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Update app.py
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app.py
CHANGED
@@ -1,146 +1,80 @@
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from diffusers import
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import torch
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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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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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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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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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with gr.Blocks(css=css) as demo:
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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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with gr.Row():
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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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run_button = gr.Button("Run", scale=0)
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result = gr.Image(label="Result", show_label=False)
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with gr.Accordion("Advanced Settings", open=False):
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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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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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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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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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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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with gr.Row():
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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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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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gr.Examples(
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examples = examples,
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inputs = [prompt]
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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],
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outputs = [result]
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)
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demo.queue().launch()
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pip install diffusers
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pip install transformers
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from diffusers import StableDiffusionPipeline
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import torch
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model_id = "runwayml/stable-diffusion-v1-5"
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sd_pipeline = StableDiffusionPipeline.from_pretrained(model_id,
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torch_dtype=torch.float16)
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sd_pipeline = sd_pipeline.to("cuda")
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def get_completion_sd(prompt):
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negative_prompt = """
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simple background, duplicate, low quality, lowest quality,
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bad anatomy, bad proportions, extra digits, lowres, username,
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artist name, error, duplicate, watermark, signature, text,
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extra digit, fewer digits, worst quality, jpeg artifacts, blurry
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"""
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return sd_pipeline(prompt, negative_prompt=negative_prompt).images[0]
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#let's prompt
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# prompt = "astronaut, riding a horse, on mars, human colony"
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# prompt = "children, playing in disneyland, view from a distance"
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prompt = """llama, wearing red socks,
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grazing, open field, raining
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"""
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print(prompt)
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sd_image = get_completion_sd(prompt)
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sd_image.save("./llama.jpg")
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pip install gradio
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pip install h5py
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pip install typing-extensions
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pip install wheel
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import gradio as gr
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def get_completion(prompt):
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negative_prompt = """
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simple background, duplicate, low quality, lowest quality,
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bad anatomy, bad proportions, extra digits, lowres, username,
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artist name, error, duplicate, watermark, signature, text,
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extra digit, fewer digits, worst quality, jpeg artifacts, blurry
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"""
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return sd_pipeline(prompt, negative_prompt=negative_prompt).images[0]
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# def generate(prompt):
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# output = get_completion_sd(prompt)
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# return output
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genai_app = gr.Interface(fn=get_completion,
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inputs=[gr.Textbox(label="Your prompt")],
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outputs=[gr.Image(label="Result")],
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title="Generate Cool Images",
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description="Generate any image with Stable Diffusion",
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allow_flagging="never",
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examples=["astronaut, riding a horse, on mars",
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"cargo ship, flying, in space"])
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genai_app.launch()
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