Upload 4 files

Dockerfile

@@ -0,0 +1,20 @@
+# Use the official Python image from the Docker Hub
+FROM python:3.10-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements.txt file into the container
+COPY requirements.txt .
+
+# Install the required Python packages
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code into the container
+COPY app.py .
+
+# Expose the port that Gradio will run on
+EXPOSE 7860
+
+# Run the Gradio app
+CMD ["python", "app.py"]

README.md

@@ -1,8 +1,8 @@
 ---
 title: Text To Image SDXL
-emoji: 😻
-colorFrom: pink
-colorTo: pink
+emoji: 📊
+colorFrom: red
+colorTo: blue
 sdk: gradio
 sdk_version: 4.36.1
 app_file: app.py

app.py

@@ -0,0 +1,74 @@
+import gradio as gr
+import torch
+from transformers import CLIPTextModel, CLIPTokenizer
+from diffusers import StableDiffusionPipeline
+
+# Load the model and tokenizer
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
+pipe = pipe.to("cpu")
+
+def generate_image(prompt, negative_prompt, size):
+    if not prompt:
+        prompt = "a beautiful landscape"
+    if not negative_prompt:
+        negative_prompt = ""
+        
+    width, height = map(int, size.split('x'))
+    generator = torch.Generator("cpu").manual_seed(42)
+    
+    # Generate the image
+    result = pipe(prompt, height=height, width=width, negative_prompt=negative_prompt, generator=generator)
+    
+    if result is not None and 'images' in result:
+        return result.images[0]
+    else:
+        return None
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Text to Image SDXL")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", placeholder="Enter the prompt here...")
+            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter the negative prompt here...")
+            size = gr.Dropdown(choices=["512x512", "768x768", "1024x1024"], value="1024x1024", label="Size")
+            submit = gr.Button("Submit")
+        with gr.Column():
+            output = gr.Image(label="Output")
+
+    submit.click(generate_image, inputs=[prompt, negative_prompt, size], outputs=output)
+
+demo.launch()
+
+# import gradio as gr
+# import torch
+# from transformers import CLIPTextModel, CLIPTokenizer
+# from diffusers import StableDiffusionPipeline
+
+# # Load the model and tokenizer
+# model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+# pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
+# pipe = pipe.to("cpu")
+
+# def generate_image(prompt, negative_prompt, size):
+#     width, height = map(int, size.split('x'))
+#     generator = torch.Generator("cpu").manual_seed(42)
+#     image = pipe(prompt, height=height, width=width, negative_prompt=negative_prompt, generator=generator).images[0]
+#     return image
+
+# with gr.Blocks() as demo:
+#     gr.Markdown("## Text to Image SDXL")
+    
+#     with gr.Row():
+#         with gr.Column():
+#             prompt = gr.Textbox(label="Prompt")
+#             negative_prompt = gr.Textbox(label="Negative Prompt")
+#             size = gr.Dropdown(choices=["512x512", "768x768", "1024x1024"], value="1024x1024", label="Size")
+#             submit = gr.Button("Submit")
+#         with gr.Column():
+#             output = gr.Image(label="Output")
+
+#     submit.click(generate_image, inputs=[prompt, negative_prompt, size], outputs=output)
+
+# demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio
+torch
+transformers
+diffusers
+accelerate