app.py

# import gradio as gr

# from absl import flags
# from absl import app
# from ml_collections import config_flags
# import os

# import spaces #[uncomment to use ZeroGPU]
# import torch


# import os
# import random

# import numpy as np
# import torch
# import torch.nn.functional as F
# from torchvision.utils import save_image
# from huggingface_hub import hf_hub_download

# from absl import logging
# import ml_collections

# from diffusion.flow_matching import ODEEulerFlowMatchingSolver
# import utils
# import libs.autoencoder
# from libs.clip import FrozenCLIPEmbedder
# from configs import t2i_512px_clip_dimr


# def unpreprocess(x: torch.Tensor) -> torch.Tensor:
#     x = 0.5 * (x + 1.0)
#     x.clamp_(0.0, 1.0)
#     return x
    
# def cosine_similarity_torch(latent1: torch.Tensor, latent2: torch.Tensor) -> torch.Tensor:
#     latent1_flat = latent1.view(-1)
#     latent2_flat = latent2.view(-1)
#     cosine_similarity = F.cosine_similarity(
#         latent1_flat.unsqueeze(0), latent2_flat.unsqueeze(0), dim=1
#     )
#     return cosine_similarity

# def kl_divergence(latent1: torch.Tensor, latent2: torch.Tensor) -> torch.Tensor:
#     latent1_prob = F.softmax(latent1, dim=-1)
#     latent2_prob = F.softmax(latent2, dim=-1)
#     latent1_log_prob = torch.log(latent1_prob)
#     kl_div = F.kl_div(latent1_log_prob, latent2_prob, reduction="batchmean")
#     return kl_div

# def batch_decode(_z: torch.Tensor, decode, batch_size: int = 10) -> torch.Tensor:
#     num_samples = _z.size(0)
#     decoded_batches = []

#     for i in range(0, num_samples, batch_size):
#         batch = _z[i : i + batch_size]
#         decoded_batch = decode(batch)
#         decoded_batches.append(decoded_batch)

#     return torch.cat(decoded_batches, dim=0)

# def get_caption(llm: str, text_model, prompt_dict: dict, batch_size: int):
#     if batch_size == 3:
#         # Only addition or only subtraction mode.
#         assert len(prompt_dict) == 2, "Expected 2 prompts for batch_size 3."
#         batch_prompts = list(prompt_dict.values()) + [" "]
#     elif batch_size == 4:
#         # Addition and subtraction mode.
#         assert len(prompt_dict) == 3, "Expected 3 prompts for batch_size 4."
#         batch_prompts = list(prompt_dict.values()) + [" "]
#     elif batch_size >= 5:
#         # Linear interpolation mode.
#         assert len(prompt_dict) == 2, "Expected 2 prompts for linear interpolation."
#         batch_prompts = [prompt_dict["prompt_1"]] + [" "] * (batch_size - 2) + [prompt_dict["prompt_2"]]
#     else:
#         raise ValueError(f"Unsupported batch_size: {batch_size}")

#     if llm == "clip":
#         latent, latent_and_others = text_model.encode(batch_prompts)
#         context = latent_and_others["token_embedding"].detach()
#     elif llm == "t5":
#         latent, latent_and_others = text_model.get_text_embeddings(batch_prompts)
#         context = (latent_and_others["token_embedding"] * 10.0).detach()
#     else:
#         raise NotImplementedError(f"Language model {llm} not supported.")

#     token_mask = latent_and_others["token_mask"].detach()
#     tokens = latent_and_others["tokens"].detach()
#     captions = batch_prompts

#     return context, token_mask, tokens, captions

# # Load configuration and initialize models.
# config_dict = t2i_512px_clip_dimr.get_config()
# config = ml_collections.ConfigDict(config_dict)

# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# logging.info(f"Using device: {device}")

# # Freeze configuration.
# config = ml_collections.FrozenConfigDict(config)

# torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
# MAX_SEED = np.iinfo(np.int32).max
# MAX_IMAGE_SIZE = 1024  # Currently not used.

# # Load the main diffusion model.
# repo_id = "QHL067/CrossFlow"
# filename = "pretrained_models/t2i_512px_clip_dimr.pth"
# checkpoint_path = hf_hub_download(repo_id=repo_id, filename=filename)
# nnet = utils.get_nnet(**config.nnet)
# nnet = nnet.to(device)
# state_dict = torch.load(checkpoint_path, map_location=device)
# nnet.load_state_dict(state_dict)
# nnet.eval()

# # Initialize text model.
# llm = "clip"
# clip = FrozenCLIPEmbedder()
# clip.eval()
# clip.to(device)

# # Load autoencoder.
# autoencoder = libs.autoencoder.get_model(**config.autoencoder)
# autoencoder.to(device)


# @torch.cuda.amp.autocast()
# def encode(_batch: torch.Tensor) -> torch.Tensor:
#     """Encode a batch of images using the autoencoder."""
#     return autoencoder.encode(_batch)


# @torch.cuda.amp.autocast()
# def decode(_batch: torch.Tensor) -> torch.Tensor:
#     """Decode a batch of latent vectors using the autoencoder."""
#     return autoencoder.decode(_batch)


# @spaces.GPU #[uncomment to use ZeroGPU]
# def infer(
#     prompt1,
#     prompt2,
#     seed,
#     randomize_seed,
#     guidance_scale,
#     num_inference_steps,
#     num_of_interpolation,
#     save_gpu_memory=True,
#     progress=gr.Progress(track_tqdm=True),
# ):
#     if randomize_seed:
#         seed = random.randint(0, MAX_SEED)

#     torch.manual_seed(seed)
#     if device.type == "cuda":
#         torch.cuda.manual_seed_all(seed)

#     # Only support interpolation in this implementation.
#     prompt_dict = {"prompt_1": prompt1, "prompt_2": prompt2}
#     for key, value in prompt_dict.items():
#         assert value is not None, f"{key} must not be None."
#     assert num_of_interpolation >= 5, "For linear interpolation, please sample at least five images."

#     # Get text embeddings and tokens.
#     _context, _token_mask, _token, _caption = get_caption(
#         llm, clip, prompt_dict=prompt_dict, batch_size=num_of_interpolation
#     )

#     with torch.no_grad():
#         _z_gaussian = torch.randn(num_of_interpolation, *config.z_shape, device=device)
#         _z_x0, _mu, _log_var = nnet(
#             _context, text_encoder=True, shape=_z_gaussian.shape, mask=_token_mask
#         )
#         _z_init = _z_x0.reshape(_z_gaussian.shape)

#         # Prepare the initial latent representations based on the number of interpolations.
#         if num_of_interpolation == 3:
#             # Addition or subtraction mode.
#             if config.prompt_a is not None:
#                 assert config.prompt_s is None, "Only one of prompt_a or prompt_s should be provided."
#                 z_init_temp = _z_init[0] + _z_init[1]
#             elif config.prompt_s is not None:
#                 assert config.prompt_a is None, "Only one of prompt_a or prompt_s should be provided."
#                 z_init_temp = _z_init[0] - _z_init[1]
#             else:
#                 raise NotImplementedError("Either prompt_a or prompt_s must be provided for 3-sample mode.")
#             mean = z_init_temp.mean()
#             std = z_init_temp.std()
#             _z_init[2] = (z_init_temp - mean) / std

#         elif num_of_interpolation == 4:
#             z_init_temp = _z_init[0] + _z_init[1] - _z_init[2]
#             mean = z_init_temp.mean()
#             std = z_init_temp.std()
#             _z_init[3] = (z_init_temp - mean) / std

#         elif num_of_interpolation >= 5:
#             tensor_a = _z_init[0]
#             tensor_b = _z_init[-1]
#             num_interpolations = num_of_interpolation - 2
#             interpolations = [
#                 tensor_a + (tensor_b - tensor_a) * (i / (num_interpolations + 1))
#                 for i in range(1, num_interpolations + 1)
#             ]
#             _z_init = torch.stack([tensor_a] + interpolations + [tensor_b], dim=0)

#         else:
#             raise ValueError("Unsupported number of interpolations.")

#         assert guidance_scale > 1, "Guidance scale must be greater than 1."

#         has_null_indicator = hasattr(config.nnet.model_args, "cfg_indicator")
#         ode_solver = ODEEulerFlowMatchingSolver(
#             nnet,
#             bdv_model_fn=None,
#             step_size_type="step_in_dsigma",
#             guidance_scale=guidance_scale,
#         )
#         _z, _ = ode_solver.sample(
#             x_T=_z_init,
#             batch_size=num_of_interpolation,
#             sample_steps=num_inference_steps,
#             unconditional_guidance_scale=guidance_scale,
#             has_null_indicator=has_null_indicator,
#         )

#         if save_gpu_memory:
#             image_unprocessed = batch_decode(_z, decode)
#         else:
#             image_unprocessed = decode(_z)

#         samples = unpreprocess(image_unprocessed).contiguous()[0]

#     # return samples, seed
#     return seed


# # examples = [
# #     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
# #     "An astronaut riding a green horse",
# #     "A delicious ceviche cheesecake slice",
# # ]

# examples = [
#     ["A dog cooking dinner in the kitchen", "An orange cat wearing sunglasses on a ship"],
# ]

# css = """
# #col-container {
#     margin: 0 auto;
#     max-width: 640px;
# }
# """

# with gr.Blocks(css=css) as demo:
#     with gr.Column(elem_id="col-container"):
#         gr.Markdown(" # CrossFlow")
#         gr.Markdown(" CrossFlow directly transforms text representations into images for text-to-image generation, enabling interpolation in the input text latent space.")

#         with gr.Row():
#             prompt1 = gr.Text(
#                 label="Prompt_1",
#                 show_label=False,
#                 max_lines=1,
#                 placeholder="Enter your prompt for the first image",
#                 container=False,
#             )
        
#         with gr.Row():
#             prompt2 = gr.Text(
#                 label="Prompt_2",
#                 show_label=False,
#                 max_lines=1,
#                 placeholder="Enter your prompt for the second image",
#                 container=False,
#             )

#         with gr.Row():
#             run_button = gr.Button("Run", scale=0, variant="primary")

#         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():
#                 guidance_scale = gr.Slider(
#                     label="Guidance scale",
#                     minimum=0.0,
#                     maximum=10.0,
#                     step=0.1,
#                     value=7.0,  # Replace with defaults that work for your model
#                 )
#             with gr.Row():
#                 num_inference_steps = gr.Slider(
#                     label="Number of inference steps",
#                     minimum=1,
#                     maximum=50,
#                     step=1,
#                     value=50,  # Replace with defaults that work for your model
#                 )
#             with gr.Row():
#                 num_of_interpolation = gr.Slider(
#                     label="Number of images for interpolation",
#                     minimum=5,
#                     maximum=50,
#                     step=1,
#                     value=10,  # Replace with defaults that work for your model
#                 )

#         gr.Examples(examples=examples, inputs=[prompt1, prompt2])
#     gr.on(
#         triggers=[run_button.click, prompt1.submit, prompt2.submit],
#         fn=infer,
#         inputs=[
#             prompt1,
#             prompt2,
#             seed,
#             randomize_seed,
#             guidance_scale,
#             num_inference_steps,
#             num_of_interpolation,
#         ],
#         # outputs=[result, seed],
#         outputs=[seed],
#     )

# if __name__ == "__main__":
#     demo.launch()

import gradio as gr
import numpy as np
import random

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    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,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    print('image.shape')
    print(image.shape)

    return image, seed


examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Template")

        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, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=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,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=2,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch()