Update app.py

app.py

@@ -88,6 +88,134 @@ def load_img(resize_width,img: str):
     img = img.resize((resize_width, resize_height), Image.Resampling.LANCZOS)
     return img, resize_width, resize_height
 
+@spaces.GPU
+def example_generate(
+    prompt: str,
+    negative_prompt: str = "",
+    seed: int = 0,
+    custom_width: int = 1024,
+    custom_height: int = 1024,
+    guidance_scale: float = 7.0,
+    num_inference_steps: int = 28,
+    sampler: str = "Euler a",
+    aspect_ratio_selector: str = "896 x 1152",
+    style_selector: str = "(None)",
+    quality_selector: str = "Standard v3.1",
+    use_upscaler: bool = False,
+    upscaler_strength: float = 0.55,
+    upscale_by: float = 1.5,
+    add_quality_tags: bool = True,
+    progress=gr.Progress(track_tqdm=True),
+):
+    generator = utils.seed_everything(seed)
+
+    width, height = utils.aspect_ratio_handler(
+        aspect_ratio_selector,
+        custom_width,
+        custom_height,
+    )
+
+    prompt = utils.add_wildcard(prompt, wildcard_files)
+
+    prompt, negative_prompt = utils.preprocess_prompt(
+        quality_prompt, quality_selector, prompt, negative_prompt, add_quality_tags
+    )
+    prompt, negative_prompt = utils.preprocess_prompt(
+        styles, style_selector, prompt, negative_prompt
+    )
+
+    width, height = utils.preprocess_image_dimensions(width, height)
+
+    backup_scheduler = pipe.scheduler
+    pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler)
+
+    if use_upscaler:
+        upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components)
+    metadata = {
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "resolution": f"{width} x {height}",
+        "guidance_scale": guidance_scale,
+        "num_inference_steps": num_inference_steps,
+        "seed": seed,
+        "sampler": sampler,
+        "sdxl_style": style_selector,
+        "add_quality_tags": add_quality_tags,
+        "quality_tags": quality_selector,
+    }
+
+    if use_upscaler:
+        new_width = int(width * upscale_by)
+        new_height = int(height * upscale_by)
+        metadata["use_upscaler"] = {
+            "upscale_method": "nearest-exact",
+            "upscaler_strength": upscaler_strength,
+            "upscale_by": upscale_by,
+            "new_resolution": f"{new_width} x {new_height}",
+        }
+    else:
+        metadata["use_upscaler"] = None
+        metadata["Model"] = {
+            "Model": DESCRIPTION,
+            "Model hash": "e3c47aedb0",
+        }
+    
+    logger.info(json.dumps(metadata, indent=4))
+
+    try:
+        if use_upscaler:
+            latents = pipe(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                width=width,
+                height=height,
+                guidance_scale=guidance_scale,
+                num_inference_steps=num_inference_steps,
+                generator=generator,
+                output_type="latent",
+            ).images
+            upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by)
+            images = upscaler_pipe(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                image=upscaled_latents,
+                guidance_scale=guidance_scale,
+                num_inference_steps=num_inference_steps,
+                strength=upscaler_strength,
+                generator=generator,
+                output_type="pil",
+            ).images
+        else:
+            images = pipe(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                width=width,
+                height=height,
+                guidance_scale=guidance_scale,
+                num_inference_steps=num_inference_steps,
+                generator=generator,
+                output_type="pil",
+            ).images
+
+        if images:
+            image_paths = [
+                utils.save_image(image, metadata, OUTPUT_DIR, IS_COLAB)
+                for image in images
+            ]
+
+            for image_path in image_paths:
+                logger.info(f"Image saved as {image_path} with metadata")
+
+        return image_paths, metadata
+    except Exception as e:
+        logger.exception(f"An error occurred: {e}")
+        raise
+    finally:
+        if use_upscaler:
+            del upscaler_pipe
+        pipe.scheduler = backup_scheduler
+        utils.free_memory()
+
 @spaces.GPU
 def generate(
     prompt: str,
@@ -457,7 +585,7 @@ with gr.Blocks(css="style.css", theme="NoCrypt/[email protected]") as demo:
                 examples=config.examples,
                 inputs=prompt,
                 outputs=[result, gr_metadata],
-                fn=lambda *args, **kwargs: fake_generate(*args, use_upscaler=True, **kwargs),
+                fn=lambda *args, **kwargs: example_generate(*args, use_upscaler=True, **kwargs),
                 cache_examples=CACHE_EXAMPLES,
             )
     use_upscaler.change(