create_splits": False},
@@ -39,19 +41,36 @@
         {
             "path": "stabilityai/stable-diffusion-xl-base-1.0",
             "use_safetensors": True,
+            "torch_dtype": torch.float16,
             "variant": "fp16",
             "pipeline": "StableDiffusionXLPipeline"
         },
         {
             "path": "SG161222/RealVisXL_V4.0",
             "use_safetensors": True,
+            "torch_dtype": torch.float16,
             "variant": "fp16",
             "pipeline": "StableDiffusionXLPipeline"
         },
         {
             "path": "Corcelio/mobius",
             "use_safetensors": True,
+            "torch_dtype": torch.float16,
             "pipeline": "StableDiffusionXLPipeline"
+        },
+        {
+            "path": 'black-forest-labs/FLUX.1-dev',
+            "use_safetensors": True,
+            "torch_dtype": torch.bfloat16,
+            "generate_args": {
+                "guidance_scale": 2,
+                "num_inference_steps": {"min": 50, "max": 125},
+                "generator": torch.Generator("cuda"),
+                "height": [512, 768],
+                "width": [512, 768]
+            },
+            "enable_cpu_offload": False,
+            "pipeline": "FluxPipeline"
         }
     ]
 }
@@ -60,16 +79,30 @@
 
 TARGET_IMAGE_SIZE = (256, 256)
 
-PROMPT_TYPES = ('random', 'annotation')
+PROMPT_TYPES = ('random', 'annotation', 'none')
 
 PROMPT_GENERATOR_ARGS = {
     m['model']: m for m in VALIDATOR_MODEL_META['prompt_generators']
 }
 
 PROMPT_GENERATOR_NAMES = list(PROMPT_GENERATOR_ARGS.keys())
 
+# args for .from_pretrained
 DIFFUSER_ARGS = {
-    m['path']: {k: v for k, v in m.items() if k != 'path' and k != 'pipeline'}  
+    m['path']: {
+        k: v for k, v in m.items()
+        if k not in ('path', 'pipeline', 'generate_args', 'enable_cpu_offload')
+    } for m in VALIDATOR_MODEL_META['diffusers']
+}
+
+GENERATE_ARGS = {
+    m['path']: m['generate_args']
+    for m in VALIDATOR_MODEL_META['diffusers']
+    if 'generate_args' in m
+}
+
+DIFFUSER_CPU_OFFLOAD_ENABLED = {
+    m['path']: m.get('enable_cpu_offload', False)
     for m in VALIDATOR_MODEL_META['diffusers']
 }
 
@@ -79,4 +112,7 @@
 
 DIFFUSER_NAMES = list(DIFFUSER_ARGS.keys())
 
-IMAGE_ANNOTATION_MODEL = "Salesforce/blip2-opt-2.7b-coco"
+IMAGE_ANNOTATION_MODEL = "Salesforce/blip2-opt-6.7b-coco"
+
+TEXT_MODERATION_MODEL = "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit" 
+# "meta-llama/Meta-Llama-3.1-8B-Instruct"

bitmind/protocol.py

@@ -26,12 +26,6 @@
 import base64
 import torch
 
-def b64_encode(image):
-    if isinstance(image, torch.Tensor):
-        image = transforms.ToPILImage()(image.cpu().detach())
-    image_bytes = BytesIO()
-    image.save(image_bytes, format="JPEG")
-    return base64.b64encode(image_bytes.getvalue())
 
 def prepare_image_synapse(image: Image):
     """
@@ -43,7 +37,12 @@ def prepare_image_synapse(image: Image):
     Returns:
         ImageSynapse: An instance of ImageSynapse containing the encoded image and a default prediction value.
     """
-    b64_encoded_image = b64_encode(image)
+    if isinstance(image, torch.Tensor):
+        image = transforms.ToPILImage()(image.cpu().detach())
+
+    image_bytes = BytesIO()
+    image.save(image_bytes, format="JPEG")
+    b64_encoded_image = base64.b64encode(image_bytes.getvalue())
     return ImageSynapse(image=b64_encoded_image)
 
 

bitmind/synthetic_image_generation/README.md

@@ -1,18 +1,4 @@
 
 # Synthetic Image Generation
 
-This folder contains files for the implementation of a joint vision-to-language and text-to-image model system that generates highly diverse and realistic images for deepfake detector training.
-
-**test_data/:**
-
-Default output directory for real-image-to-annotation and annotation-to-synthetic-image pipelines in the associated notebooks.
-
-Notebooks:
-
-**real_image_to_text_annotation.ipynb :**
-
-Pipeline for real image dataset to text caption dataset generation. Contains function that generates subdirectories of annotations for each real image dataset. Annotations are formatted as JSONs with captions (Strings) of images. The filename of the JSONs correspond to the image index in the associated dataset dictionary.
-
-**text_annotation_to_synthetic_image.ipynb :**
-
-Pipeline for text annotation to synthetic image dataset generation.
+This folder contains files for the implementation of a joint vision-to-language and text-to-image model system that generates highly diverse and realistic images for deepfake detector training and Subnet 34 validating.