attempt: text to image, diffusion

.gitignore

@@ -0,0 +1,22 @@
+# Pycache
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+
+# virtual env
+venv/
+.env/
+
+# Jupyter Notebook checkpoints
+.ipynb_checkpoints/
+
+# HF space cache files (optional)
+*.log
+*.ckpt
+*.safetensors
+*.pt
+
+# sys file
+.DS_Store
+Thumbs.db

README.md

@@ -1,14 +1,10 @@
----
-title: TextTo3DScene
-emoji: 🐨
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 5.46.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: text to 3D scene
----
+# Text2Image HF Space
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This Space demonstrates text-to-image generation using Stable Diffusion v1-4 + ControlNet.
+
+- CPU compatible for development
+- GPU recommended for faster generation
+- Allows user to control:
+  - Prompt text
+  - Number of inference steps
+  - Output resolution

app.py

@@ -1,7 +1,49 @@
+import torch
+from diffusers import StableDiffusionPipeline, ControlNetModel, StableDiffusionControlNetPipeline
+from PIL import Image
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# 自动选择设备
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# 加载 ControlNet 模型
+controlnet = ControlNetModel.from_pretrained(
+    "lllyasviel/sd-controlnet-canny", torch_dtype=torch.float32
+)
+
+# 加载 Stable Diffusion + ControlNet
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    controlnet=controlnet,
+    torch_dtype=torch.float32
+).to(device)
+
+# CPU 下节省显存
+pipe.enable_attention_slicing()
+
+# 文生图生成函数
+def generate_image(prompt, num_steps=20, height=256, width=256):
+    """
+    prompt: str, 文本描述
+    num_steps: int, 推理步数（CPU 可少一些）
+    height, width: int, 输出图像分辨率
+    """
+    image = pipe(prompt, num_inference_steps=num_steps, height=height, width=width).images[0]
+    return image
+
+# Gradio 界面
+interface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="Enter text prompt here..."),
+        gr.Slider(5, 50, value=20, step=1, label="Inference Steps"),
+        gr.Slider(128, 512, value=256, step=64, label="Height"),
+        gr.Slider(128, 512, value=256, step=64, label="Width"),
+    ],
+    outputs=gr.Image(type="pil"),
+    title="Text2Image Demo (v1-4 + ControlNet)",
+    description="Generate images from text using Stable Diffusion v1-4 + ControlNet (CPU/GPU compatible)"
+)
+
+interface.launch()

requirements.txt

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+torch
+diffusers[torch]
+transformers
+accelerate
+Pillow
+gradio