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Update app.py
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app.py
CHANGED
@@ -2,29 +2,37 @@ import gradio as gr
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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 DiffusionPipeline
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# Initialize the DiffusionPipeline model with LoRA weights
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pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
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pipeline.load_lora_weights("ostris/super-cereal-sdxl-lora")
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def text_to_image(prompt):
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generated_img_tensor = output.images[0]
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# Convert torch tensor to numpy array
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generated_img_array = generated_img_tensor.cpu().numpy().transpose((1, 2, 0))
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return generated_img_array
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def create_cereal_box(input_image):
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# Convert the input numpy array to PIL Image
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cover_img = Image.fromarray((input_image.astype(np.uint8)))
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# Load the template image
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template_img = Image.open('CerealBoxMaker/template.jpeg')
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# Simplified cereal box creation logic
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scaling_factor = 1.5
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rect_height = int(template_img.height * 0.32)
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new_width = int(rect_height * 0.70)
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@@ -41,10 +49,7 @@ def create_cereal_box(input_image):
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template_copy = template_img.copy()
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template_copy.paste(cover_resized_scaled, left_position)
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template_copy.paste(cover_resized_scaled, right_position)
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# Convert the PIL Image back to a numpy array
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template_copy_array = np.array(template_copy)
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return template_copy_array
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def combined_function(prompt):
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@@ -53,4 +58,4 @@ def combined_function(prompt):
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return final_img
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# Create a Gradio Interface
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gr.Interface(fn=combined_function, inputs="text", outputs="image").launch()
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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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import random
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from diffusers import DiffusionPipeline
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# Initialize the DiffusionPipeline model with LoRA weights
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pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
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pipeline.load_lora_weights("ostris/super-cereal-sdxl-lora")
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pipeline.to("cuda:0")
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MAX_SEED = np.iinfo(np.int32).max
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def text_to_image(prompt):
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seed = random.randint(0, MAX_SEED)
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negative_prompt = "ugly, blurry, nsfw, gore, blood"
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output = pipeline(
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prompt=prompt,
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negative_prompt=negative_prompt,
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width=1024,
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height=1024,
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guidance_scale=7.0,
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num_inference_steps=25,
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generator=torch.Generator().manual_seed(seed),
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)
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generated_img_tensor = output.images[0]
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generated_img_array = generated_img_tensor.cpu().numpy().transpose((1, 2, 0))
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return generated_img_array
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def create_cereal_box(input_image):
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cover_img = Image.fromarray((input_image.astype(np.uint8)))
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template_img = Image.open('CerealBoxMaker/template.jpeg')
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scaling_factor = 1.5
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rect_height = int(template_img.height * 0.32)
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new_width = int(rect_height * 0.70)
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template_copy = template_img.copy()
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template_copy.paste(cover_resized_scaled, left_position)
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template_copy.paste(cover_resized_scaled, right_position)
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template_copy_array = np.array(template_copy)
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return template_copy_array
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def combined_function(prompt):
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return final_img
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# Create a Gradio Interface
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gr.Interface(fn=combined_function, inputs="text", outputs="image").launch(debug=True)
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