initial commit

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,12 @@
+---
+title: Koolerspace
+emoji: 📚
+colorFrom: pink
+colorTo: purple
+sdk: gradio
+sdk_version: 5.9.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,45 @@
+# This Gradio app uses the KolorsPipeline from the diffusers library to generate images based on a given prompt.
+import gradio as gr
+import torch
+from diffusers import KolorsPipeline
+
+# Load the KolorsPipeline model
+pipe = KolorsPipeline.from_pretrained(
+    "Kwai-Kolors/Kolors-diffusers", 
+    torch_dtype=torch.float16, 
+    variant="fp16"
+).to("cuda")
+
+# Define the function to generate an image based on the prompt
+def generate_image(prompt, negative_prompt, guidance_scale, num_inference_steps, seed):
+    generator = torch.Generator(pipe.device).manual_seed(seed)
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+    ).images[0]
+    return image
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        prompt_input = gr.Textbox(label="Prompt", value="一张瓢虫的照片，微距，变焦，高质量，电影，拿着一个牌子，写着'可图'")
+        negative_prompt_input = gr.Textbox(label="Negative Prompt", value="")
+    with gr.Row():
+        guidance_scale_slider = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=10.0, value=5.0, step=0.1)
+        num_inference_steps_slider = gr.Slider(label="Number of Inference Steps", minimum=1, maximum=100, value=50, step=1)
+        seed_slider = gr.Slider(label="Seed", minimum=0, maximum=100000, value=66, step=1)
+    generate_button = gr.Button("Generate Image")
+    output_image = gr.Image(label="Generated Image")
+
+    # Define the event listener for the generate button
+    generate_button.click(
+        fn=generate_image,
+        inputs=[prompt_input, negative_prompt_input, guidance_scale_slider, num_inference_steps_slider, seed_slider],
+        outputs=output_image
+    )
+
+# Launch the Gradio app
+demo.launch(show_error=True)

requirements.txt

@@ -0,0 +1,2 @@
+torch
+diffusers