Update app.py

app.py

@@ -1,73 +1,40 @@
 import gradio as gr
-import os
-import subprocess
-import zipfile
-import urllib.request
-from huggingface_hub import hf_hub_download
+import torch
+from diffusers import StableDiffusionPipeline
 
 # =========================
-# 1. download sd.cpp binary (only once)
+# load model (once)
 # =========================
-SD_ZIP_URL = "https://github.com/leejet/stable-diffusion.cpp/releases/download/master-586-c97702e/sd-master-c97702e-bin-Linux-Ubuntu-24.04-x86_64.zip"
-SD_DIR = "sdcpp"
-SD_CLI_PATH = os.path.join(SD_DIR, "sd-cli")
+model_id = "OFA-Sys/small-stable-diffusion-v0"
 
-if not os.path.exists(SD_CLI_PATH):
-    print("Downloading stable-diffusion.cpp binary...")
-
-    os.makedirs(SD_DIR, exist_ok=True)
-
-    zip_path = os.path.join(SD_DIR, "sd.zip")
-    urllib.request.urlretrieve(SD_ZIP_URL, zip_path)
-
-    print("Unzipping...")
-    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-        zip_ref.extractall(SD_DIR)
-
-    os.remove(zip_path)
-
-    # make executable
-    os.system(f"chmod +x {SD_CLI_PATH}")
-
-# =========================
-# 2. download GGUF model
-# =========================
-model_path = hf_hub_download(
-    repo_id="stable-diffusion-v1-5/stable-diffusion-v1-5",
-    filename="v1-5-pruned-emaonly.safetensors"
+pipe = StableDiffusionPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float32  # CPU → float32 بهتره
 )
 
-OUTPUT_IMAGE = "output.png"
+pipe = pipe.to("cpu")
 
 # =========================
-# 3. generate function
+# generate function
 # =========================
 def generate(prompt):
 
-    cmd = [
-        SD_CLI_PATH,
-        "-m", model_path,
-        "-p", prompt,
-        "--steps", "8",
-        "--cfg-scale", "7",
-        "-o", OUTPUT_IMAGE
-    ]
-
-    env = os.environ.copy()
-    env["LD_LIBRARY_PATH"] = SD_DIR
-
-    subprocess.run(cmd, check=True, env=env)
+    image = pipe(
+        prompt,
+        num_inference_steps=15,   # سبک و سریع
+        guidance_scale=6.5       # بهتر از 1.0
+    ).images[0]
 
-    return OUTPUT_IMAGE
+    return image
 
 # =========================
-# 4. gradio UI
+# gradio UI
 # =========================
 demo = gr.Interface(
     fn=generate,
     inputs=gr.Textbox(label="Prompt"),
-    outputs=gr.Image(type="filepath"),
-    title="GGUF Text-to-Image"
+    outputs=gr.Image(),
+    title="Small Stable Diffusion (CPU)"
 )
 
 demo.launch()