lora dropdown

app.py

@@ -9,24 +9,36 @@ from huggingface_hub import hf_hub_download
 import os
 
 dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
+
+if torch.cuda.is_available():
+    device = "cuda"
+elif torch.backends.mps.is_available():
+    if not torch.backends.mps.is_built():
+        print("MPS not available because the current PyTorch install was not "
+              "built with MPS enabled.")
+    device = "mps"
+else:
+    device = "cpu"
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
+print(f"Device is: {device}")
 # Initialize the pipeline globally
 pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
 
 
 @spaces.GPU(duration=300)
-def infer(prompt, lora_model, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0,
+def infer(prompt, lora_models, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0,
           num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
     global pipe
     
-    # Load LoRA if specified
-    if lora_model:
+    # Load LoRAs if specified
+    if lora_models:
+        print(f"Loading Loras: {lora_models}")
         try:
-            pipe.load_lora_weights(lora_model)
+            for lora_model in lora_models:
+                pipe.load_lora_weights(lora_model)
         except Exception as e:
             return None, seed, f"Failed to load LoRA model: {str(e)}"
     
@@ -45,7 +57,7 @@ def infer(prompt, lora_model, seed=42, randomize_seed=False, width=1024, height=
         ).images[0]
         
         # Unload LoRA weights after generation
-        if lora_model:
+        if lora_models:
             pipe.unload_lora_weights()
         
         return image, seed, "Image generated successfully."
@@ -53,12 +65,6 @@ def infer(prompt, lora_model, seed=42, randomize_seed=False, width=1024, height=
         return None, seed, f"Error during image generation: {str(e)}"
 
 
-examples = [
-    ["a tiny astronaut hatching from an egg on the moon", ""],
-    ["a cat holding a sign that says hello world", ""],
-    ["an anime illustration of a wiener schnitzel", ""],
-]
-
 css = """
 #col-container {
     margin: 0 auto;
@@ -68,11 +74,6 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev] with LoRA Support
-12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
-[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
-        """)
-        
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -83,13 +84,17 @@ with gr.Blocks(css=css) as demo:
             )
             run_button = gr.Button("Run", scale=0)
         
-        lora_model = gr.Text(
-            label="LoRA Model ID (optional)",
-            placeholder="Enter Hugging Face LoRA model ID",
-        )
+        # lora_model = gr.Text(
+        #     label="LoRA Model ID (optional)",
+        #     placeholder="Enter Hugging Face LoRA model ID",
+        # )
+        lora_models = gr.Dropdown([
+            ("jerry", "bryanbrunetti/jerryfluxlora"),
+            ("sebastian", "bryanbrunetti/sebastianfluxlora"),
+            ("sadie", "bryanbrunetti/sadiefluxlora")
+        ], multiselect=True, info="load lora (optional) use the name in the prompt", label="Choose People")
         
         result = gr.Image(label="Result", show_label=False)
-        output_message = gr.Textbox(label="Output Message")
         
         with gr.Accordion("Advanced Settings", open=False):
             seed = gr.Slider(
@@ -106,18 +111,19 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,
+                    value=512,
                 )
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,
+                    value=512,
                 )
             with gr.Row():
                 guidance_scale = gr.Slider(
                     label="Guidance Scale",
+                    info="How close to follow prompt",
                     minimum=1,
                     maximum=15,
                     step=0.1,
@@ -125,24 +131,19 @@ with gr.Blocks(css=css) as demo:
                 )
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
+                    info="higher = more details",
                     minimum=1,
                     maximum=50,
                     step=1,
                     value=28,
                 )
         
-        gr.Examples(
-            examples=examples,
-            fn=infer,
-            inputs=[prompt, lora_model],
-            outputs=[result, seed, output_message],
-            cache_examples="lazy"
-        )
-    
+        output_message = gr.Textbox(label="Output Message")
+        
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
-        inputs=[prompt, lora_model, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs=[prompt, lora_models, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs=[result, seed, output_message]
     )
 

requirements.txt

@@ -1,7 +1,11 @@
 accelerate
 torch
 transformers==4.42.4
-xformers
 sentencepiece
 peft
-diffusers
+diffusers~=0.30.0
+gradio~=4.42.0
+spaces~=0.29.3
+protobuf
+numpy~=1.26.4
+huggingface_hub~=0.24.6