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.gitattributes

@@ -33,5 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+# models/**/* filter=lfs diff=lfs merge=lfs -text
 models/ByteDance/Hyper-SD/hypersd_tearser.jpg filter=lfs diff=lfs merge=lfs -text
 models/black-forest-labs/FLUX.1-dev/dev_grid.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.DS_Store

README.md

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+---
+title: flux-loras
+emoji: 🪄🧿
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.26.0
+app_file: app.py
+pinned: true
+thumbnail: >-
+  https://cdn-uploads.huggingface.co/production/uploads/66d9c5d1e83856057c852a15/yq-nzTWd81HB6NJ_IKGRi.jpeg
+short_description: GrowerAI Flux Vision Studio - Unrealistically Fast
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import spaces
+import argparse
+import os
+import time
+from os import path
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download
+from transformers.utils.hub import move_cache
+
+# move_cache()
+cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
+# os.environ["TRANSFORMERS_CACHE"] = cache_path
+os.environ["HF_HUB_CACHE"] = cache_path
+os.environ["HF_HOME"] = cache_path
+
+import gradio as gr
+import torch
+from diffusers import FluxPipeline
+
+torch.backends.cuda.matmul.allow_tf32 = True
+
+class timer:
+    def __init__(self, method_name="timed process"):
+        self.method = method_name
+    def __enter__(self):
+        self.start = time.time()
+        print(f"{self.method} starts")
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        end = time.time()
+        print(f"{self.method} took {str(round(end - self.start, 2))}s")
+
+if not path.exists(cache_path):
+    os.makedirs(cache_path, exist_ok=True)
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"))
+pipe.fuse_lora(lora_scale=0.125)
+pipe.to(device="cuda", dtype=torch.bfloat16)
+
+
+css = """
+# gen_btn{height: 100%}
+#gen_column{align-self: stretch}
+.primary{background-color: #4C76FF !important}
+#grower-label-span span{background-color: #4C76FF !important}
+#grower-label-image label{background-color: #4C76FF !important} 
+"""
+
+
+
+js_code = """
+    function createGradioAnimation() {
+        const emojis = ['✨', '🤖', '📈', '🎨', '🔍', '📱', '🔮', '🥰', '🌈', '💖'];
+        const gravity = 0.5;
+        const bounceFactor = -0.7;
+        const friction = 0.9;
+
+        document.getElementById('gen_btn').addEventListener('click', (event) => {
+            const count = Math.floor(Math.random() * 6) + 10; 
+            for (let i = 0; i < count; i++) {
+                createEmoji(event.clientX, event.clientY);
+            }
+        });
+
+        function createEmoji(x, y) {
+            const emojiElement = document.createElement('div');
+            emojiElement.textContent = emojis[Math.floor(Math.random() * emojis.length)];
+            emojiElement.style.position = 'absolute';
+            emojiElement.style.fontSize = '24px';
+            emojiElement.style.transition = 'opacity 0.1s ease-out';
+            document.body.appendChild(emojiElement);
+
+            const rect = emojiElement.getBoundingClientRect();
+            let posX = x - rect.width / 2;
+            let posY = y - rect.height / 2;
+            let velX = (Math.random() - 0.5) * 10;
+            let velY = (Math.random() - 0.5) * 10;
+
+            function update() {
+                if (posY + rect.height >= window.innerHeight) {
+                    posY = window.innerHeight - rect.height;
+                    velY *= bounceFactor;
+                } else {
+                    velY += gravity;
+                }
+
+                if (posX <= 0 || posX + rect.width >= window.innerWidth) {
+                    velX *= bounceFactor;
+                }
+
+                velX *= friction;
+                velY *= friction;
+
+                posX += velX;
+                posY += velY;
+
+                emojiElement.style.transform = `translate(${posX}px, ${posY}px)`;
+                
+                if (Math.abs(velX) > 0.1 || Math.abs(velY) > 0.1) {
+                    requestAnimationFrame(update);
+                } else {
+                    emojiElement.style.opacity=0;
+                         setTimeout(function(){     
+                        emojiElement.remove();}, 2000);
+                }
+            }
+
+            update();
+        }
+    return 'Animation created';
+    }
+    """
+
+with gr.Blocks(theme='charbel-malo/Crystal', js=js_code) as demo:
+    gr.Markdown(
+        """
+        <div style="text-align: left;margin-top:20px">
+            <h1><img src="https://staging.the-grower.com/assets/images/grower_logo_dark.png" style="height:50px;object-fit:contain;"> GrowerAI VisionPRO</h1>
+            <p style="font-size: 1rem; margin-bottom: 1.5rem;">HyperFlux-based Image Generation Model 8Steps-Lora</p>
+        </div>
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=3):
+            with gr.Group():
+                base_prompt = gr.Textbox(
+                    label="Base Prompt",
+                    placeholder="E.g., A serene landscape with mountains and a lake at sunset",
+                    lines=3,
+                    elem_id="grower-label-span"
+                )
+                with gr.Accordion("Advanced Prompt Settings", open=False):
+                    subject = gr.Textbox(label="Subject", placeholder="Enter the subject")
+                    object_ = gr.Textbox(label="Object", placeholder="Enter the object")
+                    style = gr.Textbox(label="Style", placeholder="Enter the style")
+                    clothing = gr.Textbox(label="Clothing", placeholder="Enter the clothing")
+                    objective = gr.Dropdown(
+                        choices=["digital marketing post","website hero visual","Ad cover","Movie poster"],
+                        value=None,
+                        multiselect=False,
+                        label="Objective",
+                        info="Select an objective"
+                    )
+                with gr.Accordion("Advanced Settings", open=False):
+                    with gr.Group():
+                        with gr.Row():
+                            height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=1024)
+                            width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=1024)
+                        
+                        with gr.Row():
+                            steps = gr.Slider(label="Inference Steps", minimum=6, maximum=25, step=1, value=8)
+                            scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5)
+                        
+                        seed = gr.Number(label="Seed (for reproducibility)", value=3413, precision=0)
+                
+                generate_btn = gr.Button("Generate Image", variant="primary", scale=1, elem_id="gen_btn")
+
+        with gr.Column(scale=4):
+            output = gr.Image(label="Your Generated Image", elem_id="grower-label-image")
+    
+    gr.Markdown(
+        """
+        <div style="margin: 2rem auto; padding: 1rem; border-radius: 10px;">
+            <h2 style="font-size: 1.5rem; margin-bottom: 1rem;">How to Use</h2>
+            <ol style="padding-left: 1.5rem;">
+                <li>Enter a detailed description of the image you want to create.</li>
+                <li>Adjust advanced settings if desired (tap to expand).</li>
+                <li>Tap "Generate Image" and wait for your creation!</li>
+            </ol>
+            <p style="margin-top: 1rem; font-style: italic;">Tip: Be specific in your description for best results!</p>
+        </div>
+        """
+    )
+
+    @spaces.GPU
+    def process_image(height, width, steps, scales, base_prompt, subject, object_, style, clothing, objective, seed):
+        # Build the advanced prompt
+        advanced_prompt_template = (
+            "Create a highly stylized digital avatar of {subject}, holding {object}. "
+            "joy, simplified {subject} avatar or emoji. , typical of 3D digital art :: "
+            "The overall style is {style}. {clothing}. and modern digital art style, "
+            "detailed shading, and dynamic positioning that makes it suitable for {objective}"
+        )
+        advanced_prompt = advanced_prompt_template.format(
+            subject=subject,
+            object=object_,
+            style=style,
+            clothing=clothing,
+            objective=objective
+        )
+        # Combine base prompt and advanced prompt
+        prompt = advanced_prompt
+        global pipe
+        with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
+            return pipe(
+                prompt=[prompt],
+                generator=torch.Generator().manual_seed(int(seed)),
+                num_inference_steps=int(steps),
+                guidance_scale=float(scales),
+                height=int(height),
+                width=int(width),
+                max_sequence_length=256
+            ).images[0]
+
+    generate_btn.click(
+        process_image,
+        inputs=[
+            height, width, steps, scales, base_prompt, subject, object_, style, clothing, objective, seed
+        ],
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+accelerate
+diffusers==0.30.0
+invisible_watermark
+torch
+transformers==4.43.3
+xformers
+sentencepiece
+peft
+spaces