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import gradio as gr |
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import modin.pandas as pd |
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import torch |
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import numpy as np |
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from PIL import Image |
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from diffusers import AutoPipelineForImage2Image |
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from diffusers.utils import load_image |
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import math |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16) if torch.cuda.is_available() else AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo") |
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pipe = pipe.to(device) |
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def infer(source_img, prompt, steps, seed, Strength): |
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generator = torch.Generator(device).manual_seed(seed) |
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if int(steps * Strength) < 1: |
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steps = math.ceil(1 / max(0.10, Strength)) |
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original_height, original_width, original_channel = np.array(source_img).shape |
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if 1024 * 1024 < original_width * original_height: |
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factor = ((1024 * 1024) / (original_width * original_height))**0.5 |
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process_width = math.floor(original_width * factor) |
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process_height = math.floor(original_height * factor) |
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else: |
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process_width = original_width |
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process_height = original_height |
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if (process_width % 8) != 0 or (process_height % 8) != 0: |
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process_width = process_width - (process_width % 8) |
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process_height = process_height - (process_height % 8) |
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if ((process_width + 8) * (process_height + 8)) <= (1024 * 1024): |
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process_width = process_width + 8 |
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process_height = process_height + 8 |
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source_image = source_img.resize((process_width, process_height)) |
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image = pipe(prompt, image=source_image, strength=Strength, guidance_scale=0.0, num_inference_steps=steps, width = process_width, height = process_height).images[0] |
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output_image = image.resize((original_width, original_height)) |
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return output_image |
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gr.Interface(fn=infer, inputs=[ |
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gr.Image(sources=["upload", "webcam", "clipboard"], type = "pil", label="Raw Image."), |
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gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'), |
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gr.Slider(1, 5, value = 2, step = 1, label = 'Number of Iterations'), |
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gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True), |
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gr.Slider(label='Strength', minimum = 0.1, maximum = 1, step = .05, value = .5)], |
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outputs='image', title = "Stable Diffusion XL Turbo Image to Image Pipeline CPU", description = "For more information on Stable Diffusion XL Turbo see https://huggingface.co/stabilityai/sdxl-turbo <br><br>Upload an Image, Use your Cam, or Paste an Image. Then enter a Prompt, or let it just do its Thing, then click submit. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", |
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article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=10).launch() |
