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| import os | |
| import gradio as gr | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| print(f"Is CUDA available: {torch.cuda.is_available()}") | |
| if torch.cuda.is_available(): | |
| print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}") | |
| pipe_sd = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, revision="fp16", use_auth_token=os.getenv("HUGGING_FACE_HUB_TOKEN")).to("cuda") | |
| pipe_vq = DiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq", torch_dtype=torch.float16).to("cuda") | |
| else: | |
| pipe_sd = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token=os.getenv("HUGGING_FACE_HUB_TOKEN")) | |
| pipe_vq = DiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq") | |
| examples = [ | |
| ["A sketch of a palm tree."], | |
| ["A teddy bear playing in the pool."], | |
| ["A simple wedding cake with lego bride and groom topper and cake pops."], | |
| ["A realistic tree using a mixture of different colored pencils."], | |
| ["Muscular Santa Claus."], | |
| ["A man with a pineapple head."], | |
| ["Pebble tower standing on the left on the sea beach."], | |
| ] | |
| title = "VQ Diffusion vs. Stable Diffusion 1-5" | |
| description = "This demo compares [VQ-Diffusion-ITHQ](https://huggingface.co/microsoft/vq-diffusion-ithq) and [Stable-Diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) for text to image generation." | |
| def inference(text): | |
| output_sd = pipe_sd(text).images[0] | |
| output_vq_diffusion = pipe_vq(text, truncation_rate=1.0).images[0] | |
| return [output_vq_diffusion, output_sd] | |
| io = gr.Interface( | |
| inference, | |
| gr.Textbox(lines=3), | |
| outputs=[ | |
| gr.Image(type="pil", label="VQ-Diffusion"), | |
| gr.Image(type="pil", label="Stable Diffusion"), | |
| ], | |
| title=title, | |
| description=description, | |
| examples=examples | |
| ) | |
| io.launch() | |