Revert "增加了可以直接使用pro 7b的api服务器,客户端可以直接使用 fastapi_client.py"

This reverts commit 5376490061.

demo/fastapi_pro.py

@@ -1,186 +0,0 @@
-from fastapi import FastAPI, File, Form, UploadFile, HTTPException
-from fastapi.responses import JSONResponse, StreamingResponse
-import torch
-from transformers import AutoConfig, AutoModelForCausalLM
-from janus.models import MultiModalityCausalLM, VLChatProcessor
-from PIL import Image
-import numpy as np
-import io
-
-app = FastAPI()
-
-
-
-# Load model and processor
-model_path = "deepseek-ai/Janus-Pro-7B"
-
-config = AutoConfig.from_pretrained(model_path)
-language_config = config.language_config
-language_config._attn_implementation = 'eager'
-vl_gpt = AutoModelForCausalLM.from_pretrained(model_path,
-                                              language_config=language_config,
-                                              trust_remote_code=True)
-if torch.cuda.is_available():
-    vl_gpt = vl_gpt.to(torch.bfloat16).cuda()
-else:
-    vl_gpt = vl_gpt.to(torch.float16)
-
-vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
-tokenizer = vl_chat_processor.tokenizer
-cuda_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-
-@torch.inference_mode()
-def multimodal_understanding(image_data, question, seed, top_p, temperature):
-    torch.cuda.empty_cache()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    torch.cuda.manual_seed(seed)
-
-    conversation = [
-        {
-            "role": "<|User|>",
-            "content": f"<image_placeholder>\n{question}",
-            "images": [image_data],
-        },
-        {"role": "<|Assistant|>", "content": ""},
-    ]
-    
-    # load images and prepare for inputs
-    pil_images =  [Image.open(io.BytesIO(image_data))]
-    prepare_inputs = vl_chat_processor(
-        conversations=conversation, images=pil_images, force_batchify=True
-    ).to(vl_gpt.device)
-    
-    # # run image encoder to get the image embeddings
-    inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
-    
-    # # run the model to get the response
-    outputs = vl_gpt.language_model.generate(
-        inputs_embeds=inputs_embeds,
-        attention_mask=prepare_inputs.attention_mask,
-        pad_token_id=tokenizer.eos_token_id,
-        bos_token_id=tokenizer.bos_token_id,
-        eos_token_id=tokenizer.eos_token_id,
-        max_new_tokens=512,
-        do_sample=False,
-        use_cache=True,
-    )
-
-    answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
-    return answer
-
-
-@app.post("/understand_image_and_question/")
-async def understand_image_and_question(
-    file: UploadFile = File(...),
-    question: str = Form(...),
-    seed: int = Form(42),
-    top_p: float = Form(0.95),
-    temperature: float = Form(0.1)
-):
-    image_data = await file.read()
-    response = multimodal_understanding(image_data, question, seed, top_p, temperature)
-    return JSONResponse({"response": response})
-
-
-def generate(input_ids,
-             width,
-             height,
-             temperature: float = 1,
-             parallel_size: int = 5,
-             cfg_weight: float = 5,
-             image_token_num_per_image: int = 576,
-             patch_size: int = 16):
-    torch.cuda.empty_cache()
-    tokens = torch.zeros((parallel_size * 2, len(input_ids)), dtype=torch.int).to(cuda_device)
-    for i in range(parallel_size * 2):
-        tokens[i, :] = input_ids
-        if i % 2 != 0:
-            tokens[i, 1:-1] = vl_chat_processor.pad_id
-    inputs_embeds = vl_gpt.language_model.get_input_embeddings()(tokens)
-    generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).to(cuda_device)
-
-    pkv = None
-    for i in range(image_token_num_per_image):
-        outputs = vl_gpt.language_model.model(inputs_embeds=inputs_embeds, use_cache=True, past_key_values=pkv)
-        pkv = outputs.past_key_values
-        hidden_states = outputs.last_hidden_state
-        logits = vl_gpt.gen_head(hidden_states[:, -1, :])
-        logit_cond = logits[0::2, :]
-        logit_uncond = logits[1::2, :]
-        logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond)
-        probs = torch.softmax(logits / temperature, dim=-1)
-        next_token = torch.multinomial(probs, num_samples=1)
-        generated_tokens[:, i] = next_token.squeeze(dim=-1)
-        next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
-        img_embeds = vl_gpt.prepare_gen_img_embeds(next_token)
-        inputs_embeds = img_embeds.unsqueeze(dim=1)
-    patches = vl_gpt.gen_vision_model.decode_code(
-        generated_tokens.to(dtype=torch.int), 
-        shape=[parallel_size, 8, width // patch_size, height // patch_size]
-    )
-
-    return generated_tokens.to(dtype=torch.int), patches
-
-
-def unpack(dec, width, height, parallel_size=5):
-    dec = dec.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1)
-    dec = np.clip((dec + 1) / 2 * 255, 0, 255)
-
-    visual_img = np.zeros((parallel_size, width, height, 3), dtype=np.uint8)
-    visual_img[:, :, :] = dec
-
-    return visual_img
-
-
-@torch.inference_mode()
-def generate_image(prompt, seed, guidance):
-    torch.cuda.empty_cache()
-    seed = seed if seed is not None else 12345
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    np.random.seed(seed)
-    width = 384
-    height = 384
-    parallel_size = 5
-    
-    with torch.no_grad():
-        messages = [{'role': '<|User|>', 'content': prompt},{'role': '<|Assistant|>', 'content': ''}]
-        text = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(
-            conversations=messages,
-            sft_format=vl_chat_processor.sft_format,
-            system_prompt=''
-        )
-        text = text + vl_chat_processor.image_start_tag
-        input_ids = torch.LongTensor(tokenizer.encode(text))
-        _, patches = generate(input_ids, width // 16 * 16, height // 16 * 16, cfg_weight=guidance, parallel_size=parallel_size)
-        images = unpack(patches, width // 16 * 16, height // 16 * 16)
-
-        return [Image.fromarray(images[i]).resize((1024, 1024), Image.LANCZOS) for i in range(parallel_size)]
-
-
-@app.post("/generate_images/")
-async def generate_images(
-    prompt: str = Form(...),
-    seed: int = Form(None),
-    guidance: float = Form(5.0),
-):
-    try:
-        images = generate_image(prompt, seed, guidance)
-        def image_stream():
-            for img in images:
-                buf = io.BytesIO()
-                img.save(buf, format='PNG')
-                buf.seek(0)
-                yield buf.read()
-
-        return StreamingResponse(image_stream(), media_type="multipart/related")
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Image generation failed: {str(e)}")
-
-
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)