add lora

app.py

@@ -3,39 +3,60 @@ import numpy as np
 import random
 import spaces
 import torch
-from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler
-from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
+from huggingface_hub import hf_hub_download
+import os
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
-
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
+# Initialize the pipeline globally
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
+
 @spaces.GPU(duration=190)
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+def infer(prompt, lora_model, 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:
+        try:
+            pipe.load_lora_weights(lora_model)
+        except Exception as e:
+            return None, seed, f"Failed to load LoRA model: {str(e)}"
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
-    image = pipe(
-        prompt = prompt, 
-        width = width,
-        height = height,
-        num_inference_steps = num_inference_steps, 
-        generator = generator,
-        guidance_scale=guidance_scale
-    ).images[0] 
-    return image, seed
- 
+    
+    try:
+        image = pipe(
+            prompt=prompt, 
+            width=width,
+            height=height,
+            num_inference_steps=num_inference_steps, 
+            generator=generator,
+            guidance_scale=guidance_scale
+        ).images[0]
+        
+        # Unload LoRA weights after generation
+        if lora_model:
+            pipe.unload_lora_weights()
+        
+        return image, seed, "Image generated successfully."
+    except Exception as e:
+        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",
+    ["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="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 520px;
@@ -43,15 +64,13 @@ css="""
 """
 
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev]
+        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",
                 show_label=False,
@@ -59,13 +78,17 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-            
             run_button = gr.Button("Run", scale=0)
         
+        lora_model = gr.Text(
+            label="LoRA Model ID (optional)",
+            placeholder="Enter Hugging Face LoRA model ID",
+        )
+        
         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(
                 label="Seed",
                 minimum=0,
@@ -73,11 +96,8 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-            
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
@@ -85,7 +105,6 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
@@ -93,9 +112,7 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-            
             with gr.Row():
-
                 guidance_scale = gr.Slider(
                     label="Guidance Scale",
                     minimum=1,
@@ -103,7 +120,6 @@ with gr.Blocks(css=css) as demo:
                     step=0.1,
                     value=3.5,
                 )
-  
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -113,18 +129,18 @@ with gr.Blocks(css=css) as demo:
                 )
         
         gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
+            examples=examples,
+            fn=infer,
+            inputs=[prompt, lora_model],
+            outputs=[result, seed, output_message],
             cache_examples="lazy"
         )
 
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        fn=infer,
+        inputs=[prompt, lora_model, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed, output_message]
     )
 
 demo.launch()

requirements.txt

@@ -1,6 +1,7 @@
 accelerate
-git+https://github.com/huggingface/diffusers.git
+git+https://github.com/huggingface/diffusers@lora-support-flux
 torch
 transformers==4.42.4
 xformers
-sentencepiece
+sentencepiece
+peft