Droider & VoltScribe
VoltScribe
Hey Droider, have you checked out the latest AI art tools—like Midjourney and Stable Diffusion? I’m dying to know if they’re a real breakthrough or just another hype.
Droider
Yeah, they’re kinda insane, but not a total revolution. Midjourney’s got that slick style‑shift vibe, while Stable Diffusion lets you tweak the code itself. If you’re a coder‑artist, the sandbox is the real gold—just don’t expect a magic wand. Keep hacking and see what you can remix.
VoltScribe
Absolutely, the hype curve’s peaked but the real juice is still in the code—think custom loss functions, or stacking a GAN on top of diffusion to tweak realism. If we could auto‑generate prompts that learn from user feedback, the next leap might actually happen. What’s your current stack? Let's see where we can plug a tweak.
Droider
Right now I’m juggling a few things—Python, PyTorch, and a custom lib for generative modeling. I keep most of the heavy lifting on a GPU‑cluster, and I mix in a bit of TensorFlow for the classic GAN bits. I usually run a tiny FastAPI wrapper for the UI so I can feed prompts and tweak weights on the fly. What’s your plan for the prompt‑learning loop? Maybe we can swap a hook in.
VoltScribe
Nice stack—Python + PyTorch + TF + FastAPI is a power combo. For the prompt‑learning loop, I’d try a two‑stage thing: first, use a simple language model (like GPT‑2 or a distilled BERT) to generate a pool of seed prompts from a seed image set. Then, feed those prompts into your diffusion/GAN pipeline and score the outputs with a learned reward head—maybe a small CNN that predicts human‑rated quality or a VAE latent similarity to the target style. That reward can be fed back into a policy network that tweaks the prompt distribution, like a REINFORCE loop. Swap a hook by inserting the reward head right after the decoder, so the model learns which prompt words drive higher‑score images. It’s a bit of a dance, but the feedback loop can push the prompt space toward what actually matters. Want a code skeleton?
Droider
Sounds solid, let’s keep it tight. Here’s a bare‑bones sketch that plugs into what you described. Copy, tweak, test.
```python
import torch, transformers
from diffusers import StableDiffusionPipeline
from torch.nn import functional as F
# 1️⃣ Prompt generator
tokenizer = transformers.AutoTokenizer.from_pretrained("gpt2")
model = transformers.AutoModelForCausalLM.from_pretrained("gpt2")
def seed_prompts(seed_imgs, n=10):
prompts = []
for img in seed_imgs:
# embed image into text space (you need a CLIP or similar)
# dummy: just use image path as prompt seed
prompt = f"image:{img}"
inputs = tokenizer(prompt, return_tensors="pt")
out = model.generate(**inputs, max_length=50, num_return_sequences=n)
prompts.extend([tokenizer.decode(o, skip_special_tokens=True) for o in out])
return prompts
# 2️⃣ Diffusion pipeline
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe = pipe.to("cuda")
# 3️⃣ Reward head (simple CNN)
class RewardHead(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(4, 1, 3, padding=1)
def forward(self, img):
return torch.sigmoid(self.conv(img)).mean()
reward_head = RewardHead().to("cuda")
# 4️⃣ Policy network (just a simple linear for demo)
policy = torch.nn.Linear(768, len(tokenizer))
policy.to("cuda")
optimizer = torch.optim.Adam(policy.parameters(), lr=1e-4)
def train_step(prompts):
images = pipe(prompts, num_inference_steps=50).images # list of PIL images
# Convert to tensor
imgs = torch.stack([torch.tensor(im).permute(2,0,1).float()/255. for im in images]).to("cuda")
rewards = reward_head(imgs).detach()
# RL loss (REINFORCE style)
logits = policy(torch.randn(imgs.size(0), 768).to("cuda")) # dummy state
log_probs = F.log_softmax(logits, dim=1)
loss = -(log_probs * rewards.unsqueeze(1)).mean()
optimizer.zero_grad()
loss.backward()
optimizer.step()
return loss.item()
# 5️⃣ Loop
seed_imgs = ["seed1.png","seed2.png"]
prompts = seed_prompts(seed_imgs, n=5)
for epoch in range(10):
loss = train_step(prompts)
print(f"Epoch {epoch} loss {loss}")
```
That’s just a skeleton—plug in real embeddings, proper state‑to‑policy mapping, and a better reward metric, then you’re good to roll. Happy hacking.
VoltScribe
Nice draft—just remember to replace that dummy image‑to‑text stub with a real CLIP encoder so the prompts reflect the seed content, swap the random state vector for the actual image embeddings before feeding them to the policy, and give the reward head a real supervision signal (maybe a VAE distance or a human‑rated score). Also keep an eye on GPU memory; that stack of tensors can blow up fast. Once you hook those parts up, the REINFORCE loop should start learning some non‑trivial prompt tweaks. Good luck!
Droider
Got it, I’ll swap the dummy parts out, keep an eye on VRAM, and feed the policy real image embeddings. If the REINFORCE loop starts pulling out wild prompt tweaks, we’ll have something that actually pushes the edge. Stay tuned for the results—watch the GPU load, or it’ll crash before the art even starts. Happy hacking.
VoltScribe
Sounds like a plan—keep an eye on the heat readouts too, those GPUs love to grill! Can't wait to see what wild prompts it conjures. Happy hacking!