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)
         

examples/image_synthesis/flux_text_to_image.py

@@ -12,30 +12,30 @@ model_manager.load_models([
 ])
 pipe = FluxImagePipeline.from_model_manager(model_manager)
 
-prompt = "CG. Full body. A captivating fantasy magic woman portrait in the deep sea. The woman, with blue spaghetti strap silk dress, swims in the sea. Her flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her. Smooth, delicate and fair skin."
+prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her."
-negative_prompt = "dark, worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, dim, fuzzy, depth of Field, nsfw,"
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
 
 # Disable classifier-free guidance (consistent with the original implementation of FLUX.1)
-torch.manual_seed(6)
+torch.manual_seed(9)
 image = pipe(
     prompt=prompt,
-    num_inference_steps=30, embedded_guidance=3.5
+    num_inference_steps=50, embedded_guidance=3.5
 )
 image.save("image_1024.jpg")
 
 # Enable classifier-free guidance
-torch.manual_seed(6)
+torch.manual_seed(9)
 image = pipe(
     prompt=prompt, negative_prompt=negative_prompt,
-    num_inference_steps=30, cfg_scale=2.0, embedded_guidance=3.5
+    num_inference_steps=50, cfg_scale=2.0, embedded_guidance=3.5
 )
 image.save("image_1024_cfg.jpg")
 
 # Highres-fix
-torch.manual_seed(7)
+torch.manual_seed(10)
 image = pipe(
     prompt=prompt,
-    num_inference_steps=30, embedded_guidance=3.5,
+    num_inference_steps=50, embedded_guidance=3.5,
     input_image=image.resize((2048, 2048)), height=2048, width=2048, denoising_strength=0.6, tiled=True
 )
 image.save("image_2048_highres.jpg")

examples/image_synthesis/flux_text_to_image_low_vram.py

@@ -22,30 +22,30 @@ pipe = FluxImagePipeline.from_model_manager(model_manager, device="cuda")
 pipe.enable_cpu_offload()
 pipe.dit.quantize()
 
-prompt = "CG. Full body. A captivating fantasy magic woman portrait in the deep sea. The woman, with blue spaghetti strap silk dress, swims in the sea. Her flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her. Smooth, delicate and fair skin."
+prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her."
-negative_prompt = "dark, worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, dim, fuzzy, depth of Field, nsfw,"
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
 
 # Disable classifier-free guidance (consistent with the original implementation of FLUX.1)
-torch.manual_seed(6)
+torch.manual_seed(9)
 image = pipe(
     prompt=prompt,
-    num_inference_steps=30, embedded_guidance=3.5
+    num_inference_steps=50, embedded_guidance=3.5
 )
 image.save("image_1024.jpg")
 
 # Enable classifier-free guidance
-torch.manual_seed(6)
+torch.manual_seed(9)
 image = pipe(
     prompt=prompt, negative_prompt=negative_prompt,
-    num_inference_steps=30, cfg_scale=2.0, embedded_guidance=3.5
+    num_inference_steps=50, cfg_scale=2.0, embedded_guidance=3.5
 )
 image.save("image_1024_cfg.jpg")
 
 # Highres-fix
-torch.manual_seed(7)
+torch.manual_seed(10)
 image = pipe(
     prompt=prompt,
-    num_inference_steps=30, embedded_guidance=3.5,
+    num_inference_steps=50, embedded_guidance=3.5,
     input_image=image.resize((2048, 2048)), height=2048, width=2048, denoising_strength=0.6, tiled=True
 )
-image.save("image_2048_highres.jpg")
+image.save("image_2048_highres.jpg")