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| import spaces | |
| import gradio as gr | |
| import numpy as np | |
| import random | |
| from diffusers import DiffusionPipeline | |
| import torch | |
| from huggingface_hub import login | |
| import os | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Set your Hugging Face token | |
| HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN") | |
| if HUGGINGFACE_TOKEN is None: | |
| raise ValueError("Hugging Face token not found. Please set the HUGGINGFACE_TOKEN environment variable.") | |
| login(token=HUGGINGFACE_TOKEN) | |
| # Path to your model repository and safetensors weights | |
| base_model_repo = "stabilityai/stable-diffusion-3-medium-diffusers" | |
| lora_weights_path = "./pytorch_lora_weights.safetensors" | |
| # Load the base model | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| base_model_repo, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| use_auth_token=HUGGINGFACE_TOKEN | |
| ) | |
| pipeline.load_lora_weights(lora_weights_path) | |
| # Comment out the line for sequential CPU offloading | |
| # pipeline.enable_sequential_cpu_offload() | |
| pipeline = pipeline.to(device) | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 1024 # Reduce max image size to fit within memory constraints | |
| CACHE_EXAMPLES = False | |
| def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| image = pipeline( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| width=width, | |
| height=height, | |
| generator=generator | |
| ).images[0] | |
| return image | |
| examples = [ | |
| ["Blue checked fit & flare dress,Shoulder straps,Sleeveless, no sleeves,floral print,Maxi length in flared hem"], | |
| ["Navy blue and cream-coloured colourblocked maxi dress,Sweetheart Neck,Short, flutter sleeves,Maxi length with flared hem"], | |
| ["Beige-coloured & black regular wrap top,Animal printed,V-neck, three-quarter, regular sleeves"], | |
| ["Solid regular-fit pink formal blazer, Long sleeves, two pockets"] | |
| ] | |
| css = """ | |
| body { | |
| background-color: #ffffff; /* Myntra's white background */ | |
| color: #282c3f; /* Myntra's primary text color */ | |
| font-family: 'Arial', sans-serif; | |
| margin: 0; | |
| padding: 0; | |
| } | |
| #header { | |
| background-color: #ff3f6c; /* Myntra's pink color */ | |
| color: white; | |
| text-align: center; | |
| padding: 20px; | |
| font-size: 24px; | |
| font-weight: bold; | |
| } | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 720px; | |
| padding: 20px; | |
| border: 1px solid #ebebeb; | |
| border-radius: 8px; | |
| box-shadow: 0 2px 8px rgba(0,0,0,0.1); | |
| } | |
| .gr-button { | |
| background-color: #ff3f6c; /* Myntra's pink color */ | |
| color: white; | |
| border: none; | |
| padding: 10px 20px; | |
| font-size: 16px; | |
| border-radius: 5px; | |
| cursor: pointer; | |
| margin-top: 10px; | |
| } | |
| .gr-button:hover { | |
| background-color: #e62e5c; /* Darker shade for hover effect */ | |
| } | |
| .gr-textbox, .gr-slider, .gr-checkbox, .gr-accordion { | |
| margin-bottom: 20px; | |
| } | |
| .gr-markdown { | |
| text-align: center; | |
| font-size: 24px; | |
| margin-bottom: 20px; | |
| } | |
| .gr-image { | |
| border: 1px solid #ebebeb; | |
| border-radius: 8px; | |
| margin-top: 20px; | |
| } | |
| """ | |
| if torch.cuda.is_available(): | |
| power_device = "GPU" | |
| else: | |
| power_device = "CPU" | |
| with gr.Blocks(css=css) as demo: | |
| gr.HTML("<div id='header'>Myntra Text-to-Image Generation</div>") | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(f""" | |
| Currently running on {power_device}. | |
| """) | |
| with gr.Row(): | |
| prompt = gr.Textbox( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| run_button = gr.Button("Generate", scale=0) | |
| result = gr.Image(label="Result", show_label=False) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| negative_prompt = gr.Textbox( | |
| label="Negative prompt", | |
| max_lines=1, | |
| placeholder="Enter a negative prompt", | |
| visible=True, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=512, | |
| step=32, | |
| value=512, | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=2048, | |
| step=32, | |
| value=512, | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.0, | |
| maximum=10.0, | |
| step=0.1, | |
| value=7.5, | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=30, | |
| ) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[prompt], | |
| outputs=[result], | |
| cache_examples=CACHE_EXAMPLES, | |
| ) | |
| run_button.click( | |
| fn=infer, | |
| inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
| outputs=[result] | |
| ) | |
| demo.queue().launch() | |