refactor argparser

src/mflux/generate.py

@@ -1,37 +1,19 @@
-import argparse
 import time
 from pathlib import Path
 
 from mflux import Config, Flux1, ModelConfig, StopImageGenerationException
+from mflux.ui.cli.parsers import CommandLineParser
 
 
 def main():
     # fmt: off
-    parser = argparse.ArgumentParser(description="Generate an image based on a prompt.")
-    parser.add_argument("--prompt", type=str, required=True, help="The textual description of the image to generate.")
-    parser.add_argument("--output", type=str, default="image.png", help="The filename for the output image. Default is \"image.png\".")
-    parser.add_argument("--model", "-m", type=str, required=True, choices=["dev", "schnell"], help="The model to use (\"schnell\" or \"dev\").")
-    parser.add_argument("--seed", type=int, default=None, help="Entropy Seed (Default is time-based random-seed)")
-    parser.add_argument("--height", type=int, default=1024, help="Image height (Default is 1024)")
-    parser.add_argument("--width", type=int, default=1024, help="Image width (Default is 1024)")
-    parser.add_argument("--steps", type=int, default=None, help="Inference Steps")
-    parser.add_argument('--stepwise-image-output-dir', type=str, default=None, help='[EXPERIMENTAL] Output dir to write step-wise images and their final composite image to. This feature may change in future versions.')
-    parser.add_argument("--guidance", type=float, default=3.5, help="Guidance Scale (Default is 3.5)")
-    parser.add_argument("--quantize",  "-q", type=int, choices=[4, 8], default=None, help="Quantize the model (4 or 8, Default is None)")
-    parser.add_argument("--path", type=str, default=None, help="Local path for loading a model from disk")
-    parser.add_argument("--lora-paths", type=str, nargs="*", default=None, help="Local safetensors for applying LORA from disk")
-    parser.add_argument("--lora-scales", type=float, nargs="*", default=None, help="Scaling factor to adjust the impact of LoRA weights on the model. A value of 1.0 applies the LoRA weights as they are.")
-    parser.add_argument("--metadata", action="store_true", help="Export image metadata as a JSON file.")
-    # fmt: on
-
+    parser = CommandLineParser(description="Generate an image based on a prompt.")
+    parser.add_model_arguments()
+    parser.add_lora_arguments()
+    parser.add_image_generator_arguments()
+    parser.add_output_arguments()
     args = parser.parse_args()
 
-    if args.path and args.model is None:
-        parser.error("--model must be specified when using --path")
-
-    if args.steps is None:
-        args.steps = 4 if args.model == "schnell" else 14
-
     # Load the model
     flux = Flux1(
         model_config=ModelConfig.from_alias(args.model),

src/mflux/generate_controlnet.py

@@ -1,40 +1,19 @@
-import argparse
 import time
 from pathlib import Path
 
 from mflux import ConfigControlnet, Flux1Controlnet, ModelConfig, StopImageGenerationException
+from mflux.ui.cli.parsers import CommandLineParser
 
 
 def main():
-    # fmt: off
-    parser = argparse.ArgumentParser(description="Generate an image based on a prompt.")
-    parser.add_argument("--prompt", type=str, required=True, help="The textual description of the image to generate.")
-    parser.add_argument("--controlnet-image-path", type=str, required=True, help="Local path of the image to use as input for controlnet.")
-    parser.add_argument("--controlnet-strength", type=float, default=0.4, help="Controls how strongly the control image influences the output image. A value of 0.0 means no influence. (Default is 0.4)")
-    parser.add_argument("--controlnet-save-canny", action="store_true", help="If set, save the Canny edge detection reference input image.")
-    parser.add_argument("--output", type=str, default="image.png", help="The filename for the output image. Default is \"image.png\".")
-    parser.add_argument("--model", "-m", type=str, required=True, choices=["dev", "schnell"], help="The model to use (\"schnell\" or \"dev\").")
-    parser.add_argument("--seed", type=int, default=None, help="Entropy Seed (Default is time-based random-seed)")
-    parser.add_argument("--height", type=int, default=1024, help="Image height (Default is 1024)")
-    parser.add_argument("--width", type=int, default=1024, help="Image width (Default is 1024)")
-    parser.add_argument("--steps", type=int, default=None, help="Inference Steps")
-    parser.add_argument('--stepwise-image-output-dir', type=str, default=None, help='Output dir to write step-wise images and their final composite image to.')
-    parser.add_argument("--guidance", type=float, default=3.5, help="Guidance Scale (Default is 3.5)")
-    parser.add_argument("--quantize",  "-q", type=int, choices=[4, 8], default=None, help="Quantize the model (4 or 8, Default is None)")
-    parser.add_argument("--path", type=str, default=None, help="Local path for loading a model from disk")
-    parser.add_argument("--lora-paths", type=str, nargs="*", default=None, help="Local safetensors for applying LORA from disk")
-    parser.add_argument("--lora-scales", type=float, nargs="*", default=None, help="Scaling factor to adjust the impact of LoRA weights on the model. A value of 1.0 applies the LoRA weights as they are.")
-    parser.add_argument("--metadata", action="store_true", help="Export image metadata as a JSON file.")
-    # fmt: on
-
+    parser = CommandLineParser(description="Generate an image based on a prompt and a controlnet reference image.")  # fmt: off
+    parser.add_model_arguments()
+    parser.add_lora_arguments()
+    parser.add_image_generator_arguments()
+    parser.add_controlnet_arguments()
+    parser.add_output_arguments()
     args = parser.parse_args()
 
-    if args.path and args.model is None:
-        parser.error("--model must be specified when using --path")
-
-    if args.steps is None:
-        args.steps = 4 if args.model == "schnell" else 14
-
     # Load the model
     flux = Flux1Controlnet(
         model_config=ModelConfig.from_alias(args.model),

src/mflux/save.py

@@ -1,18 +1,11 @@
-import argparse
-
 from mflux import Flux1, ModelConfig
+from mflux.ui.cli.parsers import CommandLineParser
 
 
 def main():
-    # fmt: off
-    parser = argparse.ArgumentParser(description="Save a quantized version of Flux.1 to disk.")
-    parser.add_argument("--path", type=str, required=True, help="Local path for loading a model from disk")
-    parser.add_argument("--model", "-m", type=str, required=True, choices=["dev", "schnell"], help="The model to use (\"schnell\" or \"dev\").")
-    parser.add_argument("--quantize", "-q", type=int, choices=[4, 8], default=8, help="Quantize the model (4 or 8, Default is 8)")
-    parser.add_argument("--lora-paths", type=str, nargs="*", default=None, help="Local safetensors for applying LORA from disk")
-    parser.add_argument("--lora-scales", type=float, nargs="*", default=None, help="Scaling factor to adjust the impact of LoRA weights on the model. A value of 1.0 applies the LoRA weights as they are.")
-    # fmt: on
-
+    parser = CommandLineParser(description="Save a quantized version of Flux.1 to disk.")  # fmt: off
+    parser.add_model_arguments()
+    parser.add_lora_arguments()
     args = parser.parse_args()
 
     print(f"Saving model {args.model} with quantization level {args.quantize}\n")

src/mflux/ui/cli/parsers.py

@@ -0,0 +1,51 @@
+import argparse
+from pathlib import Path
+
+from mflux.ui import defaults as ui_defaults
+
+
+# fmt: off
+class CommandLineParser(argparse.ArgumentParser):
+
+    def add_model_arguments(self):
+        self.add_argument("--model", "-m", type=str, required=True, choices=ui_defaults.MODEL_CHOICES, help=f"The model to use ({' or '.join(ui_defaults.MODEL_CHOICES)}).")
+        self.add_argument("--path", type=str, default=None, help="Local path for loading a model from disk")
+        self.add_argument("--quantize",  "-q", type=int, choices=ui_defaults.QUANTIZE_CHOICES, default=None, help=f"Quantize the model ({' or '.join(map(str, ui_defaults.QUANTIZE_CHOICES))}, Default is None)")
+
+    def add_lora_arguments(self):
+        self.add_argument("--lora-paths", type=str, nargs="*", default=None, help="Local safetensors for applying LORA from disk")
+        self.add_argument("--lora-scales", type=float, nargs="*", default=None, help="Scaling factor to adjust the impact of LoRA weights on the model. A value of 1.0 applies the LoRA weights as they are.")
+
+    def _add_image_generator_common_arguments(self):
+        self.add_argument("--height", type=int, default=ui_defaults.HEIGHT, help=f"Image height (Default is {ui_defaults.HEIGHT})")
+        self.add_argument("--width", type=int, default=ui_defaults.WIDTH, help=f"Image width (Default is {ui_defaults.HEIGHT})")
+        self.add_argument("--steps", type=int, default=None, help="Inference Steps")
+        self.add_argument("--guidance", type=float, default=ui_defaults.GUIDANCE_SCALE, help=f"Guidance Scale (Default is {ui_defaults.GUIDANCE_SCALE})")
+
+    def add_image_generator_arguments(self):
+        self.add_argument("--prompt", type=str, required=True, help="The textual description of the image to generate.")
+        self.add_argument("--seed", type=int, default=None, help="Entropy Seed (Default is time-based random-seed)")
+        self._add_image_generator_common_arguments()
+
+    def add_batch_image_generator_arguments(self):
+        self.add_argument("--prompts-file", type=Path, required=True, help="Local path for a file that holds a batch of prompts.")
+        self.add_argument("--global-seed", type=int, default=None, help="Entropy Seed (used for all prompts in the batch)")
+        self._add_image_generator_common_arguments()
+
+    def add_output_arguments(self):
+        self.add_argument("--metadata", action="store_true", help="Export image metadata as a JSON file.")
+        self.add_argument("--output", type=str, default="image.png", help="The filename for the output image. Default is \"image.png\".")
+        self.add_argument('--stepwise-image-output-dir', type=str, default=None, help='[EXPERIMENTAL] Output dir to write step-wise images and their final composite image to. This feature may change in future versions.')
+
+    def add_controlnet_arguments(self):
+        self.add_argument("--controlnet-image-path", type=str, required=True, help="Local path of the image to use as input for controlnet.")
+        self.add_argument("--controlnet-strength", type=float, default=ui_defaults.CONTROLNET_STRENGTH, help=f"Controls how strongly the control image influences the output image. A value of 0.0 means no influence. (Default is {ui_defaults.CONTROLNET_STRENGTH})")
+        self.add_argument("--controlnet-save-canny", action="store_true", help="If set, save the Canny edge detection reference input image.")
+
+    def parse_args(self):
+        namespace = super().parse_args()
+        if hasattr(namespace, "path") and namespace.path is not None and namespace.model is None:
+            namespace.error("--model must be specified when using --path")
+        if hasattr(namespace, "steps") and namespace.steps is None:
+            namespace.steps = ui_defaults.MODEL_INFERENCE_STEPS.get(namespace.model, None)
+        return namespace

src/mflux/ui/defaults.py

@@ -0,0 +1,9 @@
+CONTROLNET_STRENGTH = 0.4
+GUIDANCE_SCALE = 3.5
+HEIGHT, WIDTH = 1024, 1024
+MODEL_CHOICES = ["dev", "schnell"]
+MODEL_INFERENCE_STEPS = {
+    "dev": 14,
+    "schnell": 4,
+}
+QUANTIZE_CHOICES = [4, 8]