Create app.py

app.py

@@ -0,0 +1,94 @@
+import os
+os.system("pip uninstall torchvision -y")
+os.system("pip install torchvision --force-reinstall --no-cache-dir")
+
+import torch
+from diffusers import AutoPipelineForText2Image
+import gradio as gr
+from PIL import Image
+import spaces
+
+pipe = AutoPipelineForText2Image.from_pretrained(
+    "ostris/Flex.2-preview",
+    custom_pipeline="Flex.2-preview/pipeline.py",
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+@spaces.GPU
+def generate_image(
+    prompt: str,
+    inpaint_img: Image.Image,
+    inpaint_mask: Image.Image,
+    control_img: Image.Image,
+    height: int,
+    width: int,
+    guidance_scale: float,
+    num_inference_steps: int,
+    seed: int,
+    control_strength: float,
+    control_stop: float,
+):
+    gen = torch.Generator(device="cuda").manual_seed(seed)
+
+    inp_img = inpaint_img.convert("RGB")
+    inp_mask = inpaint_mask.convert("RGB")
+    ctrl_img = control_img.convert("RGB")
+
+    result = pipe(
+        prompt=prompt,
+        inpaint_image=inp_img,
+        inpaint_mask=inp_mask,
+        control_image=ctrl_img,
+        control_strength=control_strength,
+        control_stop=control_stop,
+        height=height,
+        width=width,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=gen,
+    )
+    return result.images[0]
+
+with gr.Blocks(title="Flex.2-preview Image Generator") as demo:
+    gr.Markdown("# Flex.2-preview Text→Image Generator")
+
+    prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt...", lines=2)
+
+    with gr.Row():
+        inpaint_img = gr.Image(type="pil", label="Inpaint Image")
+        inpaint_mask = gr.Image(type="pil", label="Inpaint Mask")
+        control_img = gr.Image(type="pil", label="Control Image")
+
+    with gr.Row():
+        height = gr.Slider(64, 2048, value=512, step=64, label="Height")
+        width = gr.Slider(64, 2048, value=512, step=64, label="Width")
+    with gr.Row():
+        guidance_scale = gr.Slider(0.0, 20.0, value=3.5, step=0.1, label="Guidance Scale")
+        num_inference_steps = gr.Slider(1, 100, value=50, step=1, label="Inference Steps")
+    seed = gr.Number(value=42, precision=0, label="Random Seed")
+    control_strength = gr.Slider(0.0, 1.0, value=0.0, step=0.01, label="Control Strength")
+    control_stop = gr.Slider(0.0, 1.0, value=1.0, step=0.01, label="Control Stop")
+
+    generate_btn = gr.Button("Generate")
+    output = gr.Image(type="pil", label="Generated Image")
+
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[
+            prompt,
+            inpaint_img,
+            inpaint_mask,
+            control_img,
+            height,
+            width,
+            guidance_scale,
+            num_inference_steps,
+            seed,
+            control_strength,
+            control_stop,
+        ],
+        outputs=[output],
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)