kolors

scripts/sampleui.py

+import os
+import torch
+import gradio as gr
+# from PIL import Image
+from kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256 import StableDiffusionXLPipeline
+from kolors.models.modeling_chatglm import ChatGLMModel
+from kolors.models.tokenization_chatglm import ChatGLMTokenizer
+from diffusers import UNet2DConditionModel, AutoencoderKL
+from diffusers import EulerDiscreteScheduler
+
+root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+# Initialize global variables for models and pipeline
+text_encoder = None
+tokenizer = None
+vae = None
+scheduler = None
+unet = None
+pipe = None
+
+def load_models():
+    global text_encoder, tokenizer, vae, scheduler, unet, pipe
+
+    if text_encoder is None:
+        ckpt_dir = f'{root_dir}/weights/Kolors'
+
+        # Load the text encoder on CPU (this speeds stuff up 2x)
+        text_encoder = ChatGLMModel.from_pretrained(
+            f'{ckpt_dir}/text_encoder',
+            torch_dtype=torch.float16).to('cpu').half()
+        tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
+
+        # Load the VAE and UNet on GPU
+        vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to('cuda')
+        scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
+        unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to('cuda')
+
+        # Prepare the pipeline
+        pipe = StableDiffusionXLPipeline(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            force_zeros_for_empty_prompt=False)
+        pipe = pipe.to("cuda")
+        pipe.enable_model_cpu_offload()  # Enable offloading to balance CPU/GPU usage
+
+def infer(prompt, use_random_seed, seed, height, width, num_inference_steps, guidance_scale, num_images_per_prompt):
+    load_models()
+
+    if use_random_seed:
+        seed = torch.randint(0, 2**32 - 1, (1,)).item()
+
+    generator = torch.Generator(pipe.device).manual_seed(seed)
+    images = pipe(
+        prompt=prompt,
+        height=height,
+        width=width,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        num_images_per_prompt=num_images_per_prompt,
+        generator=generator
+    ).images
+
+    saved_images = []
+    output_dir = f'{root_dir}/scripts/outputs'
+    os.makedirs(output_dir, exist_ok=True)
+
+    for i, image in enumerate(images):
+        file_path = os.path.join(output_dir, 'sample_test.jpg')
+        base_name, ext = os.path.splitext(file_path)
+        counter = 1
+        while os.path.exists(file_path):
+            file_path = f"{base_name}_{counter}{ext}"
+            counter += 1
+        image.save(file_path)
+        saved_images.append(file_path)
+
+    return saved_images
+
+def gradio_interface():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("## Kolors: Diffusion Model Gradio Interface")
+                prompt = gr.Textbox(label="Prompt")
+                use_random_seed = gr.Checkbox(label="Use Random Seed", value=True)
+                seed = gr.Slider(minimum=0, maximum=2**32 - 1, step=1, label="Seed", randomize=True, visible=False)
+                use_random_seed.change(lambda x: gr.update(visible=not x), use_random_seed, seed)
+                height = gr.Slider(minimum=128, maximum=2048, step=64, label="Height", value=1024)
+                width = gr.Slider(minimum=128, maximum=2048, step=64, label="Width", value=1024)
+                num_inference_steps = gr.Slider(minimum=1, maximum=100, step=1, label="Inference Steps", value=50)
+                guidance_scale = gr.Slider(minimum=1.0, maximum=20.0, step=0.1, label="Guidance Scale", value=5.0)
+                num_images_per_prompt = gr.Slider(minimum=1, maximum=10, step=1, label="Images per Prompt", value=1)
+                btn = gr.Button("Generate Image")
+
+            with gr.Column():
+                output_images = gr.Gallery(label="Output Images", elem_id="output_gallery")
+
+        btn.click(
+            fn=infer,
+            inputs=[prompt, use_random_seed, seed, height, width, num_inference_steps, guidance_scale, num_images_per_prompt],
+            outputs=output_images
+        )
+
+    return demo
+
+if __name__ == '__main__':
+    gradio_interface().launch(server_name='0.0.0.0')

setup.py

+from setuptools import setup, find_packages
+
+setup(
+    name="kolors",
+    version="0.1",
+    author="Kolors",
+    description="The training and inference code for Kolors models.",
+    packages=find_packages(),
+    install_requires=[],
+    dependency_links=[],
+)