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Update app.py
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app.py
CHANGED
@@ -2,8 +2,9 @@ import torch
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from diffusers import StableDiffusion3Pipeline
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import gradio as gr
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import os
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import
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from huggingface_hub import snapshot_download
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HF_TOKEN = os.getenv("HF_TOKEN")
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@@ -27,9 +28,34 @@ else:
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pipe = StableDiffusion3Pipeline.from_pretrained(model_path, torch_dtype=torch.float16)
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pipe.to(device)
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# Define the image generation function
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@spaces.GPU(duration=60)
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def generate_image(prompt, negative_prompt, num_inference_steps, height, width, guidance_scale, num_images_per_prompt):
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output = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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@@ -37,6 +63,7 @@ def generate_image(prompt, negative_prompt, num_inference_steps, height, width,
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height=height,
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width=width,
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guidance_scale=guidance_scale,
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num_images_per_prompt=num_images_per_prompt
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).images
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return output
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@@ -45,6 +72,8 @@ def generate_image(prompt, negative_prompt, num_inference_steps, height, width,
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prompt = gr.Textbox(label="Prompt", info="Describe the image you want", placeholder="A cat...")
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negative_prompt = gr.Textbox(label="Negative Prompt", info="Describe what you don't want in the image", placeholder="Ugly, bad anatomy...")
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num_inference_steps = gr.Number(label="Number of Inference Steps", precision=0, value=25)
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@@ -55,11 +84,13 @@ width = gr.Slider(label="Width", info="Width of the Image", minimum=256, maximum
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guidance_scale = gr.Number(minimum=0.1, value=7.5, label="Guidance Scale", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference")
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num_images_per_prompt = gr.Slider(label="Number of Images to generate with the settings",minimum=1, maximum=4, step=1, value=1)
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interface = gr.Interface(
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fn=generate_image,
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inputs=[prompt, negative_prompt, num_inference_steps, height, width, guidance_scale, num_images_per_prompt],
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outputs=gr.Gallery(label="Generated AI Images", elem_id="gallery", show_label=False),
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title="Stable Diffusion 3 Medium",
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description="Made by <a href='https://linktr.ee/Nick088' target='_blank'>Nick088</a> \n Join https://discord.gg/osai to talk about Open Source AI"
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from diffusers import StableDiffusion3Pipeline
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import gradio as gr
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import os
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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from huggingface_hub import snapshot_download
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import spaces
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HF_TOKEN = os.getenv("HF_TOKEN")
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pipe = StableDiffusion3Pipeline.from_pretrained(model_path, torch_dtype=torch.float16)
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pipe.to(device)
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tokenizer = T5Tokenizer.from_pretrained("roborovski/superprompt-v1")
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model = T5ForConditionalGeneration.from_pretrained("roborovski/superprompt-v1", device_map="auto", torch_dtype="auto")
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model.to(device)
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# Define the image generation function
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@spaces.GPU(duration=60)
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def generate_image(prompt, negative_prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt):
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if seed == 0:
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seed = random.randint(1, 2**32-1)
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if enhance_prompt:
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transformers.set_seed(seed)
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input_text = f"Expand the following prompt to add more detail: {prompt}"
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=512,
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repetition_penalty=1.2,
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do_sample=True,
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temperature=0.7,
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top_p=1,
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top_k=50,
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)
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generator = torch.Generator().manual_seed(seed)
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output = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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height=height,
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width=width,
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guidance_scale=guidance_scale,
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generator=generator,
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num_images_per_prompt=num_images_per_prompt
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).images
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return output
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prompt = gr.Textbox(label="Prompt", info="Describe the image you want", placeholder="A cat...")
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enhance_prompt = gr.Checkbox(label="Prompt Enhancement", info="Enhance your prompt with SuperPrompt-v1", value=True)
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negative_prompt = gr.Textbox(label="Negative Prompt", info="Describe what you don't want in the image", placeholder="Ugly, bad anatomy...")
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num_inference_steps = gr.Number(label="Number of Inference Steps", precision=0, value=25)
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guidance_scale = gr.Number(minimum=0.1, value=7.5, label="Guidance Scale", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference")
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seed = gr.Slider(value=42, minimum=0, maximum=2**32-1, step=1, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
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num_images_per_prompt = gr.Slider(label="Number of Images to generate with the settings",minimum=1, maximum=4, step=1, value=1)
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interface = gr.Interface(
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fn=generate_image,
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inputs=[prompt, enhance_prompt, negative_prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt],
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outputs=gr.Gallery(label="Generated AI Images", elem_id="gallery", show_label=False),
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title="Stable Diffusion 3 Medium",
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description="Made by <a href='https://linktr.ee/Nick088' target='_blank'>Nick088</a> \n Join https://discord.gg/osai to talk about Open Source AI"
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