Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from io import BytesIO
+import requests
+import json
+
+# List of available models
+models = [
+    "HHM29/finetuning_dream_fin",
+    "KappaNeuro/needlepoint",
+    "Norod78/ClaymationX_LoRA",
+    "KappaNeuro/movie-poster",
+    "digiplay/MixTape_RocknRoll_v3punk_bake_fp16",
+    "digiplay/BeautifulFantasyRealMix_diffusers",
+    "Yntec/pineappleAnimeMix",
+    "Yntec/DucHaiten-Retro-Diffusers",
+    "joachimsallstrom/aether-pixel-lora-for-sdxl",
+    "runwayml/stable-diffusion-v1-5",
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    "CompVis/stable-diffusion-v1-4",
+]
+
+def generate_image(model_name, image, prompt, length, temperature, n_samples, use_image2image=False):
+    data = {
+        "image_prompt": image,
+        "prompt": prompt,
+        "length": length,
+        "temperature": temperature,
+        "n_samples": n_samples,
+        "model": model_name,
+    }
+
+    if use_image2image:
+        data["use_image2image"] = True
+        data["image2image_prompt"] = image  # Provide the target image for image2image
+
+    response = requests.post("https://api.stable-diffusion.ml/generate", json=data)
+    response_json = response.json()
+
+    if response.status_code == 200:
+        results = response_json["generated_images"]
+        generated_image = np.frombuffer(BytesIO(results[0]["image"]).read(), dtype=np.uint8)
+        generated_image = generated_image.reshape(results[0]["metadata"]["height"], results[0]["metadata"]["width"], 3)
+        return Image.fromarray(generated_image)
+    else:
+        return None
+
+def app(model=gr.inputs.Selector(options=models), 
+        image=gr.inputs.Image(shape=(None, None)), 
+        prompt=gr.inputs.Textbox(default="an image generated with"),
+        length=gr.inputs.Slider(1, 20, step=1, default=8),
+        temperature=gr.inputs.Slider(0.5, 1.5, step=0.1, default=1),
+        n_samples=gr.inputs.Slider(1, 5, step=1, default=1),
+        use_image2image=gr.inputs.Boolean(default=False)):
+    generated_image = generate_image(model, 
+                                  image=image.data if image else None, 
+                                  prompt=prompt, 
+                                  length=int(length), 
+                                  temperature=float(temperature), 
+                                  n_samples=int(n_samples),
+                                  use_image2image=use_image2image)
+    return gr.outputs.Image(as_pil=True)(generated_image) if generated_image else None
+
+if __name__ == "__main__":
+    title = "Image Generation App"
+    description = "Select a model and customize your image generation or image2image settings!"
+    gradio.launch(app, port=8000, title=title, description=description)