Update app.py

app.py

@@ -10,6 +10,32 @@ from PIL import Image
 import re
 
 
+def query(payload, model):
+    HF_TOKEN = os.getenv("HF_TOKEN")
+    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+    url = "https://api-inference.huggingface.co/models/"
+    API_URL = f"{url}{model}"
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.content
+
+def hf_inference(prompt, negative, model, steps, sampler, guidance, width, height, seed):
+    image_bytes = query(payload={
+        "inputs": f"{prompt}",
+        "parameters": {
+            "negative_prompt": f"{negative}",
+            "num_inference_steps": steps,
+            "guidance_scale": guidance,
+            "width": width, "height": height,
+            "seed": seed,
+        },
+    }, model=model)
+    image = Image.open(io.BytesIO(image_bytes))
+    return image
+
+
+
+
+
 
 class Prodia:
     def __init__(self, api_key, base=None):
@@ -17,19 +43,19 @@ class Prodia:
         self.headers = {
             "X-Prodia-Key": api_key
         }
-    
+
     def generate(self, params):
         response = self._post(f"{self.base}/sd/generate", params)
         return response.json()
-    
+
     def transform(self, params):
         response = self._post(f"{self.base}/sd/transform", params)
         return response.json()
-    
+
     def controlnet(self, params):
         response = self._post(f"{self.base}/sd/controlnet", params)
         return response.json()
-    
+
     def get_job(self, job_id):
         response = self._get(f"{self.base}/job/{job_id}")
         return response.json()
@@ -76,7 +102,7 @@ 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())
 
@@ -100,11 +126,11 @@ def get_data(text):
         'negative_prompt': r'Negative prompt: (.*)',
         'steps': r'Steps: (\d+),',
         'seed': r'Seed: (\d+),',
-        'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)', 
+        'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)',
         'model': r'Model:\s*([^\s,]+)',
         'cfg_scale': r'CFG scale:\s*([\d\.]+)',
         'size': r'Size:\s*([0-9]+x[0-9]+)'
-        }
+    }
     for key in ['prompt', 'negative_prompt', 'steps', 'seed', 'sampler', 'model', 'cfg_scale', 'size']:
         match = re.search(patterns[key], text)
         if match:
@@ -120,18 +146,20 @@ def get_data(text):
         results['h'] = None
     return results
 
+
 def send_to_img2img_def(image):
     return image
 
-def send_to_txt2img(image):
 
+def send_to_txt2img(image):
     result = {tabs: gr.update(selected="t2i")}
 
     try:
         text = image.info['parameters']
         data = get_data(text)
         result[prompt] = gr.update(value=data['prompt'])
-        result[negative_prompt] = gr.update(value=data['negative_prompt']) if data['negative_prompt'] is not None else gr.update()
+        result[negative_prompt] = gr.update(value=data['negative_prompt']) if data[
+                                                                                  'negative_prompt'] is not None else gr.update()
         result[steps] = gr.update(value=int(data['steps'])) if data['steps'] is not None else gr.update()
         result[seed] = gr.update(value=int(data['seed'])) if data['seed'] is not None else gr.update()
         result[cfg_scale] = gr.update(value=float(data['cfg_scale'])) if data['cfg_scale'] is not None else gr.update()
@@ -206,59 +234,60 @@ css = """
 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())
-  
-        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 generation times check out our [API Docs](https://docs.prodia.com/reference/getting-started-guide).")
+            model = gr.Dropdown(interactive=True, value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True,
+                                label="Stable Diffusion Checkpoint", choices=prodia_client.list_models())
 
     with gr.Tabs() as tabs:
         with gr.Tab("txt2img", id='t2i'):
             with gr.Row():
                 with gr.Column(scale=6, min_width=600):
-                    prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3)
-                    negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
+                    prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k",
+                                        placeholder="Prompt", show_label=False, lines=3)
+                    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():
                     text_button = gr.Button("Generate", variant='primary', elem_id="generate")
-                    
+
             with gr.Row():
                 with gr.Column(scale=3):
                     with gr.Tab("Generation"):
                         with gr.Row():
                             with gr.Column(scale=1):
-                                sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
-                                
+                                sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method",
+                                                      choices=prodia_client.list_samplers())
+
                             with gr.Column(scale=1):
                                 steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)
-    
+
                         with gr.Row():
                             with gr.Column(scale=1):
                                 width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
                                 height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
-                            
+
                             with gr.Column(scale=1):
                                 batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
                                 batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
-    
-                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
+
+                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=8, step=1)
                         seed = gr.Number(label="Seed", value=-1)
 
                 with gr.Column(scale=2):
-                    image_output = gr.Image(show_label=False, type="filepath")
+                    image_output = gr.Image(show_label=False, type="filepath", interactive=False)
                     send_to_img2img = gr.Button(value="Send to img2img")
 
-            
-    
             text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height,
                                                seed], outputs=image_output, concurrency_limit=64)
-        
+
         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")
+                    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"):
@@ -266,8 +295,9 @@ with gr.Blocks(css=css) as demo:
 
                         with gr.Row():
                             with gr.Column(scale=1):
-                                i2i_sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
-                                
+                                i2i_sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method",
+                                                          choices=prodia_client.list_samplers())
+
                             with gr.Column(scale=1):
                                 i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)
 
@@ -275,32 +305,33 @@ with gr.Blocks(css=css) as demo:
                             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(show_label=False, type="filepath")
-    
+                    i2i_image_output = gr.Image(show_label=False, type="filepath", interactive=False)
+
             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, concurrency_limit=64)
         send_to_img2img.click(send_to_img2img_def, inputs=image_output, outputs=i2i_image_input)
+
         with gr.Tab("PNG Info"):
             def plaintext_to_html(text, classname=None):
                 content = "<br>\n".join(html.escape(x) for x in text.split('\n'))
-    
+
                 return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"
-    
-    
+
+
             def get_exif_data(image):
                 items = image.info
-    
+
                 info = ''
                 for key, text in items.items():
                     info += f"""
@@ -308,26 +339,66 @@ with gr.Blocks(css=css) as demo:
                     <p><b>{plaintext_to_html(str(key))}</b></p>
                     <p>{plaintext_to_html(str(text))}</p>
                     </div>
-                    """.strip()+"\n"
-    
+                    """.strip() + "\n"
+
                 if len(info) == 0:
                     message = "Nothing found in the image."
                     info = f"<div><p>{message}<p></div>"
-    
+
                 return info
-    
+
+
             with gr.Row():
                 with gr.Column():
                     image_input = gr.Image(type="pil")
-                    
+
                 with gr.Column():
                     exif_output = gr.HTML(label="EXIF Data")
                     send_to_txt2img_btn = gr.Button("Send to txt2img")
-    
+
             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],
                                       concurrency_limit=64)
+        with gr.Tab("HuggingFace Inference"):
+            with gr.Row():
+                hf_model = gr.Dropdown(label="HuggingFace checkpoint", choices=["runwayml/stable-diffusion-v1-5", "stabilityai/stable-diffusion-2-1", "dataautogpt3/OpenDalleV1.1", "CompVis/stable-diffusion-v1-4", "playgroundai/playground-v2-1024px-aesthetic", "prompthero/openjourney", "openskyml/dreamdrop-v1", "SG161222/Realistic_Vision_V1.4", "digiplay/AbsoluteReality_v1.8.1", "openskyml/dalle-3-xl", "Lykon/dreamshaper-7", "Pclanglais/Mickey-1928"], value="runwayml/stable-diffusion-v1-5", allow_custom_value=False, interactive=True)
+            with gr.Row():
+                with gr.Column(scale=6, min_width=600):
+                    hf_prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k",
+                                        placeholder="Prompt", show_label=False, lines=3)
+                    hd_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():
+                    hf_text_button = gr.Button("Generate with HF", variant='primary', elem_id="generate")
+
+            with gr.Row():
+                with gr.Column(scale=3):
+                    with gr.Tab("Generation"):
+                        with gr.Row():
+
+                            with gr.Column(scale=1):
+                                hf_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)
+
+                        with gr.Row():
+                            with gr.Column(scale=1):
+                                hf_width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
+                                hf_height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
+
+                            with gr.Column(scale=1):
+                                hf_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
+                                hf_batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
+
+                        hf_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=8, step=1)
+                        hf_seed = gr.Number(label="Seed", value=-1)
+
+                with gr.Column(scale=2):
+                    hf_image_output = gr.Image(show_label=False, type="filepath", interactive=False)
+                    #hf_send_to_img2img = gr.Button(value="Send to img2img")
+
+            hf_text_button.click(hf_inference, inputs=[hf_prompt, hf_negative_prompt, hf_model, hf_steps, hf_sampler, hf_cfg_scale, hf_width, hf_height,
+                                               hf_seed], outputs=hf_image_output, concurrency_limit=64)
+            
 
 demo.queue(max_size=80, api_open=False).launch(max_threads=256, show_api=False)