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from diffusers import LDMTextToImagePipeline |
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import gradio as gr |
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import PIL.Image |
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import numpy as np |
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import random |
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import torch |
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ldm_pipeline = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256") |
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def predict(prompt, steps=100, seed=42, guidance_scale=6.0): |
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torch.cuda.empty_cache() |
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generator = torch.manual_seed(seed) |
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images = ldm_pipeline([prompt], generator=generator, num_inference_steps=steps, eta=0.3, guidance_scale=guidance_scale)["sample"] |
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return images[0] |
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random_seed = random.randint(0, 2147483647) |
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gr.Interface( |
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predict, |
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inputs=[ |
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gr.inputs.Textbox(label='Prompt', default='a chalk pastel drawing of a llama wearing a wizard hat'), |
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gr.inputs.Slider(1, 100, label='Inference Steps', default=50, step=1), |
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gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1), |
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gr.inputs.Slider(1.0, 20.0, label='Guidance Scale - how much the prompt will influence the results', default=6.0, step=0.1), |
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], |
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outputs=gr.Image(shape=[256,256], type="pil", elem_id="output_image"), |
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|
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title="ldm-text2im-large-256 - 🧨 diffusers library", |
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description="This Spaces contains a text-to-image Latent Diffusion process for the <a href=\"https://huggingface.co/CompVis/ldm-text2im-large-256\">ldm-text2im-large-256</a> model by <a href=\"https://huggingface.co/CompVis\">CompVis</a> using the <a href=\"https://github.com/huggingface/diffusers\">diffusers library</a>. The goal of this demo is to showcase the diffusers library and you can check how the code works here. If you want the state-of-the-art experience with Latent Diffusion text-to-image check out the <a href=\"https://huggingface.co/spaces/multimodalart/latentdiffusion\">main Spaces</a>.", |
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).launch() |
