Update app.py

app.py

@@ -1,154 +1,84 @@
-import gradio as gr
-import numpy as np
-import random
+import os
+print(f"Loading model: {MODEL_ID} on {DEVICE}")
+if "inpaint" in MODEL_ID or "img2img" in MODEL_ID:
+# اگر مدل مخصوص اینپینت باشه از InpaintPipeline استفاده کن
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+MODEL_ID,
+revision="fp16",
+torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
+use_auth_token=HF_TOKEN if HF_TOKEN else None,
+)
+else:
+pipe = StableDiffusionPipeline.from_pretrained(
+MODEL_ID,
+revision="fp16",
+torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
+use_auth_token=HF_TOKEN if HF_TOKEN else None,
+)
+if DEVICE == "cuda":
+pipe = pipe.to("cuda")
+return pipe
+
+
+pipe = load_pipelines()
+
+
+# توابع تولید / ویرایش
+
+
+def generate_image(prompt: str, negative_prompt: str, steps: int, guidance: float):
+if not prompt:
+return None
+with torch.autocast("cuda") if DEVICE == "cuda" else torch.no_grad():
+out = pipe(prompt=prompt, guidance_scale=guidance, num_inference_steps=steps)
+return out.images[0]
+
+
+
+
+def edit_image(init_image, mask, prompt: str, negative_prompt: str, steps: int, guidance: float):
+if init_image is None:
+return None
+if mask is None:
+# اگر ماسک نبود، از تصویر اولیه به عنوان ماسک استفاده نکن — کاربر باید ماسک بدهد
+return None
+# تبدیل به قالب مورد نیاز
+init_img = init_image.convert("RGB")
+mask_img = mask.convert("L")
+with torch.autocast("cuda") if DEVICE == "cuda" else torch.no_grad():
+out = pipe(prompt=prompt, image=init_img, mask_image=mask_img, guidance_scale=guidance, num_inference_steps=steps)
+return out.images[0]
+
+
+# رابط گریدیو
+with gr.Blocks(title="Prompt Image Editor — JumpLander") as demo:
+gr.Markdown("# Prompt Image Editor — JumpLander (جامپلندر)")
+with gr.Row():
+with gr.Column(scale=2):
+mode = gr.Radio(["Generate", "Edit / Inpaint"], value="Generate", label="Mode")
+prompt = gr.Textbox(lines=3, label="Prompt (پرومپت)")
+negative_prompt = gr.Textbox(lines=2, label="Negative prompt (اختیاری)")
+steps = gr.Slider(minimum=10, maximum=60, step=5, value=28, label="Steps")
+guidance = gr.Slider(minimum=1.0, maximum=20.0, step=0.5, value=7.5, label="Guidance Scale")
+run = gr.Button("Run")
+with gr.Column(scale=3):
+input_image = gr.Image(type="pil", label="Initial image (برای ویرایش)")
+mask_image = gr.Image(type="pil", label="Mask (سفید = ویرایش شود)")
+output = gr.Image(label="Output")
+
+
+def _run(mode, prompt, negative_prompt, steps, guidance, input_image, mask_image):
+try:
+if mode == "Generate":
+return generate_image(prompt, negative_prompt, steps, guidance)
+else:
+return edit_image(input_image, mask_image, prompt, negative_prompt, steps, guidance)
+except Exception as e:
+return Image.new('RGB', (512,512), color=(255,0,0))
 
-# 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
+run.click(_run, inputs=[mode, prompt, negative_prompt, steps, guidance, input_image, mask_image], outputs=[output])
 
-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;
-}
-"""
-
-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,
-        ],
-        outputs=[result, seed],
-    )
 
 if __name__ == "__main__":
-    demo.launch()
+demo.launch()