`app.py`: img2img

app.py

@@ -69,15 +69,13 @@ class Prodia:
         return response
 
 
-def image_to_base64(image_path):
-    # Open the image with PIL
-    with Image.open(image_path) as image:
-        # Convert the image to bytes
-        buffered = BytesIO()
-        image.save(buffered, format="PNG")  # You can change format to PNG if needed
-        
-        # Encode the bytes to base64
-        img_str = base64.b64encode(buffered.getvalue())
+def image_to_base64(image):
+    # Convert the image to bytes
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")  # You can change format to PNG if needed
+    
+    # Encode the bytes to base64
+    img_str = base64.b64encode(buffered.getvalue())
 
     return img_str.decode('utf-8')  # Convert bytes to string
 
@@ -153,7 +151,6 @@ def send_to_txt2img(image):
         return result
 
 
-
 prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
 model_list = prodia_client.list_models()
 model_names = {}
@@ -162,7 +159,7 @@ for model_name in model_list:
     name_without_ext = remove_id_and_ext(model_name)
     model_names[name_without_ext] = model_name
 
-def flip_text(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
+def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
     result = prodia_client.generate({
         "prompt": prompt,
         "negative_prompt": negative_prompt,
@@ -179,6 +176,25 @@ def flip_text(prompt, negative_prompt, model, steps, sampler, cfg_scale, width,
 
     return job["imageUrl"]
 
+def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
+    result = prodia_client.transform({
+        "imageData": image_to_base64(input_image),
+        "denoising_strength": denoising,
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "model": model,
+        "steps": steps,
+        "sampler": sampler,
+        "cfg_scale": cfg_scale,
+        "width": width,
+        "height": height,
+        "seed": seed
+    })
+
+    job = prodia_client.wait(result)
+
+    return job["imageUrl"]
+
 
 css = """
 #generate {
@@ -186,9 +202,29 @@ css = """
 }
 """
 
-with gr.Blocks(css=css) as demo:
+samplers = [
+    "Euler",
+    "Euler a",
+    "LMS",
+    "Heun",
+    "DPM2",
+    "DPM2 a",
+    "DPM++ 2S a",
+    "DPM++ 2M",
+    "DPM++ SDE",
+    "DPM fast",
+    "DPM adaptive",
+    "LMS Karras",
+    "DPM2 Karras",
+    "DPM2 a Karras",
+    "DPM++ 2S a Karras",
+    "DPM++ 2M Karras",
+    "DPM++ SDE Karras",
+    "DDIM",
+    "PLMS",
+]
 
-    
+with gr.Blocks(css=css) as demo:
     with gr.Row():
         with gr.Column(scale=6):
             model = gr.Dropdown(interactive=True,value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True, label="Stable Diffusion Checkpoint", choices=prodia_client.list_models())
@@ -196,6 +232,7 @@ with gr.Blocks(css=css) as demo:
         with gr.Column(scale=1):
             gr.Markdown(elem_id="powered-by-prodia", value="AUTOMATIC1111 Stable Diffusion Web UI.<br>Powered by [Prodia](https://prodia.com).<br> For more features and faster gen times check out our [API Docs](https://docs.prodia.com/reference/getting-started-guide)")
 
+
     with gr.Tabs() as tabs:
         with gr.Tab("txt2img", id='t2i'):
             with gr.Row():
@@ -251,7 +288,46 @@ with gr.Blocks(css=css) as demo:
                 with gr.Column(scale=2):
                     image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")
     
-            text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output)
+            text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output)
+        
+        with gr.Tab("img2img", id='i2i'):
+            with gr.Row():
+                with gr.Column(scale=6, min_width=600):
+                    i2i_prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3)
+                    i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
+                with gr.Column():
+                    i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate")
+                    
+            with gr.Row():
+                with gr.Column(scale=3):
+                    with gr.Tab("Generation"):
+                        i2i_image_input = gr.Image(type="pil")
+
+                        with gr.Row():
+                            with gr.Column(scale=1):
+                                i2i_sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method", choices=samplers)
+                                
+                            with gr.Column(scale=1):
+                                i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1)
+    
+                        with gr.Row():
+                            with gr.Column(scale=1):
+                                i2i_width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
+                                i2i_height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
+                            
+                            with gr.Column(scale=1):
+                                i2i_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
+                                i2i_batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
+    
+                        i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
+                        i2i_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.7, step=0.1)
+                        i2i_seed = gr.Number(label="Seed", value=-1)
+    
+                    
+                with gr.Column(scale=2):
+                    i2i_image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")
+    
+            i2i_text_button.click(img2img, inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt, model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height, i2i_seed], outputs=i2i_image_output)
         
         with gr.Tab("PNG Info"):
             def plaintext_to_html(text, classname=None):
@@ -289,6 +365,6 @@ with gr.Blocks(css=css) as demo:
             image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output)
             send_to_txt2img_btn.click(send_to_txt2img, inputs=[image_input], outputs=[tabs, prompt, negative_prompt, steps, seed,
                                                                                           model, sampler, width, height, cfg_scale])
-
+            
 demo.queue(concurrency_count=32)
 demo.launch()