test

app.py

@@ -1,28 +1,174 @@
-from transformers import pipeline, Conversation
+#from transformers import pipeline, Conversation
+#import gradio as gr
+
+#chatbot = pipeline(model="llava-hf/LLaVA-NeXT-Video-7B-hf")
+#message_list = []
+#response_list = []
+
+#def vanilla_chatbot(message, history, file=None):
+#    if file is not None:
+        # Handle file processing here
+#        file_content = file.read()
+#        message += f"\n\nFile content:\n{file_content.decode('utf-8')}"
+    
+#    conversation = Conversation(text=message, past_user_inputs=message_list, generated_responses=response_list)
+#    conversation = chatbot(conversation)
+
+#    return conversation.generated_responses[-1]
+
+#chatbot_interface = gr.Interface(
+#    fn=vanilla_chatbot,
+#    inputs=[gr.inputs.Textbox(lines=2, placeholder="Enter your message here..."), gr.inputs.File(optional=True)],
+#    outputs="text",
+#    title="Vanilla Chatbot",
+#    description="Enter text to start chatting or upload a file."
+#)
+
+#chatbot_interface.launch()
+
 import gradio as gr
+import numpy as np
+import random
+from diffusers import DiffusionPipeline
+import torch
 
-chatbot = pipeline(model="llms-lab/LLaVA-NeXT-Video-32BQwen")
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-message_list = []
-response_list = []
+if torch.cuda.is_available():
+    torch.cuda.max_memory_allocated(device=device)
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe.enable_xformers_memory_efficient_attention()
+    pipe = pipe.to(device)
+else: 
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
+    pipe = pipe.to(device)
 
-def vanilla_chatbot(message, history, file=None):
-    if file is not None:
-        # Handle file processing here
-        file_content = file.read()
-        message += f"\n\nFile content:\n{file_content.decode('utf-8')}"
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+        
+    generator = torch.Generator().manual_seed(seed)
+    
+    image = pipe(
+        prompt = prompt, 
+        negative_prompt = negative_prompt,
+        guidance_scale = guidance_scale, 
+        num_inference_steps = num_inference_steps, 
+        width = width, 
+        height = height,
+        generator = generator
+    ).images[0] 
+    
+    return image
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "A dog named Kendrick riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+if torch.cuda.is_available():
+    power_device = "GPU"
+else:
+    power_device = "CPU"
+
+with gr.Blocks(css=css) as demo:
     
-    conversation = Conversation(text=message, past_user_inputs=message_list, generated_responses=response_list)
-    conversation = chatbot(conversation)
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""
+        # Text-to-Image Gradio Template
+        Currently running on {power_device}.
+        """)
+        
+        with gr.Row():
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
+            run_button = gr.Button("Run", scale=0)
+        
+        result = gr.Image(label="Result", show_label=False)
 
-    return conversation.generated_responses[-1]
+        with gr.Accordion("Advanced Settings", open=False):
+            
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=False,
+            )
+            
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+            
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            
+            with gr.Row():
+                
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
+                
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
+            
+            with gr.Row():
+                
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=0.0,
+                )
+                
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=12,
+                    step=1,
+                    value=2,
+                )
+        
+        gr.Examples(
+            examples = examples,
+            inputs = [prompt]
+        )
 
-chatbot_interface = gr.Interface(
-    fn=vanilla_chatbot,
-    inputs=[gr.inputs.Textbox(lines=2, placeholder="Enter your message here..."), gr.inputs.File(optional=True)],
-    outputs="text",
-    title="Vanilla Chatbot",
-    description="Enter text to start chatting or upload a file."
-)
+    run_button.click(
+        fn = infer,
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result]
+    )
 
-chatbot_interface.launch()
+demo.queue().launch()