Add guidance scale for prompt param and flux sampling

library/flux_train_utils.py

@@ -154,6 +154,7 @@ def sample_image_inference(
     sample_steps = prompt_dict.get("sample_steps", 20)
     width = prompt_dict.get("width", 512)
     height = prompt_dict.get("height", 512)
+    guidance_scale = prompt_dict.get("guidance_scale", args.guidance_scale)
     scale = prompt_dict.get("scale", 1.0)  # 1.0 means no guidance
     seed = prompt_dict.get("seed")
     controlnet_image = prompt_dict.get("controlnet_image")
@@ -180,9 +181,12 @@ def sample_image_inference(
     logger.info(f"prompt: {prompt}")
     if scale != 1.0:
         logger.info(f"negative_prompt: {negative_prompt}")
+    elif negative_prompt != "":
+        logger.info(f"negative prompt is ignored because scale is 1.0")
     logger.info(f"height: {height}")
     logger.info(f"width: {width}")
     logger.info(f"sample_steps: {sample_steps}")
+    logger.info(f"guidance_scale: {guidance_scale}")
     if scale != 1.0:
         logger.info(f"scale: {scale}")
     # logger.info(f"sample_sampler: {sampler_name}")
@@ -256,7 +260,7 @@ def sample_image_inference(
             txt_ids,
             l_pooled,
             timesteps=timesteps,
-            guidance=scale,
+            guidance=guidance_scale,
             t5_attn_mask=t5_attn_mask,
             controlnet=controlnet,
             controlnet_img=controlnet_image,
@@ -489,7 +493,7 @@ def get_noisy_model_input_and_timesteps(
         sigmas = torch.randn(bsz, device=device)
         sigmas = sigmas * args.sigmoid_scale  # larger scale for more uniform sampling
         sigmas = sigmas.sigmoid()
-        mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2)) # we are pre-packed so must adjust for packed size 
+        mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2))  # we are pre-packed so must adjust for packed size
         sigmas = time_shift(mu, 1.0, sigmas)
         timesteps = sigmas * num_timesteps
     else:
@@ -514,7 +518,7 @@ def get_noisy_model_input_and_timesteps(
     if args.ip_noise_gamma:
         xi = torch.randn_like(latents, device=latents.device, dtype=dtype)
         if args.ip_noise_gamma_random_strength:
-            ip_noise_gamma = (torch.rand(1, device=latents.device, dtype=dtype) * args.ip_noise_gamma)
+            ip_noise_gamma = torch.rand(1, device=latents.device, dtype=dtype) * args.ip_noise_gamma
         else:
             ip_noise_gamma = args.ip_noise_gamma
         noisy_model_input = (1.0 - sigmas) * latents + sigmas * (noise + ip_noise_gamma * xi)

library/train_util.py

@@ -6178,6 +6178,11 @@ def line_to_prompt_dict(line: str) -> dict:
                 prompt_dict["scale"] = float(m.group(1))
                 continue
 
+            m = re.match(r"g ([\d\.]+)", parg, re.IGNORECASE)
+            if m:  # guidance scale
+                prompt_dict["guidance_scale"] = float(m.group(1))
+                continue
+
             m = re.match(r"n (.+)", parg, re.IGNORECASE)
             if m:  # negative prompt
                 prompt_dict["negative_prompt"] = m.group(1)