update flux pipeline

diffsynth/models/flux_dit.py

@@ -364,6 +364,7 @@ class FluxDiT(torch.nn.Module):
         
         conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
         if self.guidance_embedder is not None:
+            guidance = guidance * 1000
             conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype)
         prompt_emb = self.context_embedder(prompt_emb)
         image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))

diffsynth/pipelines/base.py

@@ -65,9 +65,11 @@ class BasePipeline(torch.nn.Module):
         mask_scales += [100.0] * len(extended_prompt_dict.get("masks", []))
         return prompt, local_prompts, masks, mask_scales
     
+    
     def enable_cpu_offload(self):
         self.cpu_offload = True
 
+
     def load_models_to_device(self, loadmodel_names=[]):
         # only load models to device if cpu_offload is enabled
         if not self.cpu_offload:
@@ -85,3 +87,9 @@ class BasePipeline(torch.nn.Module):
                 model.to(self.device)
         # fresh the cuda cache
         torch.cuda.empty_cache()
+
+    
+    def generate_noise(self, shape, seed=None, device="cpu", dtype=torch.float16):
+        generator = None if seed is None else torch.Generator(device).manual_seed(seed)
+        noise = torch.randn(shape, generator=generator, device=device, dtype=dtype)
+        return noise

diffsynth/pipelines/flux_image.py

@@ -58,7 +58,7 @@ class FluxImagePipeline(BasePipeline):
         return image
     
 
-    def encode_prompt(self, prompt, positive=True, t5_sequence_length=256):
+    def encode_prompt(self, prompt, positive=True, t5_sequence_length=512):
         prompt_emb, pooled_prompt_emb, text_ids = self.prompter.encode_prompt(
             prompt, device=self.device, positive=positive, t5_sequence_length=t5_sequence_length
         )
@@ -80,7 +80,7 @@ class FluxImagePipeline(BasePipeline):
         mask_scales= None,
         negative_prompt="",
         cfg_scale=1.0,
-        embedded_guidance=1.0,
+        embedded_guidance=3.5,
         input_image=None,
         denoising_strength=1.0,
         height=1024,
@@ -90,6 +90,7 @@ class FluxImagePipeline(BasePipeline):
         tiled=False,
         tile_size=128,
         tile_stride=64,
+        seed=None,
         progress_bar_cmd=tqdm,
         progress_bar_st=None,
     ):
@@ -104,10 +105,10 @@ class FluxImagePipeline(BasePipeline):
             self.load_models_to_device(['vae_encoder'])
             image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
             latents = self.encode_image(image, **tiler_kwargs)
-            noise = torch.randn((1, 16, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+            noise = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
             latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
         else:
-            latents = torch.randn((1, 16, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+            latents = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
 
         # Extend prompt
         self.load_models_to_device(['text_encoder_1', 'text_encoder_2'])

diffsynth/prompters/flux_prompter.py

@@ -57,7 +57,7 @@ class FluxPrompter(BasePrompter):
         prompt,
         positive=True,
         device="cuda",
-        t5_sequence_length=256,
+        t5_sequence_length=512,
     ):
         prompt = self.process_prompt(prompt, positive=positive)