Update README.md

README.md

@@ -9,4 +9,214 @@ pipeline_tag: image-to-image
 tags:
 - art
 - 8bit
----
+---
+
+## Quick Start with Diffusers 🧨
+
+### Install the required packages
+
+```py
+transformers # - transformers@v4.57.6
+torch        # - torch@v2.9.1+cu128
+diffusers    # - diffusers@v0.37.0.dev0
+bitsandbytes # - bitsandbytes@v0.49.2
+gradio       # - gradio@v6.6.0
+accelerate   # - accelerate@v1.12.0
+```
+
+### Run FireRed-Image-Edit-1.0-8bit [Demo] 
+
+```py
+import os
+import gc
+import gradio as gr
+import numpy as np
+#import spaces # Uncomment the Spaces-related modules if you are using HF ZeroGPU
+
+import torch
+import random
+from PIL import Image
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
+print("torch.__version__ =", torch.__version__)
+print("Using device:", device)
+
+from diffusers.models import QwenImageTransformer2DModel
+from diffusers import QwenImageEditPlusPipeline
+from diffusers.utils import load_image
+
+dtype = torch.bfloat16
+
+transformer = QwenImageTransformer2DModel.from_pretrained(
+    "prithivMLmods/FireRed-Image-Edit-1.0-8bit",
+    subfolder="transformer",
+    torch_dtype=dtype
+)
+
+pipe = QwenImageEditPlusPipeline.from_pretrained(
+    "prithivMLmods/FireRed-Image-Edit-1.0-8bit",
+    transformer=transformer,
+    torch_dtype=dtype
+).to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+
+def update_dimensions_on_upload(image):
+    if image is None:
+        return 1024, 1024
+    
+    original_width, original_height = image.size
+    
+    if original_width > original_height:
+        new_width = 1024
+        aspect_ratio = original_height / original_width
+        new_height = int(new_width * aspect_ratio)
+    else:
+        new_height = 1024
+        aspect_ratio = original_width / original_height
+        new_width = int(new_height * aspect_ratio)
+        
+    new_width = (new_width // 8) * 8
+    new_height = (new_height // 8) * 8
+    
+    return new_width, new_height
+
+#@spaces.GPU
+def infer(
+    images,
+    prompt,
+    seed,
+    randomize_seed,
+    guidance_scale,
+    steps,
+    progress=gr.Progress(track_tqdm=True)
+):
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    if not images:
+        raise gr.Error("Please upload at least one image to edit.")
+
+    pil_images = []
+    if images is not None:
+        for item in images:
+            try:
+                if isinstance(item, tuple) or isinstance(item, list):
+                    path_or_img = item[0]
+                else:
+                    path_or_img = item
+
+                if isinstance(path_or_img, str):
+                    pil_images.append(Image.open(path_or_img).convert("RGB"))
+                elif isinstance(path_or_img, Image.Image):
+                    pil_images.append(path_or_img.convert("RGB"))
+                else:
+                    pil_images.append(Image.open(path_or_img.name).convert("RGB"))
+            except Exception as e:
+                print(f"Skipping invalid image item: {e}")
+                continue
+
+    if not pil_images:
+        raise gr.Error("Could not process uploaded images.")
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator(device=device).manual_seed(seed)
+    negative_prompt = "worst quality, low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry"
+
+    width, height = update_dimensions_on_upload(pil_images[0])
+
+    try:
+        result_image = pipe(
+            image=pil_images,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            height=height,
+            width=width,
+            num_inference_steps=steps,
+            generator=generator,
+            true_cfg_scale=guidance_scale,
+        ).images[0]
+        
+        return result_image, seed
+
+    except Exception as e:
+        raise e
+    finally:
+        gc.collect()
+        torch.cuda.empty_cache()
+
+#@spaces.GPU
+def infer_example(images, prompt):
+    if not images:
+        return None, 0
+    
+    if isinstance(images, str):
+        images_list = [images]
+    else:
+        images_list = images
+        
+    result, seed = infer(
+        images=images_list,
+        prompt=prompt,
+        seed=0,
+        randomize_seed=True,
+        guidance_scale=1.0,
+        steps=20
+    )
+    return result, seed
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 1000px;
+}
+#main-title h1 {font-size: 2.4em !important;}
+"""
+
+with gr.Blocks() as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# **FireRed-Image-Edit-1.0-8bit**", elem_id="main-title")
+
+        with gr.Row(equal_height=True):
+            with gr.Column():
+                images = gr.Gallery(
+                    label="Upload Images", 
+                    type="filepath", 
+                    columns=2, 
+                    rows=1, 
+                    height=300,
+                    allow_preview=True
+                )
+
+                with gr.Row():
+                    prompt = gr.Text(
+                        label="Edit Prompt",
+                        show_label=True,
+                        placeholder="e.g., transform into anime..",
+                    )
+
+                with gr.Row():
+                    run_button = gr.Button("Edit Image", variant="primary")
+
+            with gr.Column():
+                output_image = gr.Image(label="Output Image", interactive=False, format="png", height=390)
+                
+        with gr.Accordion("Advanced Settings", open=False, visible=True):
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+            guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
+            steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=20)
+        
+    run_button.click(
+        fn=infer,
+        inputs=[images, prompt, seed, randomize_seed, guidance_scale, steps],
+        outputs=[output_image, seed]
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=30).launch(css=css, mcp_server=True, ssr_mode=False, show_error=True)
+```