KingNish commited on
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a8c9598
1 Parent(s): 3d738b9

Update app.py

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Files changed (1) hide show
  1. app.py +20 -135
app.py CHANGED
@@ -2,145 +2,30 @@ import gradio as gr
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  import numpy as np
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  import random
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  from diffusers import DiffusionPipeline
 
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  import torch
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- device = "cuda" if torch.cuda.is_available() else "cpu"
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- if torch.cuda.is_available():
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- torch.cuda.max_memory_allocated(device=device)
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- pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
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- pipe.enable_xformers_memory_efficient_attention()
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- pipe = pipe.to(device)
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- else:
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- pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
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- pipe = pipe.to(device)
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- MAX_SEED = np.iinfo(np.int32).max
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- MAX_IMAGE_SIZE = 1024
 
 
 
20
 
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- def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
 
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- if randomize_seed:
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- seed = random.randint(0, MAX_SEED)
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-
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- generator = torch.Generator().manual_seed(seed)
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-
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- image = pipe(
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- prompt = prompt,
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- negative_prompt = negative_prompt,
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- guidance_scale = guidance_scale,
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- num_inference_steps = num_inference_steps,
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- width = width,
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- height = height,
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- generator = generator
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- ).images[0]
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-
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- return image
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- examples = [
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- "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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- "An astronaut riding a green horse",
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- "A delicious ceviche cheesecake slice",
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- ]
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-
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- css="""
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- #col-container {
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- margin: 0 auto;
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- max-width: 520px;
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- }
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- """
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-
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- if torch.cuda.is_available():
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- power_device = "GPU"
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- else:
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- power_device = "CPU"
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-
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- with gr.Blocks(css=css) as demo:
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-
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- with gr.Column(elem_id="col-container"):
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- gr.Markdown(f"""
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- # Text-to-Image Gradio Template
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- Currently running on {power_device}.
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- """)
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-
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- with gr.Row():
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-
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- prompt = gr.Text(
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- label="Prompt",
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- show_label=False,
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- max_lines=1,
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- placeholder="Enter your prompt",
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- container=False,
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- )
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-
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- run_button = gr.Button("Run", scale=0)
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-
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- result = gr.Image(label="Result", show_label=False)
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-
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- with gr.Accordion("Advanced Settings", open=False):
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-
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- negative_prompt = gr.Text(
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- label="Negative prompt",
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- max_lines=1,
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- placeholder="Enter a negative prompt",
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- visible=False,
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- )
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-
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- seed = gr.Slider(
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- label="Seed",
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- minimum=0,
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- maximum=MAX_SEED,
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- step=1,
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- value=0,
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- )
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-
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- randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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-
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- with gr.Row():
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-
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- width = gr.Slider(
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- label="Width",
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- minimum=256,
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- maximum=MAX_IMAGE_SIZE,
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- step=32,
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- value=512,
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- )
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-
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- height = gr.Slider(
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- label="Height",
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- minimum=256,
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- maximum=MAX_IMAGE_SIZE,
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- step=32,
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- value=512,
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- )
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-
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- with gr.Row():
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-
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- guidance_scale = gr.Slider(
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- label="Guidance scale",
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- minimum=0.0,
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- maximum=10.0,
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- step=0.1,
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- value=0.0,
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- )
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-
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- num_inference_steps = gr.Slider(
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- label="Number of inference steps",
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- minimum=1,
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- maximum=12,
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- step=1,
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- value=2,
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- )
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-
135
- gr.Examples(
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- examples = examples,
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- inputs = [prompt]
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- )
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-
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- run_button.click(
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- fn = infer,
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- inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
143
- outputs = [result]
144
- )
145
-
146
- demo.queue().launch()
 
2
  import numpy as np
3
  import random
4
  from diffusers import DiffusionPipeline
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+ from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
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  import torch
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+ pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16, variant="fp16").to("cuda")
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+ pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
 
 
 
 
 
 
 
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+ @spaces.GPU(duration=50)
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+ def generate_image(prompt, negative_prompt):
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+ # Run the diffusion model to generate an image
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+ output = pipe(prompt, negative_prompt, num_inference_steps=7, guidance_scale=3.5)
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+ return output.images[0]
17
 
18
+ prompt = gr.Textbox(label = "Prompt", info = "Describe the subject, the background and the style of image", placeholder = "Describe what you want to see", lines = 2)
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+ negative_prompt = gr.Textbox(label = "Negative prompt", placeholder = "Describe what you do NOT want to see", value = "Ugly, malformed, noise, blur, watermark")
20
 
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+ gr_interface = gr.Interface(
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+ fn=generate_image,
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+ inputs=[prompt, negative_prompt],
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+ outputs="image",
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+ title="Real-time Image Generation with Diffusion",
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+ description="Enter a prompt to generate an image",
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+ theme="soft"
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+ )
 
 
 
 
 
 
 
 
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30
+ # Launch the Gradio app
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+ gr_interface.launch()