Program not working on GPU but works on CPU

Intermediate
3

Hello

I am getting the following error after modifying the code:


(venv) D:\Ganu\AIImage\project\Train-10Images-chatgptParameters\runs\1sstrun-23thApril2025\generation\1stGo>python John-Training-12thFeb2025-original.py
2025-05-15 09:56:28,925 - INFO - Initializing...
2025-05-15 09:56:28,951 - INFO - Using seed: 1421589
2025-05-15 09:56:28,951 - INFO - Loading base model and LoRA weights...
2025-05-15 09:56:29,970 - ERROR - Failed to load model pipeline: auto not supported. Supported strategies are: balanced
Traceback (most recent call last):
  File "D:\Ganu\AIImage\project\Train-10Images-chatgptParameters\runs\1sstrun-23thApril2025\generation\1stGo\John-Training-12thFeb2025-original.py", line 52, in <module>
    pipeline = AutoPipelineForText2Image.from_pretrained(
  File "D:\Ganu\AIImage\venv\lib\site-packages\huggingface_hub\utils\_validators.py", line 114, in _inner_fn
    return fn(*args, **kwargs)
  File "D:\Ganu\AIImage\venv\lib\site-packages\diffusers\pipelines\auto_pipeline.py", line 443, in from_pretrained
    return text_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs)
  File "D:\Ganu\AIImage\venv\lib\site-packages\huggingface_hub\utils\_validators.py", line 114, in _inner_fn
    return fn(*args, **kwargs)
  File "D:\Ganu\AIImage\venv\lib\site-packages\diffusers\pipelines\pipeline_utils.py", line 745, in from_pretrained
    raise NotImplementedError(
NotImplementedError: auto not supported. Supported strategies are: balanced

The program is as follows:

import logging
from diffusers import AutoPipelineForText2Image, AutoencoderKL
import torch
import numpy as np
import random
import os
from PIL import Image

# =========================
# STEP 0: Logging Setup
# =========================
log_file = "generation_log.txt"
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler(log_file),
        logging.StreamHandler()
    ]
)

logging.info("Initializing...")

# =========================
# STEP 1: Environment Setup
# =========================

torch.cuda.empty_cache()
torch.cuda.ipc_collect()

seed = random.randint(0, 9999999)
torch.manual_seed(seed)
np.random.seed(seed)
logging.info(f"Using seed: {seed}")

# ===============================
# STEP 2: Model and LoRA Setup
# ===============================
logging.info("Loading base model and LoRA weights...")

model_dir = "D:\\Ganu\\AIImage\\huggingface\\kohya_ss\\kohya_ss\\outputs"
lora_weights_path = os.path.join(model_dir, "model")
model_id = "stabilityai/stable-diffusion-xl-base-1.0"

# Optional: Custom VAE (uncomment if needed)
vae = AutoencoderKL.from_pretrained(
     "madebyollin/sdxl-vae-fp16-fix",
     torch_dtype=torch.float16
 ).to("cuda")

try:
    pipeline = AutoPipelineForText2Image.from_pretrained(
        model_id,
        torch_dtype=torch.float16,
        variant="fp16",
        device_map="auto"
    ).to("cuda")
    logging.info("Pipeline loaded to GPU with float16.")
except Exception as e:
    logging.error(f"Failed to load model pipeline: {e}")
    raise

# If using VAE:
pipeline.vae = vae

pipeline.enable_attention_slicing()
pipeline.enable_vae_slicing()

try:
    pipeline.load_lora_weights(lora_weights_path, weight_name="last.safetensors")
    logging.info("LoRA weights loaded successfully.")
except ValueError as e:
    logging.error("Invalid LoRA checkpoint. Check the format or compatibility.")
    raise e

# =========================
# STEP 3: Prompt Inference
# =========================
text_prompt = (
    "A wide, breathtaking landscape with all real vibrant nature-themed background, lush forests, mountains, and a Doctor standing prominently in the foreground"
)

negative_prompt = (
    "text, letters, words, signage, logos, labels, writing, messy background, busy layout, clutter, double faces, abstract shapes, UI panels with words, overlapping elements, header, footer, top bar, navigation bar, bottom menu, toolbar, top text, website layout, browser frame, button row, page border, UI bar"
)

logging.info(f"Running inference with prompt: {text_prompt}")

try:
    result = pipeline(
        prompt=text_prompt,
        negative_prompt=negative_prompt,
        guidance_scale=7.5,
        num_inference_steps=30
    )
    generated_image = result.images[0]
    output_path = f"generated_image_{seed}.png"
    generated_image.save(output_path)
    logging.info(f"Image saved to: {output_path}")
    generated_image.show()
except Exception as e:
    logging.error(f"Error during image generation: {e}")
    raise

And i get the same error with “balanced” too!

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