Lora schnell steps (#42)

* now with num_inference_steps

.dockerignore

@@ -19,3 +19,4 @@ __pycache__
 /model-cache
 /safety-cache
 /falcon-cache
+

predict.py

@@ -102,13 +102,11 @@ class SharedInputs:
         description="Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars'",
         default=None,
     )
-    lora_scale: Input = (
-        Input(
+    lora_scale: Input = Input(
             description="Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.",
             default=1.0,
             le=5.0,
             ge=-5.0,
-        ),
     )
     megapixels: Input = Input(
         description="Approximate number of megapixels for generated image",
@@ -642,6 +640,12 @@ def predict(
         prompt: str = SHARED_INPUTS.prompt,
         aspect_ratio: str = SHARED_INPUTS.aspect_ratio,
         num_outputs: int = SHARED_INPUTS.num_outputs,
+        num_inference_steps: int = Input(
+            description="Number of denoising steps. 4 is recommended, and lower number of steps produce lower quality outputs, faster.",
+            ge=1,
+            le=4,
+            default=4,
+        ),
         seed: int = SHARED_INPUTS.seed,
         output_format: str = SHARED_INPUTS.output_format,
         output_quality: int = SHARED_INPUTS.output_quality,
@@ -657,15 +661,15 @@ def predict(
                 prompt,
                 aspect_ratio,
                 num_outputs,
-                num_inference_steps=self.num_steps,
+                num_inference_steps=num_inference_steps,
                 seed=seed,
             )
         else:
             imgs, np_imgs = self.base_predict(
                 prompt,
                 aspect_ratio,
                 num_outputs,
-                num_inference_steps=self.num_steps,
+                num_inference_steps=num_inference_steps,
                 seed=seed,
             )