Update app.py

app.py

@@ -1,154 +1,162 @@
+# -*- coding: utf-8 -*-
+# ZenCtrl Inpainting Playground (Baseten backend)
+
+import os, json, base64, requests
+from io import BytesIO
+from PIL import Image, ImageDraw
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-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
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, 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,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        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",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
+
+# ────────── Secrets & endpoints ──────────
+BASETEN_MODEL_URL = "https://app.baseten.co/models/YOUR_MODEL_ID/predict" 
+BASETEN_API_KEY = os.getenv("BASETEN_API_KEY")
+REPLICATE_TOKEN = os.getenv("REPLICATE_API_TOKEN")
+
+from florence_sam.detect_and_segment import fill_detected_bboxes
+
+# ────────── Globals ──────────
+ADAPTER_NAME = "inpaint"
+ADAPTER_SIZE = 1024
+model_config = dict(union_cond_attn=True, add_cond_attn=False,
+                    latent_lora=False, independent_condition=False)
+css = "#col-container {margin:0 auto; max-width:960px;}"
+
+#Background prompt via Replicate 
+def _gen_bg(prompt: str):
+    url = replicate.run(
+        "google/imagen-4-fast",
+        input={"prompt": prompt or "cinematic background", "aspect_ratio": "1:1"},
+    )
+    url = url[0] if isinstance(url, list) else url
+    return Image.open(BytesIO(requests.get(url, timeout=120).content)).convert("RGB")
+
+# Core generation
+def process_image_and_text(subject_image, adapter_dict, prompt, use_detect, detect_prompt, size=ADAPTER_SIZE, rank=10.0):
+    seed, guidance_scale, steps = 42, 2.5, 28
+
+    if use_detect:
+        base_img = adapter_dict["image"] if isinstance(adapter_dict, dict) else adapter_dict
+        if base_img is None:
+            raise gr.Error("Upload a background image first.")
+        adapter_image, _ = fill_detected_bboxes(
+            image=base_img, text=detect_prompt,
+            inflate_pct=0.15, fill_color="#00FF00"
+        )
+    else:
+        adapter_image = adapter_dict["image"] if isinstance(adapter_dict, dict) else adapter_dict
+        if isinstance(adapter_dict, dict) and adapter_dict.get("mask") is not None:
+            m = adapter_dict["mask"].convert("L").point(lambda p: 255 if p else 0)
+            if bbox := m.getbbox():
+                rect = Image.new("L", m.size, 0)
+                ImageDraw.Draw(rect).rectangle(bbox, fill=255)
+                m = rect
+            green = Image.new("RGB", adapter_image.size, "#00FF00")
+            adapter_image = Image.composite(green, adapter_image, m)
+
+    def prep(img: Image.Image):
+        w, h = img.size
+        m = min(w, h)
+        return img.crop(((w-m)//2, (h-m)//2, (w+m)//2, (h+m)//2)).resize((size, size), Image.LANCZOS)
+
+    subj_proc = prep(subject_image)
+    adap_proc = prep(adapter_image)
+
+    def b64(img):
+        buf = BytesIO(); img.save(buf, format="PNG")
+        return base64.b64encode(buf.getvalue()).decode()
+
+    payload = {
+        "prompt": prompt,
+        "subject_image": b64(subj_proc),
+        "adapter_image": b64(adap_proc),
+        "height": size, "width": size,
+        "steps": steps, "seed": seed,
+        "guidance_scale": guidance_scale, "rank": rank,
+    }
+
+    headers = {"Content-Type": "application/json"}
+    if BASETEN_API_KEY:
+        headers["Authorization"] = f"Api-Key {BASETEN_API_KEY}"
+
+    resp = requests.post(BASETEN_MODEL_URL, headers=headers, json=payload, timeout=120)
+    resp.raise_for_status()
+
+    if resp.headers.get("content-type", "").startswith("image/"):
+        raw_img = Image.open(BytesIO(resp.content))
+    else:
+        url = resp.json().get("image_url")
+        if not url:
+            raise gr.Error("Baseten response missing image data.")
+        raw_img = Image.open(BytesIO(requests.get(url, timeout=120).content))
+
+    return [[raw_img]], raw_img
+
+# ────────── Header HTML ──────────
+header_html = """
+<h1>ZenCtrl Inpainting</h1>
+<div align="center" style="line-height:1;">
+  <a href="https://discord.com/invite/b9RuYQ3F8k" target="_blank" style="margin:10px;">
+    <img src="https://img.shields.io/badge/Discord-Join-7289da.svg?logo=discord" alt="Discord">
+  </a>
+  <a href="https://fotographer.ai/zen-control" target="_blank" style="margin:10px;">
+    <img src="https://img.shields.io/badge/Website-Landing_Page-blue" alt="LP">
+  </a>
+  <a href="https://x.com/FotographerAI" target="_blank" style="margin:10px;">
+    <img src="https://img.shields.io/twitter/follow/FotographerAI?style=social" alt="X">
+  </a>
+</div>
 """
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, 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,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                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,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
+# ────────── Gradio UI ──────────
+with gr.Blocks(css=css, title="ZenCtrl Playground") as demo:
+    raw_state = gr.State()
+
+    gr.HTML(header_html)
+    gr.Markdown("""
+**Generate context-aware images of your subject with ZenCtrl’s inpainting playground.**  
+Upload a subject + optional mask, write a prompt, and hit **Generate**.  
+Open *Advanced Settings* to fetch an AI-generated background.
+""")
+
+    with gr.Row():
+        with gr.Column(scale=2, elem_id="col-container"):
+            subj_img = gr.Image(type="pil", label="Subject image")
+            ref_img  = gr.Image(type="pil", label="Background / Mask image", tool="sketch", brush_color="#00FF00", sources=["upload", "clipboard"])
+            use_detect_ck = gr.Checkbox(False, label="Detect with Florence-SAM")
+            detect_box    = gr.Textbox(label="Detection prompt", value="person, chair", visible=False)
+            promptbox     = gr.Textbox(label="Generation prompt", value="furniture", lines=2)
+            run_btn       = gr.Button("Generate", variant="primary")
+
+            with gr.Accordion("Advanced Settings", open=False):
+                bgprompt = gr.Textbox(label="Background Prompt", value="Scandinavian living room …")
+                bg_btn   = gr.Button("Generate BG")
+
+        with gr.Column(scale=2):
+            gallery = gr.Gallery(columns=[1], rows=[1], object_fit="contain", height="auto")
+            bg_img  = gr.Image(label="Background", visible=False)
+
+    gr.Examples(
+        examples=[
+            ["examples/subject1.png", "examples/bg1.png", "Make the toy sit on a marble table", "examples/out1.png"],
+            ["examples/subject2.png", "examples/bg2.png", "Turn the flowers into sunflowers", "examples/out2.png"],
+            ["examples/subject3.png", "examples/bg3.png", "Make this monster ride a skateboard on the beach", "examples/out3.png"],
+            ["examples/subject4.png", "examples/bg4.png", "Make this cat happy", "examples/out4.png"],
         ],
-        outputs=[result, seed],
+        inputs=[subj_img, ref_img, promptbox],
+        outputs=[gallery],
+        fn=process_image_and_text,
+        examples_per_page="all",
+        label="Presets (Input · Background · Prompt · Output)",
+        cache_examples="lazy"
     )
 
-if __name__ == "__main__":
-    demo.launch()
+    run_btn.click(
+        process_image_and_text,
+        inputs=[subj_img, ref_img, promptbox, use_detect_ck, detect_box],
+        outputs=[gallery, raw_state]
+    )
+
+    bg_btn.click(_gen_bg, inputs=[bgprompt], outputs=[bg_img])
+    use_detect_ck.change(lambda v: gr.update(visible=v), inputs=use_detect_ck, outputs=detect_box)
+
+# ────────── Launch ──────────
+demo.launch(show_api=False, share=True)