feat: 動かなかったのでデモをマルコピしてきた

app.py

@@ -1,26 +1,124 @@
-import spaces
-import transformers
+#Lisence: Apache 2.0
 import gradio as gr
+import numpy as np
+import random
+from diffusers import DiffusionPipeline
+import torch
+import spaces
 
-def greet(name):
-    return "Hello " + name + "!!"
+pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+pipe = pipe.to("cuda")
 
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
 
 @spaces.GPU
-def infer(input_text: str = "Who are you?"):
+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
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
 
-    # messages = [
-    #     {"role": "user", "content": name},
-    # ]
-    model = transformers.AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
-    token = transformers.AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
-    token.encode(input_text, return_tensors="pt" )
-    output = model(input_ids)
-    print(output)
-    return output
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""
+        # Text-to-Image Gradio Template
+        """)
+        
+        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)
 
+        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,
+                )
 
-text_input = gr.Textbox(label="Input Text", placeholder="test")
+    run_button.click(
+        fn = infer,
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result]
+    )
 
-demo = gr.Interface(fn=infer, inputs="text", outputs="text")
-demo.launch()
+demo.queue().launch()

app_py.old

@@ -0,0 +1,28 @@
+# import spaces
+# import transformers
+# import gradio as gr
+
+# def greet(name):
+#     return "Hello " + name + "!!"
+
+
+# # @spaces.GPU
+# # def infer(input_text: str = "Who are you?"):
+
+# #     # messages = [
+# #     #     {"role": "user", "content": name},
+# #     # ]
+# #     model = transformers.AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
+# #     token = transformers.AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
+# #     input_ids = token.encode(input_text, return_tensors="pt" )
+# #     output = model(input_ids)
+# #     print(output)
+# #     return output
+
+# @spaces.GPU
+# def infer_demo(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+
+# text_input = gr.Textbox(label="Input Text", placeholder="test")
+
+# demo = gr.Interface(fn=infer, inputs="text", outputs="text")
+# demo.launch()