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README (1).md

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+---
+title: FLUX.1 Dev Serverless
+emoji: 🔥
+colorFrom: pink
+colorTo: purple
+sdk: gradio
+sdk_version: 4.41.0
+app_file: app.py
+pinned: true
+license: mit
+short_description: FLUX.1-Dev on serverless inference, no GPU required
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app (3).py

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+import gradio as gr
+import requests
+import io
+import random
+import os
+import time
+from PIL import Image
+from deep_translator import GoogleTranslator
+import json
+
+# Project by Nymbo
+
+API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
+API_TOKEN = os.getenv("HF_READ_TOKEN")
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+timeout = 100
+
+def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7):
+    if prompt == "" or prompt == None:
+        return None
+
+    key = random.randint(0, 999)
+    
+    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    
+    prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
+    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
+
+    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
+    print(f'\033[1mGeneration {key}:\033[0m {prompt}')
+    
+    payload = {
+        "inputs": prompt,
+        "is_negative": is_negative,
+        "steps": steps,
+        "cfg_scale": cfg_scale,
+        "seed": seed if seed != -1 else random.randint(1, 1000000000),
+        "strength": strength
+    }
+
+    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
+    if response.status_code != 200:
+        print(f"Error: Failed to get image. Response status: {response.status_code}")
+        print(f"Response content: {response.text}")
+        if response.status_code == 503:
+            raise gr.Error(f"{response.status_code} : The model is being loaded")
+        raise gr.Error(f"{response.status_code}")
+    
+    try:
+        image_bytes = response.content
+        image = Image.open(io.BytesIO(image_bytes))
+        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
+        return image
+    except Exception as e:
+        print(f"Error when trying to open the image: {e}")
+        return None
+
+css = """
+#app-container {
+    max-width: 600px;
+    margin-left: auto;
+    margin-right: auto;
+}
+"""
+
+with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
+    gr.HTML("<center><h1>FLUX.1-Dev</h1></center>")
+    with gr.Column(elem_id="app-container"):
+        with gr.Row():
+            with gr.Column(elem_id="prompt-container"):
+                with gr.Row():
+                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
+                with gr.Row():
+                    with gr.Accordion("Advanced Settings", open=False):
+                        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input")
+                        steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
+                        cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
+                        method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
+                        strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
+                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
+
+        with gr.Row():
+            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
+        with gr.Row():
+            image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
+        
+        text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength], outputs=image_output)
+
+app.launch(show_api=False, share=False)