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app.py

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+import spaces
+import gradio as gr
+import torch
+from diffusers import FluxPipeline, FluxTransformer2DModel, FlowMatchEulerDiscreteScheduler
+from huggingface_hub import hf_hub_download
+from PIL import Image
+import requests
+from translatepy import Translator
+import numpy as np
+import random
+import os
+hf_token = os.environ.get('HF_TOKEN')
+from io import BytesIO
+
+translator = Translator()
+
+# Constants
+model = "black-forest-labs/FLUX.1-dev"
+
+
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+# Ensure model and scheduler are initialized in GPU-enabled function
+if torch.cuda.is_available():
+    transformer = FluxTransformer2DModel.from_single_file(
+        "https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v27.safetensors",
+        torch_dtype=torch.bfloat16
+    )
+    pipe = FluxPipeline.from_pretrained(
+        model, 
+        transformer=transformer,
+        torch_dtype=torch.bfloat16, token=hf_token)
+    pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(
+        pipe.scheduler.config, use_beta_sigmas=True
+    )
+    pipe.to("cuda")
+    
+    
+@spaces.GPU()
+def infer(prompt, width, height, num_inference_steps, guidance_scale, nums, seed=42, randomize_seed=True, progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed)
+    image = pipe(
+            prompt = prompt, 
+            width = width,
+            height = height,
+            num_inference_steps = num_inference_steps,
+            guidance_scale=guidance_scale,
+            num_images_per_prompt=nums,
+            generator = generator
+    ).images
+
+    
+    return image, seed
+
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 1024px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.HTML("<h1><center>Model Testing</center></h1><p><center>Chroma</center></p>")
+        
+        with gr.Row():
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=2,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
+            run_button = gr.Button("Run", scale=0)
+        
+        result = gr.Gallery(label="Gallery", format="png", columns = 1, preview=True, height=400)
+        
+        with gr.Accordion("Advanced Settings", open=False):
+                       
+            with gr.Row():
+                
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+                
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+            
+            with gr.Row():
+                  
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=30,
+                )
+
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=0,
+                    maximum=10,
+                    step=0.1,
+                    value=3.5,
+                )
+
+
+            with gr.Row():
+            
+                nums = gr.Slider(
+                    label="Number of Images",
+                    minimum=1,
+                    maximum=2,
+                    step=1,
+                    value=1,
+                    scale=1,
+                )
+       
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=-1,
+                )
+            
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [prompt, width, height, num_inference_steps, guidance_scale, nums, seed, randomize_seed],
+        outputs = [result, seed]
+    )
+    
+demo.launch()

requirements.txt

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+diffusers
+transformers
+numpy
+torch
+pillow
+scipy
+translatepy
+accelerate
+sentencepiece
+peft