reduce args for generate_image by using a config class. update ignore to account for idea folder, re-org code to increase speed.

3 files changed, 72 insertions, 59 deletions

diff --git a/.gitignore b/.gitignore
index 96ef086..73f016a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,5 @@
 *.png
 *.log
 prompts.txt
+.idea
+Flux.egg-info
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..2b12a4f
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,12 @@
+[project]
+name = "Flux"
+version = "0.0.1"
+authors = [
+    { name="Zach Berwaldt", email="zberwaldt@gmail.com" }
+]
+description = "A simple CLI to generate images with flux locally."
+readme = "README.md"
+requires-python = ">=3.12"
+
+[project.scripts]
+flux = "run_flux:main"
\ No newline at end of file
diff --git a/run_flux.py b/run_flux.py
index b288935..446f57a 100644
--- a/run_flux.py
+++ b/run_flux.py
@@ -1,11 +1,7 @@
-import torch
 import os
-import base64
 import argparse
-import argcomplete
-from diffusers import FluxPipeline, FluxImg2ImgPipeline
-from diffusers.utils import load_image
-from typing import Tuple
+from dataclasses import dataclass, asdict
+from typing import Tuple, Optional
 from pathlib import Path
 from PIL import Image
 import uuid
@@ -39,6 +35,9 @@ def record_prompt(prompt, filename="prompts.txt"):
         logger.info(f"Prompt already exists in the file: \"{prompt}\"")
 
 def load_flux_img_to_img():
+    import torch
+    from diffusers import FluxImg2ImgPipeline
+
     pipeline = FluxImg2ImgPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
     pipeline.enable_model_cpu_offload()
     pipeline.vae.enable_slicing()
@@ -46,31 +45,32 @@ def load_flux_img_to_img():
     return pipeline
 
 def load_flux():
+    import torch
+    from diffusers import FluxPipeline
+
     pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
     pipeline.enable_model_cpu_offload()
     pipeline.vae.enable_slicing()
     pipeline.vae.enable_tiling()
     return pipeline
 
-def generate_image(pipeline, prompt, strength=None, prompt_2=None, init_image=None, height=1024, width=1024, guideance_scale=0, num_images_per_prompt=1, num_inference_steps=50):
-    kwargs = { 
-          "prompt": prompt,
-          "prompt_2": prompt_2,
-          "guidance_scale": guideance_scale,
-          "height":height,
-          "width": width,
-          "max_sequence_length": 256,
-          "num_inference_steps": num_inference_steps,
-          "num_images_per_prompt": num_images_per_prompt
-    }
-
-    if strength:
-        kwargs["strength"] = strength
-
-    if isinstance(pipeline, FluxImg2ImgPipeline) and init_image is not None:
-        kwargs["image"] = init_image
-
-    images = pipeline(**kwargs).images
+@dataclass(frozen=True)
+class GenerateImageConfig:
+    prompt: str
+    prompt_2: Optional[str] = None
+    init_image: Optional[Image] = None
+    strength: int = 0.0
+    guidance_scale: float = 0.0
+    height: int = 1024
+    width: int = 1024
+    num_images_per_prompt: int = 1
+    num_inference_steps: int = 50
+
+    def to_dict(self):
+        return {k: v for k, v in asdict(self).items() if v is not None}
+
+def generate_image(pipeline, config: GenerateImageConfig):
+    images = pipeline(**config.to_dict()).images
     return images
 
 def generate_random_string(length=16) -> str:
@@ -79,27 +79,36 @@ def generate_random_string(length=16) -> str:
 def parse_dimensions(dim_str: str) -> Tuple[int, int]:
     try:
         width, height = map(int, dim_str.split(':'))
-        return (width, height)
+        return width, height
     except ValueError:
         raise argparse.ArgumentError('Dimensions must be in format width:height')
 
 def main():
-    try:
-        logging.basicConfig(filename="flux.log", level=logging.INFO, format='%(asctime)s - %(levelname)s -> %(message)s', datefmt="%m/%d/%Y %I:%M:%S %p")
-        
-        logger.info("Parsing arguments")
+    logging.basicConfig(filename="flux.log", level=logging.INFO, format='%(asctime)s - %(levelname)s -> %(message)s',
+                        datefmt="%m/%d/%Y %I:%M:%S %p")
+
+    logger.info("Parsing arguments")
+
+    parser = argparse.ArgumentParser(description="Generate some A.I. images", epilog="All done!")
+
+    parser.add_argument("-n", "--number", type=int, default=1, help="the number of images you want to generate")
+    parser.add_argument("-o", "--output", type=str, default="image", help="the name of the output image")
+    parser.add_argument("-p", "--prompt", type=str, required=True, help="the prompt")
+    parser.add_argument("-p2", "--prompt2", type=str, help="A second prompt")
+    parser.add_argument("-gs", "--guideance-scale", type=float, default=0)
+    parser.add_argument("--strength", type=float)
+    parser.add_argument("--size", type=parse_dimensions, default="1024:1024", help="the size of the output images")
+    parser.add_argument("-u", "--use-image", action="store_true", help="use a predefined image")
+    args = parser.parse_args()
 
-        args = parser.parse_args()
+    try:
+        import torch
+        from diffusers.utils import load_image
 
         logger.info("Choosing model...")
 
         pipeline = load_flux_img_to_img() if args.use_image else load_flux()
 
-        if isinstance(pipeline, FluxPipeline):
-            logger.info("Using text-to-image model")
-        else:
-            logger.info("Using image-to-image model")
-
         pipeline.to(torch.float16)
 
         # target_img = STORAGE_DIR / "1517481062292.jpg"
@@ -119,16 +128,20 @@ def main():
 
         logger.info("Generating image(s)...")
 
+        config = GenerateImageConfig(
+            init_image=init_image if args.use_image else None,
+            prompt=args.prompt,
+            prompt_2=args.prompt2 if args.prompt2 else None,
+            width=width,
+            height=height,
+            strength=args.strength,
+            guidance_scale=args.guideance_scale,
+            num_images_per_prompt=args.number
+        )
+
         images = generate_image(
-                pipeline=pipeline, 
-                init_image=init_image,
-                prompt=args.prompt,
-                prompt_2=args.prompt2,
-                width=width,
-                height=height,
-                strength=args.strength,
-                guideance_scale=args.guideance_scale,
-                num_images_per_prompt=args.number
+                pipeline=pipeline,
+                config=config
             )
 
         for image in images:
@@ -148,20 +161,6 @@ def main():
         print(f"An error occured: {e}")
         exit(1)
 
-parser = argparse.ArgumentParser(description="Generate some A.I. images", epilog="All done!")
-
-parser.add_argument("-n", "--number", type=int, default=1, help="the number of images you want to generate") 
-parser.add_argument("-o", "--output", type=str, default="image", help="the name of the output image")
-parser.add_argument("-p", "--prompt", type=str, required=True, help="the prompt")
-parser.add_argument("-p2", "--prompt2", type=str, help="A second prompt")
-parser.add_argument("-gs", "--guideance-scale", type=float, default=0)
-parser.add_argument("--strength", type=float)
-parser.add_argument("--size", type=parse_dimensions, default="1024:1024", help="the size of the output images")
-parser.add_argument("-u", "--use-image", action="store_true", help="use a predefined image")
-
-# parser.add_argument("-b", "--base-image").completer = image_completer
-
-# argcomplete.autocomplete(parser)
 
 if __name__ == "__main__":
     main()