initial commit

README.md

@@ -1,12 +1,12 @@
 ---
-title: Flux Half Illustration
-emoji: 🏆
-colorFrom: blue
-colorTo: yellow
+title: FLUX-HALF-ILLUSTRATION
+emoji: 🖥️
+colorFrom: red
+colorTo: red
 sdk: gradio
-sdk_version: 4.41.0
+sdk_version: 4.40.0
 app_file: app.py
-pinned: false
+pinned: true
 license: mit
 ---
 

app.py

@@ -0,0 +1,146 @@
+import gradio as gr
+import numpy as np
+import random
+import spaces
+import torch
+from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
+from huggingface_hub import hf_hub_download
+import os
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+# Initialize the pipeline globally
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
+
+@spaces.GPU(duration=300)
+def infer(prompt, lora_model, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+    global pipe
+    
+    # Load LoRA if specified
+    if lora_model:
+        try:
+            pipe.load_lora_weights(lora_model)
+        except Exception as e:
+            return None, seed, f"Failed to load LoRA model: {str(e)}"
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed)
+    
+    try:
+        image = pipe(
+            prompt=prompt, 
+            width=width,
+            height=height,
+            num_inference_steps=num_inference_steps, 
+            generator=generator,
+            guidance_scale=guidance_scale
+        ).images[0]
+        
+        # Unload LoRA weights after generation
+        if lora_model:
+            pipe.unload_lora_weights()
+        
+        return image, seed, "Image generated successfully."
+    except Exception as e:
+        return None, seed, f"Error during image generation: {str(e)}"
+
+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:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# FLUX.1 [dev] with LoRA Support
+12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
+[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
+        """)
+        
+        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)
+        
+        lora_model = gr.Text(
+            label="LoRA Model ID (optional)",
+            placeholder="Enter Hugging Face LoRA model ID",
+        )
+        
+        result = gr.Image(label="Result", show_label=False)
+        output_message = gr.Textbox(label="Output Message")
+        
+        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():
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1,
+                    maximum=15,
+                    step=0.1,
+                    value=3.5,
+                )
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=28,
+                )
+        
+        gr.Examples(
+            examples=examples,
+            fn=infer,
+            inputs=[prompt, lora_model],
+            outputs=[result, seed, output_message],
+            cache_examples="lazy"
+        )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[prompt, lora_model, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed, output_message]
+    )
+
+demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+accelerate
+git+https://github.com/huggingface/diffusers
+torch
+transformers==4.42.4
+xformers
+sentencepiece
+peft