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README.md

@@ -1,12 +1,6 @@
 ---
-title: Stable Diffusion Image Gen
-emoji: 🚀
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 4.42.0
+title: stable_diffusion_image_gen
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 4.41.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,87 @@
+import gradio as gr
+import os
+import io
+import requests, json
+from PIL import Image
+import base64 
+from dotenv import load_dotenv, find_dotenv
+
+
+_ = load_dotenv(find_dotenv()) # read local .env file
+hf_api_key = os.environ['HF_API_KEY']
+
+
+# Text-to-image endpoint
+def get_completion(inputs, parameters=None, ENDPOINT_URL=os.environ['HF_API_TTI_STABILITY_AI']):
+    headers = {
+        "Authorization": f"Bearer {hf_api_key}",
+        "Content-Type": "application/json"
+    }
+    data = {"inputs": inputs}
+    if parameters is not None:
+        data.update({"parameters": parameters})
+
+    response = requests.post(ENDPOINT_URL, headers=headers, data=json.dumps(data))
+    
+    # Check the content type of the response
+    content_type = response.headers.get('Content-Type', '')
+    print(content_type)
+    if 'application/json' in content_type:
+        return json.loads(response.content.decode("utf-8"))
+    elif 'image/' in content_type:
+        return response.content  # return raw image data
+
+    response.raise_for_status()  # raise an error for unexpected content types
+    
+    
+#A helper function to convert the PIL image to base64 
+# so you can send it to the API
+def base64_to_pil(img_base64):
+    base64_decoded = base64.b64decode(img_base64)
+    byte_stream = io.BytesIO(base64_decoded)
+    pil_image = Image.open(byte_stream)
+    return pil_image
+
+def generate(prompt, negative_prompt, steps, guidance, width, height):
+    params = {
+        "negative_prompt": negative_prompt,
+        "num_inference_steps": steps,
+        "guidance_scale": guidance,
+        "width": width,
+        "height": height
+    }
+    
+    output = get_completion(prompt,params)
+    
+    # Check if the output is an image (bytes) or JSON (dict)
+    if isinstance(output, dict):
+        raise ValueError("Expected an image but received JSON: {}".format(output))
+    
+    # If output is raw image data, convert it to a PIL image
+    result_image = Image.open(io.BytesIO(output))
+    return result_image
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Image Generation with stable-diffusion-xl-base-1.0")
+    with gr.Row():
+        with gr.Column(scale=4):
+            prompt = gr.Textbox(label="Your prompt") #Give prompt some real estate
+        with gr.Column(scale=1, min_width=50):
+            btn = gr.Button("Submit") #Submit button side by side!
+    with gr.Accordion("Advanced options", open=False): #Let's hide the advanced options!
+            negative_prompt = gr.Textbox(label="Negative prompt")
+            with gr.Row():
+                with gr.Column():
+                    steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=25,
+                      info="In many steps will the denoiser denoise the image?")
+                    guidance = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, value=7,
+                      info="Controls how much the text prompt influences the result")
+                with gr.Column():
+                    width = gr.Slider(label="Width", minimum=64, maximum=1024, step=64, value=512)
+                    height = gr.Slider(label="Height", minimum=64, maximum=1024, step=64, value=512)
+    output = gr.Image(label="Result") #Move the output up too
+            
+    btn.click(fn=generate, inputs=[prompt,negative_prompt,steps,guidance,width,height], outputs=[output])
+
+gr.close_all()
+demo.launch(share=True)

requirements.txt

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+gradio
+pillow
+python-dotenv