Spaces:
Sleeping
Sleeping
File size: 10,733 Bytes
4f48282 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 |
#img_gen_modal.py
import modal
import random
import io
from config.config import prompts, models # Indirect import
import os
import gradio as gr
import torch
import sentencepiece
import torch
from huggingface_hub import login
from transformers import AutoTokenizer
import random
from datetime import datetime
####### IMPORTS FOR LIVE PREVIEW
import numpy as np
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
######### LIVE PREVIEW TEST 1/3 ##########
from live_preview_helpers import flux_pipe_call_that_returns_an_iterable_of_images
##########################################
CACHE_DIR = "/model_cache"
# Define the Modal image
image = (
modal.Image.from_registry("nvidia/cuda:12.2.0-devel-ubuntu22.04", add_python="3.9").pip_install_from_requirements("requirements.txt")
#modal.Image.debian_slim(python_version="3.9") # Base image
.env(
{
"HF_HUB_ENABLE_HF_TRANSFER": "1", "HF_HOME": "HF_HOME", "HF_HUB_CACHE": CACHE_DIR
}
)
)
# Create a Modal app
app = modal.App("img-gen-modal", image=image)
with image.imports():
import os
flux_model_vol = modal.Volume.from_name("flux-model-vol", create_if_missing=True) # Reference your volume
# GPU FUNCTION
@app.function(volumes={"/data": flux_model_vol},
secrets=[modal.Secret.from_name("huggingface-token")],
gpu="L40S",
timeout = 300
)
def generate_image_gpu(prompt_alias, team_color, model_alias, custom_prompt):
image = generate_image(prompt_alias, team_color, model_alias, custom_prompt)
return image, "Image generated successfully! Call the banners!"
# CPU FUNCTION
@app.function(volumes={"/data": flux_model_vol},
secrets=[modal.Secret.from_name("huggingface-token")],
cpu = 1,
timeout = 300
)
def generate_image_cpu(prompt_alias, team_color, model_alias, custom_prompt):
image = generate_image(prompt_alias, team_color, model_alias, custom_prompt)
return image, "Image generated successfully! Call the banners!"
# MAIN GENERATE IMAGE FUNCTION
def generate_image(
prompt_alias,
team_color,
model_alias,
custom_prompt,
height=360,
width=640,
num_inference_steps=20,
guidance_scale=2.0,
seed=-1,
progress=gr.Progress(track_tqdm=True) # Add progress parameter
):
with modal.enable_output():
print("Hello from ctb_modal!")
########### LIVE PREVIEW 2/3 ##################
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
taef1 = AutoencoderTiny.from_pretrained("/data/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained("/data/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
pipe_LIVE = DiffusionPipeline.from_pretrained("/data/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
torch.cuda.empty_cache()
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
pipe_LIVE.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe_LIVE)
#################################################
print("Running debug check...")
# Debug function to check installed packages
def check_dependencies():
packages = [
"diffusers", # For Stable Diffusion
"transformers", # For Hugging Face models
"torch", # PyTorch
"accelerate", # For distributed training/inference
"gradio", # For the Gradio interface (updated to latest version)
"safetensors", # For safe model loading
"pillow", # For image processing
"sentencepiece"
]
for package in packages:
try:
import importlib
module = importlib.import_module(package)
print(f" {package} is installed. Version:")
except ImportError:
print(f" {package} is NOT installed.")
check_dependencies()
# Find the selected prompt and model
try:
prompt = next(p for p in prompts if p["alias"] == prompt_alias)["text"]
model_name = next(m for m in models if m["alias"] == model_alias)["name"]
except StopIteration:
return None, "ERROR: Invalid prompt or model selected."
# Determine the enemy color
enemy_color = "blue" if team_color.lower() == "red" else "red"
# Print the original prompt and dynamic values for debugging
print("Original Prompt:")
print(prompt)
print(f"Enemy Color: {enemy_color}")
print(f"Team Color: {team_color.lower()}")
prompt = prompt.format(team_color=team_color.lower(), enemy_color=enemy_color)
# Print the formatted prompt for debugging
print("\nFormatted Prompt:")
print(prompt)
# Append the custom prompt (if provided)
if custom_prompt and len(custom_prompt.strip()) > 0:
prompt += " " + custom_prompt.strip()
# Randomize the seed if needed
if seed == -1:
seed = random.randint(0, 1000000)
try:
#from diffusers import FluxPipeline
print("Initializing HF TOKEN")
hf_token = os.environ["HF_TOKEN"]
print(hf_token)
print("HF TOKEN:")
login(token=hf_token)
print("model_name:")
print(model_name)
# Use absolute path with leading slash
local_path = f"/data/{model_name}" # Changed from "data/" to "/data/"
print(f"Loading model from local path: {local_path}")
# Debug: Check if the directory exists and list its contents
if os.path.exists(local_path):
print("Directory exists. Contents:")
for item in os.listdir(local_path):
print(f" - {item}")
else:
print(f"Directory does not exist: {local_path}")
print("Contents of /data:")
print(os.listdir("/data"))
# CHECK FOR TORCH USING CUDA
print("CHECK FOR TORCH USING CUDA")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print("inside if")
print(f"CUDA device count: {torch.cuda.device_count()}")
print(f"Current device: {torch.cuda.current_device()}")
print(f"Device name: {torch.cuda.get_device_name(torch.cuda.current_device())}")
# ########## INITIALIZING CPU PIPE ##########
# print("-----INITIALIZING PIPE-----")
# pipe = FluxPipeline.from_pretrained(
# local_path,
# torch_dtype=torch.bfloat16,
# #torch_dtype=torch.float16,
# #torch_dtype=torch.float32,
# local_files_only=True
# )
# if torch.cuda.is_available():
# print("CUDA available")
# print("using gpu")
# pipe = pipe.to("cuda")
# pipe_message = "CUDA"
# else:
# print("CUDA not available")
# print("using cpu")
# pipe = pipe.to("cpu")
# pipe_message = "CPU"
# # pipe.enable_model_cpu_offload() # Use official recommended method
# print(f"-----{pipe_message} PIPE INITIALIZED-----")
# print(f"Using device: {pipe.device}")
# except Exception as e:
# print(f"Detailed error: {str(e)}")
# return None, f"ERROR: Failed to initialize PIPE2. Details: {e}"
# try:
# print("-----SENDING IMG GEN TO PIPE-----")
# print("-----HOLD ON-----")
# ################################################
################ LIVE PREVIEW TEST 3/3 ####################
print("-----SENDING IMG GEN TO PIPE LIVE-----")
print("-----HOLD ON-----")
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
for image in pipe_LIVE.flux_pipe_call_that_returns_an_iterable_of_images(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
output_type="pil",
good_vae=good_vae,
):
yield image, "Update"
############################################################
# ########## SENDING IMG GEN TO PIPE - WORKING CODE ##########
# image = pipe(
# prompt,
# guidance_scale=guidance_scale,
# num_inference_steps=num_inference_steps,
# width=width,
# height=height,
# max_sequence_length=512,
# # seed=seed
# ).images[0]
# #############################################################
print("-----IMAGE GENERATED SUCCESSFULLY!-----")
print(image)
except Exception as e:
return f"ERROR: Failed to initialize InferenceClient. Details: {e}"
try:
print("-----SAVING-----")
print("-----DONE!-----")
print("-----CALL THE BANNERS!-----")
# Save the image with a timestamped filename
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_filename = f"/data/images/{timestamp}_{model_alias.replace(' ', '_').lower()}_{prompt_alias.replace(' ', '_').lower()}_{team_color.lower()}.png"
# Save the image using PIL's save method
image.save(output_filename)
print(f"File path: {output_filename}")
except Exception as e:
print(f"ERROR: Failed to save image. Details: {e}")
# Return the filename and success message
return image |