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Update app.py
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app.py
CHANGED
@@ -2,27 +2,15 @@ import os
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import gradio as gr
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import torch
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from diffusers import StableDiffusionPipeline
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from PIL import Image
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import
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model_id = "runwayml/stable-diffusion-v1-5"
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pipe = StableDiffusionPipeline.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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)
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model = RM.load("ImageReward-v1.0")
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images_in_gallery = []
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rewards_in_gallery = []
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# event functions
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def generate_images(
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prompt, magic_words, num, height, width, num_inference_steps, guidance_scale
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):
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@@ -31,14 +19,33 @@ def generate_images(
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if magic_words is not None:
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prompt += ", ".join(magic_words)
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rewards_in_gallery = [None] * len(images_in_gallery)
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return list(zip(images_in_gallery, rewards_in_gallery))
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@@ -50,9 +57,25 @@ def score_and_rank(prompt):
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if num_not_scored > 0:
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images_to_score = images_in_gallery[-num_not_scored:]
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with torch.no_grad():
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if not isinstance(rewards, list):
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rewards = [rewards]
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rewards_in_gallery = rewards_in_gallery[:-num_not_scored] + rewards
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import gradio as gr
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import torch
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from PIL import Image
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import io
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import base64
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import requests
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import json
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images_in_gallery = []
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rewards_in_gallery = []
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def generate_images(
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prompt, magic_words, num, height, width, num_inference_steps, guidance_scale
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):
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if magic_words is not None:
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prompt += ", ".join(magic_words)
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# post 请求发送到服务器
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# 定义请求的 URL 和数据
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url = 'https://tianqi.aminer.cn/image_reward_hf/generate_image'
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data = {'prompt': prompt,
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'height': height,
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'width':width,
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'num_inference_steps':num_inference_steps,
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'guidance_scale':guidance_scale,
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'num':num
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}
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headers = {'Content-Type': 'application/json'}
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# 发送 POST 请求
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data = json.dumps(data)
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response = requests.post(url, data=data, headers=headers)
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image_ls = response.json()['image_list']
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images_in_gallery = []
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for base_image in image_ls:
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image_bytes = base64.b64decode(base_image)
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# 创建 BytesIO 对象并读取图像字节流
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image_stream = io.BytesIO(image_bytes)
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# 打开图像
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image = Image.open(image_stream)
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images_in_gallery.append(image)
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rewards_in_gallery = [None] * len(images_in_gallery)
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return list(zip(images_in_gallery, rewards_in_gallery))
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if num_not_scored > 0:
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images_to_score = images_in_gallery[-num_not_scored:]
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image_ls = []
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for image in images_to_score:
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image_bytes = io.BytesIO()
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image.save(image_bytes, format='JPEG')
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image_bytes.seek(0)
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# 将字节流转换为 Base64 编码
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base64_image = base64.b64encode(image_bytes.read()).decode('utf-8')
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image_ls.append(base64_image)
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with torch.no_grad():
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# post 请求发送到服务器
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url = 'https://tianqi.aminer.cn/image_reward_hf/score_and_rank'
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data = {'images_to_score': image_ls, 'prompt':prompt}
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data = json.dumps(data)
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headers = {'Content-Type': 'application/json'}
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# 发送 POST 请求
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response = requests.post(url, data=data, headers=headers)
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rewards = response.json()['rewards']
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if not isinstance(rewards, list):
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rewards = [rewards]
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rewards_in_gallery = rewards_in_gallery[:-num_not_scored] + rewards
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