Update app.py

app.py

@@ -1,46 +1,204 @@
+import gradio as gr
+import numpy as np
+import random
+
+# import spaces #[uncomment to use ZeroGPU]
+from diffusers import DiffusionPipeline, AutoPipelineForImage2Image
+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
+
+# Text-to-image pipeline (same as original)
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = pipe.to(device)
+
+# Image-to-image pipeline — built from `pipe` via from_pipe(), so it reuses the
+# same unet/vae/text-encoders already in memory. No second checkpoint load,
+# no extra VRAM usage.
+pipe_img2img = AutoPipelineForImage2Image.from_pipe(pipe)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+
+# @spaces.GPU #[uncomment to use ZeroGPU]
+def infer(
+    prompt,
+    init_image,
+    negative_prompt,
+    seed,
+    randomize_seed,
+    width,
+    height,
+    guidance_scale,
+    num_inference_steps,
+    strength,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator().manual_seed(seed)
+
+    if init_image is not None:
+        # HYBRID MODE: prompt + reference image -> image-to-image
+        init_image = init_image.convert("RGB").resize((width, height))
+
+        # SDXL Turbo requirement: num_inference_steps * strength must be >= 1,
+        # otherwise the pipeline errors out. Auto-bump steps if needed instead
+        # of crashing.
+        steps = num_inference_steps
+        if steps * strength < 1:
+            steps = max(1, int(np.ceil(1 / max(strength, 1e-3))))
+            gr.Warning(
+                f"Steps bumped to {steps} so that steps × strength ≥ 1 (SDXL Turbo requirement)."
+            )
+
+        image = pipe_img2img(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=init_image,
+            strength=strength,
+            guidance_scale=guidance_scale,
+            num_inference_steps=steps,
+            generator=generator,
+        ).images[0]
+    else:
+        # ORIGINAL MODE: prompt only -> text-to-image
+        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;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(" # Text-to-Image + Image-to-Image (Hybrid)")
+        gr.Markdown(
+            "Upload a reference image below to guide generation with it (image-to-image). "
+            "Leave it empty to generate from the prompt alone (text-to-image)."
+        )
+
+        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")
+
+        with gr.Row():
+            init_image = gr.Image(label="Reference image (optional)", type="pil")
+            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
+                )
+
+            strength = gr.Slider(
+                label="Strength (image-to-image only — higher = further from reference image)",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.05,
+                value=0.5,
+            )
+
+        gr.Examples(examples=examples, inputs=[prompt])
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            prompt,
+            init_image,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            strength,
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch()
 
-from fastapi import FastAPI
-from fastapi.responses import HTMLResponse
-from pydantic import BaseModel
-import json, requests
-app=FastAPI()
-CFG="config.json"
-class Config(BaseModel):
-    base_url:str
-    api_key:str
-    model:str
-class Prompt(BaseModel):
-    prompt:str
-def load():
-    try:
-        return json.load(open(CFG))
-    except:
-        return {"base_url":"https://openrouter.ai/api/v1","api_key":"","model":"google/gemma-3-27b-it"}
-HTML="""<!doctype html><html><body><h2>Simple AI Backend</h2>
-Base URL<br><input id=b size=70><br><br>
-API Key<br><input id=k size=70><br><br>
-Model<br><input id=m size=70><br><br>
-<button onclick='saveCfg()'>Save</button><hr>
-<textarea id=p rows=10 cols=100></textarea><br>
-<button onclick='gen()'>Generate</button><pre id=o></pre>
-<script>
-fetch('/config').then(r=>r.json()).then(c=>{b.value=c.base_url||'';k.value=c.api_key||'';m.value=c.model||'';});
-async function saveCfg(){await fetch('/save',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({base_url:b.value,api_key:k.value,model:m.value})});alert('Saved');}
-async function gen(){o.innerText='Generating...';let r=await fetch('/generate',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({prompt:p.value})});let j=await r.json();o.innerText=j.text||JSON.stringify(j);}
-</script></body></html>"""
-@app.get("/")
-def home(): return HTMLResponse(HTML)
-@app.get("/config")
-def config(): return load()
-@app.post("/save")
-def save(cfg:Config):
-    json.dump(cfg.model_dump(),open(CFG,"w"),indent=2)
-    return {"success":True}
-@app.post("/generate")
-def generate(data:Prompt):
-    c=load()
-    r=requests.post(c["base_url"].rstrip("/")+"/chat/completions",
-    headers={"Authorization":"Bearer "+c["api_key"],"Content-Type":"application/json"},
-    json={"model":c["model"],"messages":[{"role":"user","content":data.prompt}]},timeout=300)
-    r.raise_for_status()
-    return {"text":r.json()["choices"][0]["message"]["content"]}