Rename main.py to app.py

app.py

@@ -0,0 +1,33 @@
+import gradio as gr
+import torch
+from PIL import Image
+from diffusers import StableDiffusionImg2ImgPipeline
+
+# Load your model (replace with your own model repo)
+model_id = "brothelsnsprout/Training_image_generator"
+
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16
+).to("cuda")
+
+# Stylize function
+def stylize_image(image: Image.Image, prompt: str):
+    image = image.convert("RGB").resize((512, 512))
+    result = pipe(prompt=prompt, image=image, strength=0.8, guidance_scale=7.5)
+    return result.images[0]
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=stylize_image,
+    inputs=[
+        gr.Image(type="pil", label="Input Image"),
+        gr.Textbox(label="Text Prompt (Style Instruction)", placeholder="e.g. in the style of Studio Ghibli")
+    ],
+    outputs=gr.Image(label="Styled Output"),
+    title="Image-to-Image Styler",
+    description="Upload an image and describe how it should be transformed in style.",
+)
+
+if __name__ == "__main__":
+    interface.launch()

main.py

@@ -1,29 +0,0 @@
-import gradio as gr
-import modin.pandas as pd
-import torch
-import numpy as np
-from PIL import Image
-from diffusers import DiffusionPipeline
-from huggingface_hub import login
-
-#import os
-
-#login(token=os.environ.get('HF_KEY'))
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
-pipe = pipe.to(device)
-# ok
-def infer(prompt, source_image, negative_prompt, guide, steps, seed, Strength):
-    seed = int(seed)
-    generator = torch.Generator(device).manual_seed(seed)
-    if not isinstance(source_image, Image.Image):
-        source_image = Image.open(source_image).convert("RGB")
-    image = pipe(prompt, negative_prompt=negative_prompt, image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps).images[0]
-    return image
-
-gr.Interface(fn=infer, inputs=[gr.Text(label="Prompt"), gr.Image(label="Initial Image", type="pil"), gr.Text(label="Prompt"), gr.Slider(2, 15, value = 7, label = 'Guidance Scale'),
-    gr.Slider(1, 25, value = 10, step = 1, label = 'Number of Iterations'),
-    gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True), 
-    gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)], 
-    outputs='image', title = "Stable Diffusion XL 1.0 Image to Image Pipeline CPU", description = "For more information on Stable Diffusion XL 1.0 see https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0 <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "CuongTran").queue(max_size=5).launch(share=True)