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Update app.py
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app.py
CHANGED
@@ -1,6 +1,11 @@
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import gradio as gr
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import torch
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from diffusers import
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from huggingface_hub import hf_hub_download
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# --- Configuration ---
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@@ -36,14 +41,14 @@ pipe.load_lora_weights(lora_file_path)
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pipe.to("cuda")
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# --- Default Settings from your Recommendations ---
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# These are pulled directly from your "Recomendations.txt".
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default_positive_prompt = "masterpiece, best quality, ultra-detailed, realistic skin, intricate details, highres"
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default_negative_prompt = "low quality, worst quality, blurry, (deformed:1.3), extra fingers, cartoon, 3d, anime, bad anatomy"
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default_sampler = "DPM++ 2M Karras"
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default_cfg = 6.0
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default_steps = 30
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trigger_word = "emilyh"
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lora_tag_main = "<lora:emilyh:0.9>"
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# --- Define the Inference Function ---
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@@ -51,7 +56,7 @@ def generate_image(prompt, negative_prompt, sampler, steps, cfg, width, height,
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"""
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Function to generate an image based on user inputs.
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"""
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# Combine the user prompt with the trigger word and LoRA tag
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full_prompt = f"{lora_tag_main}, {trigger_word}, {prompt}"
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# Set the scheduler (sampler)
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@@ -84,7 +89,7 @@ with gr.Blocks(css="style.css") as demo:
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gr.Markdown("# `emilyh` LoRA Image Generator")
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gr.Markdown(
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"A Gradio interface for the `emilyh` LoRA. "
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"Based on the recommendations provided.
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)
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with gr.Row():
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@@ -97,10 +102,10 @@ with gr.Blocks(css="style.css") as demo:
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label="Sampler",
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choices=["DPM++ 2M Karras", "DPM++ SDE Karras"],
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value=default_sampler,
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)
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steps = gr.Slider(label="Steps", minimum=15, maximum=50, value=default_steps, step=1)
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cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=10.0, value=default_cfg, step=0.5)
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with gr.Row():
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width = gr.Slider(label="Width", minimum=512, maximum=1024, value=1024, step=64)
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output_image = gr.Image(label="Generated Image", type="pil")
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gr.Markdown(
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"""
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### 🔧 Usage Guide
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* The trigger word `emilyh` and the LoRA tag `<lora:emilyh:0.9>` are automatically added to your prompt.
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* For best results, generate images in batches and choose the most consistent ones.
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* The LoRA captures the subject's appearance well across various poses and outfits.
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* A weight of 0.9 provides a good balance of likeness and flexibility.
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* This interface does not include ADetailer, which is recommended for final face refinement.
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"""
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)
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import gradio as gr
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import torch
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from diffusers import (
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StableDiffusionXLPipeline,
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EulerDiscreteScheduler,
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AutoencoderKL,
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DPMSolverSinglestepScheduler,
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)
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from huggingface_hub import hf_hub_download
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# --- Configuration ---
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pipe.to("cuda")
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# --- Default Settings from your Recommendations ---
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# These are pulled directly from your "Recomendations.txt".
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default_positive_prompt = "masterpiece, best quality, ultra-detailed, realistic skin, intricate details, highres"
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default_negative_prompt = "low quality, worst quality, blurry, (deformed:1.3), extra fingers, cartoon, 3d, anime, bad anatomy"
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default_sampler = "DPM++ 2M Karras"
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default_cfg = 6.0
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default_steps = 30
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trigger_word = "emilyh"
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lora_tag_main = "<lora:emilyh:0.9>"
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# --- Define the Inference Function ---
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"""
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Function to generate an image based on user inputs.
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"""
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# Combine the user prompt with the trigger word and LoRA tag
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full_prompt = f"{lora_tag_main}, {trigger_word}, {prompt}"
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# Set the scheduler (sampler)
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gr.Markdown("# `emilyh` LoRA Image Generator")
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gr.Markdown(
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"A Gradio interface for the `emilyh` LoRA. "
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"Based on the recommendations provided."
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)
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with gr.Row():
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label="Sampler",
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choices=["DPM++ 2M Karras", "DPM++ SDE Karras"],
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value=default_sampler,
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)
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steps = gr.Slider(label="Steps", minimum=15, maximum=50, value=default_steps, step=1)
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cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=10.0, value=default_cfg, step=0.5)
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with gr.Row():
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width = gr.Slider(label="Width", minimum=512, maximum=1024, value=1024, step=64)
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output_image = gr.Image(label="Generated Image", type="pil")
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gr.Markdown(
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"""
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### 🔧 Usage Guide
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* The trigger word `emilyh` and the LoRA tag `<lora:emilyh:0.9>` are automatically added to your prompt.
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* For best results, generate images in batches and choose the most consistent ones.
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* The LoRA captures the subject's appearance well across various poses and outfits.
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* A weight of 0.9 provides a good balance of likeness and flexibility. Using a weight closer to 1.0 can increase consistency but may cause stiffness.
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* This interface does not include ADetailer, which is recommended for final face refinement.
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"""
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)
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