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import argparse |
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import time |
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import torch |
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from diffusers import PixArtAlphaPipeline, PixArtTransformer2DModel |
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from diffusers.pipelines.flux import FluxPriorReduxPipeline |
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from transformers import SiglipImageProcessor |
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from pathlib import Path |
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from PIL import Image |
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pipe = None |
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redux = None |
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redux_embedder = None |
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def generate(prompt, image_prompt=None, guidance_scale=2, num_images=4, resolution=512): |
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with torch.no_grad(): |
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clip_image_processor = SiglipImageProcessor(size={"height": 384, "width": 384}) |
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clip_pixel_values = clip_image_processor.preprocess( |
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image_prompt.convert("RGB"), return_tensors="pt" |
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).pixel_values.to("cuda", dtype=torch.bfloat16) |
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image_prompt_latents = redux.image_encoder(clip_pixel_values).last_hidden_state |
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image_prompt_embeds = redux_embedder(image_prompt_latents).image_embeds |
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prompt_embeds = image_prompt_embeds[:, :120, :] * 0.04 |
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attention_mask = torch.ones(prompt_embeds.shape[0], prompt_embeds.shape[1]).to("cuda") |
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images = pipe( |
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prompt_embeds=prompt_embeds, |
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prompt_attention_mask=attention_mask, |
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negative_prompt="", |
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height=resolution, |
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width=resolution, |
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guidance_scale=guidance_scale, |
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num_images_per_prompt=num_images, |
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).images |
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widths, heights = zip(*[img.size for img in images]) |
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total_width = sum(widths) + len(images) - 1 |
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max_height = max(heights) |
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out = Image.new('RGB', (total_width, max_height)) |
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x_offset = 0 |
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for img in images: |
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out.paste(img, (x_offset, 0)) |
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x_offset += img.width + 1 |
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if image_prompt is not None: |
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out_with_image_prompt = Image.new('RGB', (out.width, out.height + 1 + resolution)) |
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resized_prompt = image_prompt.resize((resolution, resolution), Image.Resampling.BILINEAR) |
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out_with_image_prompt.paste(resized_prompt, (0, 0)) |
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out_with_image_prompt.paste(out, (0, resolution + 1)) |
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out = out_with_image_prompt |
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Path("image-outputs").mkdir(parents=True, exist_ok=True) |
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output_filename = f"image-outputs/{prompt[:40].replace(' ', '_')}.{int(time.time())}.png" |
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out.save(output_filename) |
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print(f"Saved output to {output_filename}") |
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def main(): |
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parser = argparse.ArgumentParser( |
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description="Generate images using an image and a text prompt (PixArt Flux Redux)." |
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) |
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parser.add_argument("--prompt", type=str, default="", |
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help='The text prompt for image generation (default: "")') |
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parser.add_argument("--image_prompt", type=str, default=None, |
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help="Path to an optional image to use as a prompt") |
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parser.add_argument("--guidance_scale", type=float, default=2, |
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help="Guidance scale for image generation (default: 2)") |
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parser.add_argument("--num_images", type=int, default=4, |
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help="Number of images to generate (default: 4)") |
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parser.add_argument("--resolution", type=int, default=512, |
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help="Resolution for generated images (default: 512)") |
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args = parser.parse_args() |
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global pipe, redux, redux_embedder |
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pipe = PixArtAlphaPipeline.from_pretrained( |
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"PixArt-alpha/PixArt-XL-2-512x512", transformer=None, torch_dtype=torch.bfloat16 |
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) |
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transformer = PixArtTransformer2DModel.from_pretrained("pixart-flux-redux", torch_dtype=torch.bfloat16) |
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pipe.transformer = transformer |
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redux = FluxPriorReduxPipeline.from_pretrained("FLUX.1-Redux-dev", torch_dtype=torch.bfloat16) |
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redux_embedder = redux.image_embedder |
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redux.to("cuda") |
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pipe.to("cuda") |
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img_prompt = Image.open(args.image_prompt) if args.image_prompt else None |
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generate(args.prompt, image_prompt=img_prompt, guidance_scale=args.guidance_scale, |
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num_images=args.num_images, resolution=args.resolution) |
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if __name__ == "__main__": |
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main() |
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