Update README.md

README.md

@@ -94,26 +94,40 @@ def flux_shift(t, s=3.0):
     """Flux-style timestep shifting - biases toward data end."""
     return s * t / (1 + (s - 1) * t)
 
-def generate(model, t5_emb, clip_emb, clip_pooled, num_steps=25, cfg_scale=4.0):
-    """Euler sampling with classifier-free guidance."""
+def generate(model, t5_emb, clip_pooled, num_steps=25, cfg_scale=4.0):
+    """
+    Euler sampling for rectified flow.
+    
+    Flow matching: x_t = (1-t)*noise + t*data
+    - t=0: pure noise
+    - t=1: pure data
+    - v = data - noise
+    
+    Integrate from t=0 → t=1
+    """
     device = next(model.parameters()).device
     dtype = next(model.parameters()).dtype
     
-    # Start from noise
+    # Start from pure noise (t=0)
     x = torch.randn(1, 64*64, 16, device=device, dtype=dtype)
     img_ids = TinyFluxDeep.create_img_ids(1, 64, 64, device)
     
-    # Timesteps with Flux shift
-    timesteps = flux_shift(torch.linspace(1, 0, num_steps + 1, device=device))
+    # Timesteps: 0 → 1 with Flux shift
+    t_linear = torch.linspace(0, 1, num_steps + 1, device=device)
+    timesteps = flux_shift(t_linear, s=3.0)
+    
+    # Null embeddings for CFG
+    t5_null = torch.zeros_like(t5_emb)
+    clip_null = torch.zeros_like(clip_pooled)
     
     for i in range(num_steps):
         t_curr = timesteps[i]
         t_next = timesteps[i + 1]
-        dt = t_next - t_curr
+        dt = t_next - t_curr  # Positive
         
-        t_batch = t_curr.expand(1)
+        t_batch = t_curr.unsqueeze(0)
         
-        # Conditional prediction
+        # Predict velocity
         v_cond = model(
             hidden_states=x,
             encoder_hidden_states=t5_emb,
@@ -122,11 +136,10 @@ def generate(model, t5_emb, clip_emb, clip_pooled, num_steps=25, cfg_scale=4.0):
             img_ids=img_ids,
         )
         
-        # Unconditional prediction (for CFG)
         v_uncond = model(
             hidden_states=x,
-            encoder_hidden_states=torch.zeros_like(t5_emb),
-            pooled_projections=torch.zeros_like(clip_pooled),
+            encoder_hidden_states=t5_null,
+            pooled_projections=clip_null,
             timestep=t_batch,
             img_ids=img_ids,
         )
@@ -134,7 +147,7 @@ def generate(model, t5_emb, clip_emb, clip_pooled, num_steps=25, cfg_scale=4.0):
         # Classifier-free guidance
         v = v_uncond + cfg_scale * (v_cond - v_uncond)
         
-        # Euler step
+        # Euler step: x_{t+dt} = x_t + v * dt
         x = x + v * dt
     
     return x  # [1, 4096, 16] - decode with VAE
@@ -405,9 +418,12 @@ vae = AutoencoderKL.from_pretrained(
 model_py = hf_hub_download("AbstractPhil/tiny-flux-deep", "scripts/model_v4.py")
 exec(open(model_py).read())
 
-config = TinyFluxConfig()
+config = TinyFluxConfig(
+    use_sol_prior=True,  # Disabled until trained
+    use_t5_vec=True,     # Disabled until trained
+)
 model = TinyFluxDeep(config).to("cuda", torch.bfloat16)
-weights = load_file(hf_hub_download("AbstractPhil/tiny-flux-deep", "model.safetensors"))
+weights = load_file(hf_hub_download("AbstractPhil/tiny-flux-deep", "checkpoint_runs/v4_init/lailah_401434_v4_init.safetensors"))
 model.load_state_dict(weights, strict=False)
 model.eval()
 
@@ -428,58 +444,71 @@ def encode_prompt(prompt):
     
     return t5_emb, clip_pooled
 
+
+def flux_shift(t, s=3.0):
+    """Flux-style timestep shift."""
+    return s * t / (1 + (s - 1) * t)
+
+
+@torch.inference_mode()
 def generate_image(prompt, num_steps=25, cfg_scale=4.0, seed=None):
-    """Generate image from text prompt."""
+    """
+    Euler sampling for rectified flow.
+    
+    Flow matching formulation:
+        x_t = (1 - t) * noise + t * data
+        At t=0: pure noise
+        At t=1: pure data  
+        Velocity v = data - noise (constant)
+        
+    Sampling: Integrate from t=0 (noise) → t=1 (data)
+    """
     if seed is not None:
         torch.manual_seed(seed)
-    
-    t5_emb, clip_pooled = encode_prompt(prompt)
-    
-    # Null embeddings for CFG
-    t5_null, clip_null = encode_prompt("")
-    
-    # Start from noise
-    x = torch.randn(1, 64*64, 16, device="cuda", dtype=torch.bfloat16)
-    img_ids = TinyFluxDeep.create_img_ids(1, 64, 64, "cuda")
-    
-    # Flux-shifted timesteps
-    def flux_shift(t, s=3.0):
-        return s * t / (1 + (s - 1) * t)
-    
-    timesteps = flux_shift(torch.linspace(1, 0, num_steps + 1, device="cuda"))
-    
-    with torch.no_grad():
-        for i in range(num_steps):
-            t = timesteps[i].expand(1)
-            dt = timesteps[i + 1] - timesteps[i]
-            
-            # Conditional
-            v_cond = model(x, t5_emb, clip_pooled, t, img_ids)
-            
-            # Unconditional
-            v_uncond = model(x, t5_null, clip_null, t, img_ids)
-            
-            # CFG
-            v = v_uncond + cfg_scale * (v_cond - v_uncond)
-            
-            # Euler step
-            x = x + v * dt
-    
-    # Decode with VAE
-    x = x.reshape(1, 64, 64, 16).permute(0, 3, 1, 2)  # [B, C, H, W]
-    x = x / vae.config.scaling_factor
-    with torch.no_grad():
-        image = vae.decode(x).sample
-    
-    # Convert to PIL
-    image = (image / 2 + 0.5).clamp(0, 1)
-    image = image[0].permute(1, 2, 0).cpu().float().numpy()
-    image = (image * 255).astype("uint8")
-    
-    from PIL import Image
-    return Image.fromarray(image)
-
-# Generate!
+    with torch.autocast("cuda", dtype=torch.bfloat16):
+      t5_emb, clip_pooled = encode_prompt(prompt)
+      t5_null, clip_null = encode_prompt("")
+      
+      # Start from pure noise (t=0)
+      x = torch.randn(1, 64*64, 16, device="cuda", dtype=torch.bfloat16)
+      img_ids = TinyFluxDeep.create_img_ids(1, 64, 64, "cuda")
+      
+      # Timesteps: 0 → 1 with Flux shift
+      t_linear = torch.linspace(0, 1, num_steps + 1, device="cuda", dtype=torch.float32)
+      timesteps = flux_shift(t_linear, s=3.0)
+      
+      for i in range(num_steps):
+          t_curr = timesteps[i]
+          t_next = timesteps[i + 1]
+          dt = t_next - t_curr  # Positive, moving toward data
+          
+          t_batch = t_curr.unsqueeze(0)
+          
+          # Predict velocity
+          v_cond = model(x, t5_emb, clip_pooled, t_batch, img_ids)
+          v_uncond = model(x, t5_null, clip_null, t_batch, img_ids)
+          
+          # Classifier-free guidance
+          v = v_uncond + cfg_scale * (v_cond - v_uncond)
+          
+          # Euler step: x_{t+dt} = x_t + v * dt
+          x = x + v * dt
+      
+      # Decode with VAE
+      x = x.reshape(1, 64, 64, 16).permute(0, 3, 1, 2)  # [B, C, H, W]
+      x = x / vae.config.scaling_factor
+      image = vae.decode(x).sample
+      
+      # Convert to PIL
+      image = (image / 2 + 0.5).clamp(0, 1)
+      image = image[0].permute(1, 2, 0).cpu().float().numpy()
+      image = (image * 255).astype("uint8")
+      
+      from PIL import Image
+      return Image.fromarray(image)
+
+
+# Generate
 image = generate_image("a photograph of a tiger in natural habitat", seed=42)
 image.save("tiger.png")
 ```