Update

app.py

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+import gradio as gr
+import torch
+from torch import Tensor, nn
+from transformers import (CLIPTextModel, CLIPTokenizer, T5EncoderModel,
+                          T5Tokenizer)
+import spaces
+import numpy as np
+import io
+import base64
+
+class HFEmbedder(nn.Module):
+    def __init__(self, version: str, max_length: int, **hf_kwargs):
+        super().__init__()
+        self.is_clip = version.startswith("openai")
+        self.max_length = max_length
+        self.output_key = "pooler_output" if self.is_clip else "last_hidden_state"
+
+        if self.is_clip:
+            self.tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(version, max_length=max_length)
+            self.hf_module: CLIPTextModel = CLIPTextModel.from_pretrained(version, **hf_kwargs)
+        else:
+            self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(version, max_length=max_length)
+            self.hf_module: T5EncoderModel = T5EncoderModel.from_pretrained(version, **hf_kwargs)
+
+        self.hf_module = self.hf_module.eval().requires_grad_(False)
+
+    def forward(self, text: list[str]) -> Tensor:
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=False,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        outputs = self.hf_module(
+            input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
+            attention_mask=None,
+            output_hidden_states=False,
+        )
+        return outputs[self.output_key]
+
+
+
+def load_t5(device: str | torch.device = "cuda", max_length: int = 512) -> HFEmbedder:
+    # max length 64, 128, 256 and 512 should work (if your sequence is short enough)
+    return HFEmbedder("lnyan/t5-v1_1-xxl-encoder", max_length=max_length, torch_dtype=torch.bfloat16).to("cuda")
+
+
+def load_clip(device: str | torch.device = "cuda") -> HFEmbedder:
+    return HFEmbedder("openai/clip-vit-large-patch14", max_length=77, torch_dtype=torch.bfloat16).to("cuda")
+
+@spaces.GPU(duration=20)
+def load_encoders():
+    is_schnell = True
+    t5 = load_t5("cuda", max_length=256 if is_schnell else 512)
+    clip = load_clip("cuda")
+    return t5, clip
+
+import numpy as np
+def b64(txt,vec):
+    buffer = io.BytesIO()
+    np.savez_compressed(buffer, txt=txt, vec=vec)
+    buffer.seek(0)
+    encoded = base64.b64encode(buffer.getvalue()).decode('utf-8')
+    return encoded
+
+t5,clip=load_encoders()
+@spaces.GPU(duration=10)
+def convert(prompt):
+    if isinstance(prompt, str):
+        prompt = [prompt]
+    txt = t5(prompt)
+    vec = clip(prompt)
+    return b64(txt.cpu().numpy(),vec.cpu().numpy())
+
+with gr.Blocks() as demo:
+    gr.Markdown("""A workaround for flux-flax to fit into 40G VRAM""")
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="prompt")
+            convert_btn = gr.Button(value="Convert")
+        with gr.Column():
+            output = gr.Textbox(label="output")
+
+    convert_btn.click(convert, inputs=prompt, outputs=output, api_name="convert")
+
+
+demo.launch()

requirements.txt

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+torch
+torchvision
+opencv-python-headless
+einops
+huggingface_hub
+transformers
+tokenizers
+sentencepiece
+invisible-watermark