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README.md

@@ -9,13 +9,17 @@ tags:
 base_model: nvidia/PixelDiT-1300M-1024px
 ---
 
-# PixelDiT 1.3B — Diffusers-Compatible Conversion
+![FourNeuron-PixelDiT Banner](assets/banner.png)
 
-This is an **unofficial** HuggingFace diffusers-compatible conversion of NVIDIA's [PixelDiT-1300M-1024px](https://huggingface.co/nvidia/PixelDiT-1300M-1024px).
+# PixelDiT 1.3B — Diffusers-Compatible Pipeline
 
-All credit goes to the original authors at NVIDIA. This repo only provides a `DiffusionPipeline` wrapper to enable standard diffusers usage, `from_pretrained`, and LoRA fine-tuning via `peft`.
+> **Two RTX 3060s. Infinite Lore. Zero Fear.**
 
-> **I do not own this model.** Original weights, architecture, and training are the work of NVIDIA Research. Please refer to their [original repository](https://huggingface.co/nvidia/PixelDiT-1300M-1024px) for license terms.
+Unofficial HuggingFace diffusers-compatible conversion of NVIDIA's [PixelDiT-1300M-1024px](https://huggingface.co/nvidia/PixelDiT-1300M-1024px) with dual text encoder support (Gemma-2-2B + Qwen3-2B) and ComfyUI integration.
+
+All credit for the model architecture and weights goes to NVIDIA Research. This repo provides the pipeline wrapper, Qwen encoder integration, and tooling.
+
+> **I do not own this model.** Original weights, architecture, and training are the work of NVIDIA Research.
 
 ---
 
@@ -24,7 +28,7 @@ All credit goes to the original authors at NVIDIA. This repo only provides a `Di
 PixelDiT is a 1.3B parameter **pixel-space** diffusion transformer — no VAE, generates images directly in pixel space. Runs on **4GB VRAM**.
 
 - **Architecture**: MMDiT patch blocks + pixel pathway (PiT blocks)
-- **Text encoder**: Gemma-2-2B with chi_prompt instruction prefix
+- **Text encoders**: Gemma-2-2B (photorealistic) or Qwen3-2B (creative/fantasy)
 - **Native resolution**: 1024×1024
 - **Sampler**: Flow matching (FlowMatchEulerDiscreteScheduler, shift=4.0)
 - **Minimum steps**: 45–50 — below 45 produces garbage output
@@ -46,6 +50,8 @@ python scripts/setup_diffusers_pixeldit.py
 
 ## Usage
 
+### Gemma encoder (photorealistic)
+
 ```python
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -67,15 +73,53 @@ pipe = PixelDiTPipeline.from_pretrained(
 pipe.enable_model_cpu_offload()
 
 image = pipe(
-    "a white horse galloping through a meadow at sunset, cinematic lighting",
-    negative_prompt="blurry, flat, low quality, cartoon",
+    "a viking warrior on a cliff overlooking the stormy sea at sunset",
+    negative_prompt="blurry, low quality, deformed, watermark",
     height=1024, width=1024,
-    num_inference_steps=50,   # minimum 45 — below that produces garbage
+    num_inference_steps=50,
     guidance_scale=7.5,
 ).images[0]
 image.save("out.jpg")
 ```
 
+### Qwen encoder (creative / fantasy / absurd realism)
+
+```python
+# pip install -r requirements.txt first
+python generate.py --encoder qwen --proj qwen_proj.pt --prompt "your epic prompt"
+```
+
+Qwen excels at complex world-building prompts. The more detail you give it, the better.
+
+---
+
+## generate.py — Quick Start
+
+```bash
+# Gemma (default, photorealistic)
+python generate.py --prompt "a leopard in the jungle, National Geographic"
+
+# Qwen (creative, fantasy)
+python generate.py --encoder qwen --proj qwen_proj.pt --cfg 7.5 --steps 50 \
+  --prompt "A giant fluffy hamster emperor inside a colossal mechanical battle fortress"
+
+# Batch mode (runs all PROMPTS list)
+python generate.py --encoder qwen --proj qwen_proj.pt
+```
+
+---
+
+## ComfyUI
+
+```bash
+ln -s /path/to/pixeldit-diffusers/comfyui_pixeldit /path/to/ComfyUI/custom_nodes/comfyui_pixeldit
+```
+
+Three nodes under **PixelDiT** category:
+- **PixelDiT Text Encoder** — load Gemma or swap any compatible encoder
+- **PixelDiT Model Loader** — loads transformer from HF
+- **PixelDiT Sampler** — prompt → image, all params exposed
+
 ---
 
 ## LoRA fine-tuning
@@ -92,17 +136,8 @@ model.print_trainable_parameters()
 
 ---
 
-## Sample outputs
-
-| Prompt | Image |
-|--------|-------|
-| *a viking warrior at sunset* | cinematic, photorealistic |
-| *elemental goddess with fire and ice powers* | epic fantasy, 1024px |
-
----
-
 ## Credits
 
 - **Original model & all credit**: [NVIDIA Research](https://huggingface.co/nvidia/PixelDiT-1300M-1024px)
 - **Paper**: *PixelDiT: Pixel-Space Diffusion Transformers for Text-to-Image Generation* — NVIDIA
-- **This repo**: unofficial diffusers conversion only, no claim of authorship
+- **This repo**: unofficial diffusers conversion, Qwen integration, and tooling only