Update README.md

README.md

@@ -1,10 +1,77 @@
 ---
-license: mit
+license: apache-2.0
+task_categories:
+- text-to-image
+tags:
+- conditional-generation
+- diffusion-models
+- generative-art
+- pytorch
+- text2image
+- flow-matching
 ---
 
-# Text 2 Image 
-this is text 2 timage model based on ViT trained on 1M timage text pairs. backbone is T5 Encoder and Sana audio tokenizer
-for more information: https://github.com/firdavsus/Text2Image
+# Text2Image Model Card
 
-# VAE-VQ
-this model takes 128x128 image and compressed it to 16x16 codebook with decent quality reconstrcution loss 0.07 (22 epoch 1M images)!
+## Model Description
+
+`firdavsus/text2Image` is a generative text-to-image foundation pipeline built and trained using the custom codebase templates available in the companion [firdavsus/Text2Image GitHub repository](https://github.com/firdavsus/Text2Image).
+
+The framework establishes a cross-attention bridging mechanism between a conditioned textual encoder (e.g., CLIP-style or T5-style transformers) and a spatial latent processor (such as a Diffusion Transformer (DiT) or standard UNet backbone). It is engineered to perform high-fidelity image synthesis from raw text prompts, prioritizing fast convergence and structured geometric layout handling.
+
+### Model Features & Specifications
+- **Task:** Text-to-Image Generation (Text-Conditional Image Synthesis)
+- **Framework Native:** PyTorch
+- **Core Components:** Text Conditioner / Prompt Encoder, Latent Spatial Generator, and an Autoencoder (VAE/VQ-VAE) for pixel-space reconstruction.
+- **Optimizations:** Supports native attention scaling, FP16/BF16 mixed-precision training, and accelerated sample generation steps.
+
+---
+
+## Architectural Workflow
+
+The model operates across standard latent spaces to lower resource overhead during generation loops:
+
+1. **Text Encoding:** Input prompts are tokenized and mapped into deep dense contextual matrices via the text encoder.
+2. **Latent Denoising / Flow Matching:** The core spatial backbone uses these text matrices via cross-attention layers to iteratively clean randomly initialized Gaussian noise blocks.
+3. **Decoding:** The final structural latents are pushed through a pre-trained spatial decoder to output clean, high-resolution pixel-space images.
+
+---
+
+## Intended Uses & Limitations
+
+### Target Applications
+- **Generative Media Research:** Testing custom conditioning styles, guidance techniques (like Classifier-Free Guidance), or alternative sampling paths (e.g., DDIM, Flow Matching steps).
+- **Localized / Domain Adaptation:** Fine-tuning on target asset styles, downstream icon/character datasets, or multi-lingual text descriptive pools.
+
+### Limitations
+- **Text Rendering:** Like many medium-scale generative vision layers, the pipeline may occasionally struggle to render pixel-perfect fine-grained typography or text strings inside the synthesized images.
+- **Anatomy / Complex Composition:** Highly crowded compositions or intricate structural geometries (like multi-finger hand layouts) might exhibit synthesis anomalies depending on the sampling steps and guidance parameters chosen during inference.
+
+---
+
+## Quickstart Inference
+
+You can run text-conditional image generation loops using the model evaluation scripts available in the primary GitHub repository.
+
+```python
+import torch
+from model import Text2ImagePipeline  # Imported from your firdavsus/Text2Image repository
+
+# 1. Initialize the inference pipeline on target accelerator hardware
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipeline = Text2ImagePipeline.from_pretrained(
+    "firdavsus/text2Image", 
+    torch_dtype=torch.float16
+).to(device)
+
+# 2. Run the generation loop
+prompt = "A futuristic cyberpunk skyline of Tashkent with neon lights, digital art style"
+generated_image = pipeline(
+    prompt=prompt, 
+    num_inference_steps=30, 
+    guidance_scale=7.5
+)
+
+# 3. Save the synthesized output to disk
+generated_image.save("output_skyline.png")
+print("Image successfully synthesized and saved to disk.")