Update app.py

app.py

@@ -1,61 +1,49 @@
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+from diffusers.utils import load_image
+from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
+from diffusers.models.controlnet_flux import FluxControlNetModel
+import random
+import numpy as np
 
+# Initialize models
+base_model = 'black-forest-labs/FLUX.1-dev'
+controlnet_model = 'promeai/FLUX.1-controlnet-lineart-promeai'
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch_dtype)
+pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
 
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
+    control_image_path,
+    controlnet_conditioning_scale,
     guidance_scale,
     num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
+    seed,
+    randomize_seed,
 ):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
+    generator = torch.manual_seed(seed)
+    control_image = load_image(control_image_path) if control_image_path else None
 
-    image = pipe(
+    # Generate image
+    result = pipe(
         prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
+        control_image=control_image,
+        controlnet_conditioning_scale=controlnet_conditioning_scale,
         num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
+        guidance_scale=guidance_scale,
         generator=generator,
     ).images[0]
 
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+    return result, seed
 
 css = """
 #col-container {
@@ -66,89 +54,75 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown("## Zero-shot Partial Style Transfer for Line Art Images, Powered by FLUX.1")
 
         with gr.Row():
-            prompt = gr.Text(
+            prompt = gr.Textbox(
                 label="Prompt",
-                show_label=False,
-                max_lines=1,
                 placeholder="Enter your prompt",
-                container=False,
+                max_lines=1,
             )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
+            run_button = gr.Button("Generate", variant="primary")
 
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
+            control_image = gr.Image(
+                source="upload",
+                type="filepath",
+                label="Control Image (Line Art)"
+            )
+            controlnet_conditioning_scale = gr.Slider(
+                label="ControlNet Conditioning Scale",
+                minimum=0.0,
+                maximum=1.0,
+                value=0.6,
+                step=0.1
+            )
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=1.0,
+                maximum=10.0,
+                value=3.5,
+                step=0.1
+            )
+            num_inference_steps = gr.Slider(
+                label="Number of Inference Steps",
+                minimum=1,
+                maximum=100,
+                value=28,
+                step=1
             )
-
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=0,
+                value=0
             )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
+            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+
+        gr.Examples(
+            examples=[
+                "Anime girl with fennec ears holding a cake",
+                "Victorian style mansion interior with candlelight"
+            ],
+            inputs=[prompt]
+        )
+
+    run_button.click(
+        infer,
         inputs=[
             prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
+            control_image,
+            controlnet_conditioning_scale,
             guidance_scale,
             num_inference_steps,
+            seed,
+            randomize_seed
         ],
-        outputs=[result, seed],
+        outputs=[result, seed]
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()