Fix pipe establishment

app.py

@@ -1,13 +1,30 @@
 import gradio as gr
+import numpy as np
+import random
 import spaces
+import torch
 import os
-from diffusers import FluxPipeline
+#from diffusers import FluxPipeline
+from diffusers import DiffusionPipeline
 # PuLID veya IP-Adapter yüklemeleri varsayılarak...
 
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+hf_token = os.getenv("HF_TOKEN")
+
 # Modeli belleğe al
-pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell") 
+pipe = DiffusionPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell",
+    torch_dtype=dtype,
+    token=hf_token
+).to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+@spaces.GPU() # ZeroGPU'yu tetikleyen dekoratör
 
-@spaces.GPU # ZeroGPU'yu tetikleyen dekoratör
 def generate_character_image(character_name, prompt):
     # 1. Karakterin referans fotoğrafının yolunu bul
     ref_image_path = f"characters/{character_name}/ref1.jpg"
@@ -20,12 +37,109 @@ def generate_character_image(character_name, prompt):
     
     return image
 
-with gr.Blocks() as demo:
-    char_dropdown = gr.Dropdown(choices=["Kahraman 1", "Kötü Karakter"], label="Karakter Seç")
-    prompt_input = gr.Textbox(label="Zengin Sahne Promptu (Örn: Karanlık bir ormanda büyü yapıyor)")
-    btn = gr.Button("Üret")
-    out_img = gr.Image()
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, 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, 
+            generator = generator,
+            guidance_scale=0.0
+    ).images[0] 
+    return image, seed
+ 
+examples = [
+    "a tiny astronaut hatching from an egg on the moon",
+    "a cat holding a sign that says hello world",
+    "an anime illustration of a wiener schnitzel",
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
     
-    btn.click(fn=generate_character_image, inputs=[char_dropdown, prompt_input], outputs=out_img)
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# FLUX.1 [schnell]
+12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
+[[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
+        """)
+        
+        with gr.Row():
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
+            run_button = gr.Button("Run", scale=0)
+        
+        result = gr.Image(label="Result", show_label=False)
+        
+        with gr.Accordion("Advanced Settings", open=False):
+            
+            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=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=4,
+                )
+        
+        gr.Examples(
+            examples = examples,
+            fn = infer,
+            inputs = [prompt],
+            outputs = [result, seed],
+            cache_examples=False
+        )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
+        outputs = [result, seed]
+    )
 
 demo.launch()

requirements.txt

@@ -1,6 +1,9 @@
-starlette
 gradio
 gradio-client
 huggingface_hub
-datasets
-openai
+accelerate
+diffusers==0.30.0
+transformers==4.44.2
+torch
+xformers
+sentencepiece