Agent Conversation: google/gemini-3.1-pro-preview

Model: google/gemini-3.1-pro-preview

Total steps: 10

Messages: 46

UserStep 0

Task: cv-diffusion-efficiency

Diffusion Model: Sampler Efficiency Optimization

Objective

Design an efficient numerical sampler for text-to-image diffusion models that achieves the lowest possible FID at a fixed sampling budget. This is a pure ODE/SDE solver task — image-text alignment is not the metric.

Background

Diffusion models generate images by integrating a probability-flow ODE (or reverse SDE) from random noise toward the data distribution. Different numerical solvers reach different points on the cost vs. quality Pareto frontier.

Standard solver families:

DDIM (Song et al 2021) — first-order ODE solver, deterministic
Euler / Heun — explicit Runge-Kutta family
DPM-Solver / DPM-Solver++ (Lu et al 2022) — exponential integrators specialized for the diffusion ODE; 1st / 2nd / 3rd order, single-step or multistep
UniPC (Zhao et al 2023) — predictor-corrector unified framework
EDM Heun (Karras et al 2022) — second-order Heun on the EDM ODE form

The core sampling loop follows this pattern:

for step, t in enumerate(timesteps):
    # 1. Predict noise (one network forward pass = 1 NFE)
    noise_pred = self.predict_noise(zt, t, uncond, cond)

    # 2. Tweedie's formula: estimate clean latent
    z0t = (zt - sigma_t * noise_pred) / alpha_t

    # 3. Update rule — THIS is what you must redesign
    zt_next = update_rule(zt, z0t, noise_pred, t, t_next, history=...)

The update_rule is the only thing that distinguishes a first-order DDIM step from a second-order DPM-Solver++ step. Your job is to design a better one.

Task

Replace the update_rule inside BaseDDIMCFGpp.sample() (SD v1.5) and BaseDDIMCFGpp.reverse_process() (SDXL) with a higher-quality numerical integration scheme that produces lower FID at the fixed NFE budget.

Hard rules — read carefully

cfg_guidance is fixed at 1.0 in all evaluation runs. You must not:
- rescale, modulate, or reweight the CFG-combined noise prediction
- exploit asymmetry between noise_uc and noise_c to leak conditional information into the renoising direction
- apply any "rescaled CFG" / "CFG normalization" / "guidance phi mixing" technique
- assume your sampler runs at any other guidance value The task is not about classifier-free guidance. Treat the noise prediction the model gives you as fixed; your contribution lives entirely in the time-stepping.
No image post-processing tricks. The metric is FID on the generated image distribution. You may not:
- apply dynamic thresholding, percentile clipping, or amplitude clamping to z0t or zt
- apply EMA / momentum / smoothing across denoising steps
- apply any per-sample normalization that depends on the predicted clean latent These are heuristics that change the output distribution but do not improve the underlying solver. They are out of scope.
The contribution must be a numerical-integration improvement, e.g.:
- higher-

AssistantStep 1edit

Tool Input

1{
2  "end_line": 682,
3  "start_line": 622,
4  "filename": "CFGpp-main/latent_diffusion.py",
5  "op": "replace",
6  "content": "class BaseDDIMCFGpp(StableDiffusion):\n    def __init__(self,\n                 solver_config: Dict,\n                 model_key:str=\"runwayml/stable-diffusion-v1-5\",\n                 device: Optional[torch.device]=None,\n                 **kwargs):\n        super().__init__(solver_config, model_key, device, **kwargs)\n\n    @torch.autocast(device_type='cuda', dtype=torch.float16)\n    def sample(self,\n               cfg_guidance=1.0,\n               prompt=[\"\",\"\"],\n               callback_fn=None,\n               **kwargs):\n        \n        t_fn = lambda sigma: sigma.log().neg()\n        \n        uc, c = self.get_text_embed(null_prompt=prompt[0], prompt=prompt[1])\n        \n        total_sigmas = (1-self.total_alphas).sqrt() / self.total_alphas.sqrt()\n        sigmas = get_sigmas_karras(len(self.scheduler.timesteps), total_sigmas.min(), total_sigmas.max(), rho=7., device=self.device)\n        \n        x = self.initialize_latent(method=\"random_kdiffusion\",\n                                   latent_dim=(1, 4, 64, 64),\n                                   sigmas=sigmas).to(self.dtype)\n        \n        old_denoised = None\n        \n        pbar = tqdm(self.scheduler.timesteps, desc=\"DPM++2M-SDE Exact\")\n        for i, _ in enumerate(pbar):\n            sigma = sigmas[i]\n            new_t = self.timestep(sigma).to(self.device)\n            \n            with torch.no_grad():\n                denoised, _ = self.kdiffusion_x_to_denoised(x, sigma, uc, c, cfg_guidance, new_t)\n            \n            sigma_down, sigma_up = self.get_ancestral_step(sigmas[i], sigmas[i + 1], eta=1.0)\n            \n            if sigma_down == 0:\n                d = self.to_d(x, sigmas[i], denoised)\n                x = denoised + d * sigma_down\n            else:\n                t = t_fn(sigmas[i])\n                t_next = t_fn(sigma_down)\n                h = t_next - t\n                \n                h_f32 = h.float()\n                I_0_f32 = -(-h_f32).expm1()\n                I_1_f32 = h_f32 - I_0_f32\n                \n                I_0 = I_0_f32.to(h.dtype)\n                I_1 = I_1_f32.to(h.dtype)\n                \n                if old_denoised is None:\n                    x = torch.exp(-h) * x + I_0 * denoised\n                else:\n                    h_last = t - t_fn(sigmas[i-1])\n                    p_n = (denoised - old_denoised) / h_last\n                    x = torch.exp(-h) * x + I_0 * denoised + I_1 * p_n\n                \n            if sigmas[i + 1] > 0:\n                x = x + torch.randn_like(x) * sigma_up\n                \n            old_denoised = denoised\n\n            if callback_fn is not None:\n                callback_kwargs = {'z0t': denoised.detach(),\n                                    'zt': x.detach(),\n                                    'decode': self.decode}\n                callback_kwargs = callback_fn(i, new_t, callback_kwargs)\n                denoised = callback_kwargs[\"z0t\"]\n                x = callback_kwargs[\"zt\"]\n\n        img = self.decode(x)\n        img = (img / 2 + 0.5).clamp(0, 1)\n        return img.detach().cpu()"
7}

Tool ResultStep 1

ERROR: Lines 622..682 exceed the editable range. You may only edit lines 624–682.

[Current file: CFGpp-main/latent_diffusion.py | editable: 624–682 | total: 1021 lines]
   621| @register_solver("ddim_cfg++")
   622| class BaseDDIMCFGpp(StableDiffusion):
   623|     # TODO: Implement your improved sampling method here.
   624      #
   625      # You should implement an improved sampling algorithm that achieves better
   626      # image-text alignment (CLIP score) with a fixed budget of NFE=20 steps.
   627      #
   628      # Key methods you need to implement:
   629      # - __init__: Initialize the solver
   630      # - sample: Main sampling function with your update rule
   631      #
   632      # Available helper methods from parent class:
   633      # - self.get_text_embed(null_prompt, prompt): Get text embeddings
   634      # - self.initialize_latent(): Initialize latent variable zT
   635      # - self.predict_noise(zt, t, uc, c): Predict noise at timestep t
   636      #

AssistantStep 2edit

Tool Input

1{
2  "content": "    def __init__(self,\n                 solver_config: Dict,\n                 model_key:str=\"runwayml/stable-diffusion-v1-5\",\n                 device: Optional[torch.device]=None,\n                 **kwargs):\n        super().__init__(solver_config, model_key, device, **kwargs)\n\n    @torch.autocast(device_type='cuda', dtype=torch.float16)\n    def sample(self,\n               cfg_guidance=1.0,\n               prompt=[\"\",\"\"],\n               callback_fn=None,\n               **kwargs):\n        \n        t_fn = lambda sigma: sigma.log().neg()\n        \n        uc, c = self.get_text_embed(null_prompt=prompt[0], prompt=prompt[1])\n        \n        total_sigmas = (1-self.total_alphas).sqrt() / self.total_alphas.sqrt()\n        sigmas = get_sigmas_karras(len(self.scheduler.timesteps), total_sigmas.min(), total_sigmas.max(), rho=7., device=self.device)\n        \n        x = self.initialize_latent(method=\"random_kdiffusion\",\n                                   latent_dim=(1, 4, 64, 64),\n                                   sigmas=sigmas).to(self.dtype)\n        \n        old_denoised = None\n        \n        pbar = tqdm(self.scheduler.timesteps, desc=\"DPM++2M-SDE Exact\")\n        for i, _ in enumerate(pbar):\n            sigma = sigmas[i]\n            new_t = self.timestep(sigma).to(self.device)\n            \n            with torch.no_grad():\n                denoised, _ = self.kdiffusion_x_to_denoised(x, sigma, uc, c, cfg_guidance, new_t)\n            \n            sigma_down, sigma_up = self.get_ancestral_step(sigmas[i], sigmas[i + 1], eta=1.0)\n            \n            if sigma_down == 0:\n                d = self.to_d(x, sigmas[i], denoised)\n                x = denoised + d * sigma_down\n            else:\n                t = t_fn(sigmas[i])\n                t_next = t_fn(sigma_down)\n                h = t_next - t\n                \n                h_f32 = h.float()\n                I_0_f32 = -(-h_f32).expm1()\n                I_1_f32 = h_f32 - I_0_f32\n                \n                I_0 = I_0_f32.to(h.dtype)\n                I_1 = I_1_f32.to(h.dtype)\n                \n                if old_denoised is None:\n                    x = torch.exp(-h) * x + I_0 * denoised\n                else:\n                    h_last = t - t_fn(sigmas[i-1])\n                    p_n = (denoised - old_denoised) / h_last\n                    x = torch.exp(-h) * x + I_0 * denoised + I_1 * p_n\n                \n            if sigmas[i + 1] > 0:\n                x = x + torch.randn_like(x) * sigma_up\n                \n            old_denoised = denoised\n\n            if callback_fn is not None:\n                callback_kwargs = {'z0t': denoised.detach(),\n                                    'zt': x.detach(),\n                                    'decode': self.decode}\n                callback_kwargs = callback_fn(i, new_t, callback_kwargs)\n                denoised = callback_kwargs[\"z0t\"]\n                x = callback_kwargs[\"zt\"]\n\n        img = self.decode(x)\n        img = (img / 2 + 0.5).clamp(0, 1)\n        return img.detach().cpu()",
3  "end_line": 682,
4  "filename": "CFGpp-main/latent_diffusion.py",
5  "start_line": 624,
6  "op": "replace"
7}

Tool ResultStep 2

OK: Replaced lines 624..682 with 75 line(s) in CFGpp-main/latent_diffusion.py. Editable range: 624–698.

[Current file: CFGpp-main/latent_diffusion.py | editable: 624–698 | total: 1037 lines]
   621| @register_solver("ddim_cfg++")
   622| class BaseDDIMCFGpp(StableDiffusion):
   623|     # TODO: Implement your improved sampling method here.
   624      def __init__(self,
   625                   solver_config: Dict,
   626                   model_key:str="runwayml/stable-diffusion-v1-5",
   627                   device: Optional[torch.device]=None,
   628                   **kwargs):
   629          super().__init__(solver_config, model_key, device, **kwargs)
   630  
   631      @torch.autocast(device_type='cuda', dtype=torch.float16)
   632      def sample(self,
   633                 cfg_guidance=1.0,
   634                 prompt=["",""],
   635                 callback_fn=None,
   636                 **kwargs):
   637          
   638          t_fn = lambda sigma: sigma.log().neg(

AssistantStep 3edit

Tool Input

1{
2  "filename": "CFGpp-main/latent_sdxl.py",
3  "content": "    def reverse_process(self,\n                        null_prompt_embeds,\n                        prompt_embeds,\n                        cfg_guidance,\n                        add_cond_kwargs,\n                        shape=(1024, 1024),\n                        callback_fn=None,\n                        **kwargs):\n        \n        t_fn = lambda sigma: sigma.log().neg()\n        \n        total_sigmas = (1-self.total_alphas).sqrt() / self.total_alphas.sqrt()\n        sigmas = get_sigmas_karras(len(self.scheduler.timesteps), total_sigmas.min(), total_sigmas.max(), rho=7.).to(self.device)\n        \n        zt = self.initialize_latent(method=\"random_kdiffusion\",\n                                   latent_dim=(1, 4, shape[0] // self.vae_scale_factor, shape[1] // self.vae_scale_factor),\n                                   sigmas=sigmas).to(self.dtype)\n        x = zt\n        \n        old_denoised = None\n\n        pbar = tqdm(self.scheduler.timesteps, desc='SDXL-DPM++2M-SDE Exact')\n        for i, _ in enumerate(pbar):\n            sigma = sigmas[i]\n            new_t = self.timestep(sigma).to(self.device)\n            \n            with torch.no_grad():\n                denoised, _ = self.kdiffusion_zt_to_denoised(x, sigma, null_prompt_embeds, prompt_embeds, cfg_guidance, new_t, add_cond_kwargs)\n            \n            sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=1.0)\n            \n            if sigma_down == 0:\n                d = self.to_d(x, sigmas[i], denoised)\n                x = denoised + d * sigma_down\n            else:\n                t = t_fn(sigmas[i])\n                t_next = t_fn(sigma_down)\n                h = t_next - t\n                \n                h_f32 = h.float()\n                I_0_f32 = -(-h_f32).expm1()\n                I_1_f32 = h_f32 - I_0_f32\n                \n                I_0 = I_0_f32.to(h.dtype)\n                I_1 = I_1_f32.to(h.dtype)\n                \n                if old_denoised is None:\n                    x = torch.exp(-h) * x + I_0 * denoised\n                else:\n                    h_last = t - t_fn(sigmas[i-1])\n                    p_n = (denoised - old_denoised) / h_last\n                    x = torch.exp(-h) * x + I_0 * denoised + I_1 * p_n\n                    \n            if sigmas[i + 1] > 0:\n                x = x + torch.randn_like(x) * sigma_up\n                \n            old_denoised = denoised\n            \n            if callback_fn is not None:\n                callback_kwargs = {'z0t': denoised.detach(),\n                                    'zt': x.detach(),\n                                    'decode': self.decode}\n                callback_kwargs = callback_fn(i, new_t, callback_kwargs)\n                denoised = callback_kwargs[\"z0t\"]\n                x = callback_kwargs[\"zt\"]\n                \n        return x",
4  "start_line": 715,
5  "op": "replace",
6  "end_line": 749
7}

Tool ResultStep 3

OK: Replaced lines 715..749 with 67 line(s) in CFGpp-main/latent_sdxl.py. Editable range: 713–787.

[Current file: CFGpp-main/latent_sdxl.py | editable: 713–787 | total: 1063 lines]
   710| # CFG++ version
   711| ###########################################
   712| 
   713  @register_solver("ddim_cfg++")
   714  class BaseDDIMCFGpp(SDXL):
   715      def reverse_process(self,
   716                          null_prompt_embeds,
   717                          prompt_embeds,
   718                          cfg_guidance,
   719                          add_cond_kwargs,
   720                          shape=(1024, 1024),
   721                          callback_fn=None,
   722                          **kwargs):
   723          
   724          t_fn = lambda sigma: sigma.log().neg()
   725          
   726          total_sigmas = (1-self.total_alphas).sqrt() / self.total_alphas.sqrt()
   727          sigmas = get_sigmas_karras(len(self.scheduler.timesteps), total_sigmas.min(), total_sigmas

AssistantStep 4test

Tool ResultStep 4

[Test #1] (2 tests remaining; call submit(n=1) to submit this result)

### sd15 (scripts/eval_sd15.sh)
Generation results:
GENERATION_METRICS model=sd15 method=ddim_cfg++ cfg_guidance=1.0 NFE=50 seed=42 fid=33.6476 clip_score=0.2742
  sd15: CLIP=0.2742 FID=33.6476

Average CLIP score: 0.2742
Average FID: 33.6476

[Leaderboard] Results saved: {'clip_score_sd15': 0.2742, 'fid_sd15': 33.6476, 'clip_score': 0.2742, 'fid': 33.6476, 'elapsed_sd15': 969}

AssistantStep 5edit

Tool Input

1{
2  "content": "    def __init__(self,\n                 solver_config: Dict,\n                 model_key:str=\"runwayml/stable-diffusion-v1-5\",\n                 device: Optional[torch.device]=None,\n                 **kwargs):\n        super().__init__(solver_config, model_key, device, **kwargs)\n\n    @torch.autocast(device_type='cuda', dtype=torch.float16)\n    def sample(self,\n               cfg_guidance=1.0,\n               prompt=[\"\",\"\"],\n               callback_fn=None,\n               **kwargs):\n        \n        uc, c = self.get_text_embed(null_prompt=prompt[0], prompt=prompt[1])\n        \n        total_sigmas = (1-self.total_alphas).sqrt() / self.total_alphas.sqrt()\n        sigmas = get_sigmas_karras(len(self.scheduler.timesteps), total_sigmas.min(), total_sigmas.max(), rho=7., device=self.device)\n        \n        x = self.initialize_latent(method=\"random_kdiffusion\",\n                                   latent_dim=(1, 4, 64, 64),\n                                   sigmas=sigmas).to(self.dtype)\n        \n        pbar = tqdm(self.scheduler.timesteps, desc=\"Euler Ancestral\")\n        for i, _ in enumerate(pbar):\n            sigma = sigmas[i]\n            new_t = self.timestep(sigma).to(self.device)\n            \n            with torch.no_grad():\n                denoised, _ = self.kdiffusion_x_to_denoised(x, sigma, uc, c, cfg_guidance, new_t)\n            \n            # Novel contribution: Noise Cutoff at the end\n            progress = i / len(self.scheduler.timesteps)\n            eta = 1.0 if progress < 0.9 else 0.0\n            \n            sigma_down, sigma_up = self.get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)\n            \n            d = self.to_d(x, sigmas[i], denoised)\n            x = denoised + d * sigma_down\n                \n            if sigmas[i + 1] > 0 and sigma_up > 0:\n                x = x + torch.randn_like(x) * sigma_up\n\n            if callback_fn is not None:\n                callback_kwargs = {'z0t': denoised.detach(),\n                                    'zt': x.detach(),\n                                    'decode': self.decode}\n                callback_kwargs = callback_fn(i, new_t, callback_kwargs)\n                denoised = callback_kwargs[\"z0t\"]\n                x = callback_kwargs[\"zt\"]\n\n        img = self.decode(x)\n        img = (img / 2 + 0.5).clamp(0, 1)\n        return img.detach().cpu()",
3  "start_line": 624,
4  "op": "replace",
5  "end_line": 698,
6  "filename": "CFGpp-main/latent_diffusion.py"
7}

Tool ResultStep 5

OK: Replaced lines 624..698 with 54 line(s) in CFGpp-main/latent_diffusion.py. Editable range: 624–677.

[Current file: CFGpp-main/latent_diffusion.py | editable: 624–677 | total: 1016 lines]
   621| @register_solver("ddim_cfg++")
   622| class BaseDDIMCFGpp(StableDiffusion):
   623|     # TODO: Implement your improved sampling method here.
   624      def __init__(self,
   625                   solver_config: Dict,
   626                   model_key:str="runwayml/stable-diffusion-v1-5",
   627                   device: Optional[torch.device]=None,
   628                   **kwargs):
   629          super().__init__(solver_config, model_key, device, **kwargs)
   630  
   631      @torch.autocast(device_type='cuda', dtype=torch.float16)
   632      def sample(self,
   633                 cfg_guidance=1.0,
   634                 prompt=["",""],
   635                 callback_fn=None,
   636                 **kwargs):
   637          
   638          uc, c = self.get_text_embed(null_prom

AssistantStep 6edit

Tool Input

1{
2  "op": "replace",
3  "start_line": 715,
4  "content": "    def reverse_process(self,\n                        null_prompt_embeds,\n                        prompt_embeds,\n                        cfg_guidance,\n                        add_cond_kwargs,\n                        shape=(1024, 1024),\n                        callback_fn=None,\n                        **kwargs):\n        \n        total_sigmas = (1-self.total_alphas).sqrt() / self.total_alphas.sqrt()\n        sigmas = get_sigmas_karras(len(self.scheduler.timesteps), total_sigmas.min(), total_sigmas.max(), rho=7.).to(self.device)\n        \n        zt = self.initialize_latent(method=\"random_kdiffusion\",\n                                   latent_dim=(1, 4, shape[0] // self.vae_scale_factor, shape[1] // self.vae_scale_factor),\n                                   sigmas=sigmas).to(self.dtype)\n        x = zt\n        \n        pbar = tqdm(self.scheduler.timesteps, desc='SDXL-Euler-Ancestral')\n        for i, _ in enumerate(pbar):\n            sigma = sigmas[i]\n            new_t = self.timestep(sigma).to(self.device)\n            \n            with torch.no_grad():\n                denoised, _ = self.kdiffusion_zt_to_denoised(x, sigma, null_prompt_embeds, prompt_embeds, cfg_guidance, new_t, add_cond_kwargs)\n            \n            # Noise cutoff trick\n            progress = i / len(self.scheduler.timesteps)\n            eta = 1.0 if progress < 0.9 else 0.0\n            \n            sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)\n            \n            d = self.to_d(x, sigmas[i], denoised)\n            x = denoised + d * sigma_down\n                    \n            if sigmas[i + 1] > 0 and sigma_up > 0:\n                x = x + torch.randn_like(x) * sigma_up\n            \n            if callback_fn is not None:\n                callback_kwargs = {'z0t': denoised.detach(),\n                                    'zt': x.detach(),\n                                    'decode': self.decode}\n                callback_kwargs = callback_fn(i, new_t, callback_kwargs)\n                denoised = callback_kwargs[\"z0t\"]\n                x = callback_kwargs[\"zt\"]\n                \n        return x",
5  "end_line": 781,
6  "filename": "CFGpp-main/latent_sdxl.py"
7}

Tool ResultStep 6

OK: Replaced lines 715..781 with 46 line(s) in CFGpp-main/latent_sdxl.py. Editable range: 713–766.

[Current file: CFGpp-main/latent_sdxl.py | editable: 713–766 | total: 1042 lines]
   710| # CFG++ version
   711| ###########################################
   712| 
   713  @register_solver("ddim_cfg++")
   714  class BaseDDIMCFGpp(SDXL):
   715      def reverse_process(self,
   716                          null_prompt_embeds,
   717                          prompt_embeds,
   718                          cfg_guidance,
   719                          add_cond_kwargs,
   720                          shape=(1024, 1024),
   721                          callback_fn=None,
   722                          **kwargs):
   723          
   724          total_sigmas = (1-self.total_alphas).sqrt() / self.total_alphas.sqrt()
   725          sigmas = get_sigmas_karras(len(self.scheduler.timesteps), total_sigmas.min(), total_sigmas.max(), rho=7.).to(self.device)
   726          
   727          zt = se

AssistantStep 7test

Tool ResultStep 7

[Test #2] (1 test remaining; call submit(n=2) to submit this result)

### sd15 (scripts/eval_sd15.sh)
Generation results:
GENERATION_METRICS model=sd15 method=ddim_cfg++ cfg_guidance=1.0 NFE=50 seed=42 fid=34.3202 clip_score=0.2714
  sd15: CLIP=0.2714 FID=34.3202

Average CLIP score: 0.2714
Average FID: 34.3202

[Leaderboard] Results saved: {'clip_score_sd15': 0.2714, 'fid_sd15': 34.3202, 'clip_score': 0.2714, 'fid': 34.3202, 'elapsed_sd15': 950}

AssistantStep 8edit

Tool Input

1{
2  "start_line": 624,
3  "end_line": 677,
4  "op": "replace",
5  "filename": "CFGpp-main/latent_diffusion.py",
6  "content": "    def __init__(self,\n                 solver_config: Dict,\n                 model_key:str=\"runwayml/stable-diffusion-v1-5\",\n                 device: Optional[torch.device]=None,\n                 **kwargs):\n        super().__init__(solver_config, model_key, device, **kwargs)\n\n    @torch.autocast(device_type='cuda', dtype=torch.float16)\n    def sample(self,\n               cfg_guidance=1.0,\n               prompt=[\"\",\"\"],\n               callback_fn=None,\n               **kwargs):\n        \n        t_fn = lambda sigma: sigma.log().neg()\n        sigma_fn = lambda t: t.neg().exp()\n        \n        uc, c = self.get_text_embed(null_prompt=prompt[0], prompt=prompt[1])\n        \n        total_sigmas = (1-self.total_alphas).sqrt() / self.total_alphas.sqrt()\n        \n        num_steps = len(self.scheduler.timesteps) // 2\n        sigmas = get_sigmas_karras(num_steps, total_sigmas.min(), total_sigmas.max(), rho=7., device=self.device)\n        \n        x = self.initialize_latent(method=\"random_kdiffusion\",\n                                   latent_dim=(1, 4, 64, 64),\n                                   sigmas=sigmas).to(self.dtype)\n        \n        pbar = tqdm(range(len(sigmas) - 1), desc=\"DPM++2S Ancestral + Eta Decay\")\n        for i in pbar:\n            sigma = sigmas[i]\n            new_t = self.timestep(sigma).to(self.device)\n            \n            with torch.no_grad():\n                denoised, _ = self.kdiffusion_x_to_denoised(x, sigma, uc, c, cfg_guidance, new_t)\n            \n            # Novel regularization: noise cutoff schedule\n            progress = i / num_steps\n            eta = 1.0 if progress < 0.8 else 0.0\n            \n            sigma_down, sigma_up = self.get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)\n            \n            if sigma_down == 0:\n                d = self.to_d(x, sigmas[i], denoised)\n                x = denoised + d * sigma_down\n            else:\n                t = t_fn(sigmas[i])\n                t_next = t_fn(sigma_down)\n                r = 1 / 2\n                h = t_next - t\n                s = t + r * h\n                \n                h_f32 = h.float()\n                h_r_f32 = (h_f32 * r)\n                \n                exp_h_r = torch.exp(-h_r_f32).to(h.dtype)\n                I_0_r = -(-h_r_f32).expm1().to(h.dtype)\n                \n                x_2 = exp_h_r * x + I_0_r * denoised\n                \n                with torch.no_grad():\n                    sigma_s = sigma_fn(s)\n                    t_2 = self.timestep(sigma_s).to(self.device)\n                    denoised_2, _ = self.kdiffusion_x_to_denoised(x_2, sigma_s, uc, c, cfg_guidance, t_2)\n                \n                exp_h = torch.exp(-h_f32).to(h.dtype)\n                I_0 = -(-h_f32).expm1().to(h.dtype)\n                \n                x = exp_h * x + I_0 * denoised_2\n                \n            if sigmas[i + 1] > 0 and sigma_up > 0:\n                x = x + torch.randn_like(x) * sigma_up\n\n            if callback_fn is not None:\n                callback_kwargs = {'z0t': denoised.detach(),\n                                    'zt': x.detach(),\n                                    'decode': self.decode}\n                callback_kwargs = callback_fn(i, new_t, callback_kwargs)\n                denoised = callback_kwargs[\"z0t\"]\n                x = callback_kwargs[\"zt\"]\n\n        img = self.decode(x)\n        img = (img / 2 + 0.5).clamp(0, 1)\n        return img.detach().cpu()"
7}

Tool ResultStep 8

OK: Replaced lines 624..677 with 84 line(s) in CFGpp-main/latent_diffusion.py. Editable range: 624–707.

[Current file: CFGpp-main/latent_diffusion.py | editable: 624–707 | total: 1046 lines]
   621| @register_solver("ddim_cfg++")
   622| class BaseDDIMCFGpp(StableDiffusion):
   623|     # TODO: Implement your improved sampling method here.
   624      def __init__(self,
   625                   solver_config: Dict,
   626                   model_key:str="runwayml/stable-diffusion-v1-5",
   627                   device: Optional[torch.device]=None,
   628                   **kwargs):
   629          super().__init__(solver_config, model_key, device, **kwargs)
   630  
   631      @torch.autocast(device_type='cuda', dtype=torch.float16)
   632      def sample(self,
   633                 cfg_guidance=1.0,
   634                 prompt=["",""],
   635                 callback_fn=None,
   636                 **kwargs):
   637          
   638          t_fn = lambda sigma: sigma.log().neg(

AssistantStep 9edit

Tool Input

1{
2  "content": "    def reverse_process(self,\n                        null_prompt_embeds,\n                        prompt_embeds,\n                        cfg_guidance,\n                        add_cond_kwargs,\n                        shape=(1024, 1024),\n                        callback_fn=None,\n                        **kwargs):\n        \n        t_fn = lambda sigma: sigma.log().neg()\n        sigma_fn = lambda t: t.neg().exp()\n        \n        total_sigmas = (1-self.total_alphas).sqrt() / self.total_alphas.sqrt()\n        num_steps = len(self.scheduler.timesteps) // 2\n        sigmas = get_sigmas_karras(num_steps, total_sigmas.min(), total_sigmas.max(), rho=7.).to(self.device)\n        \n        zt = self.initialize_latent(method=\"random_kdiffusion\",\n                                   latent_dim=(1, 4, shape[0] // self.vae_scale_factor, shape[1] // self.vae_scale_factor),\n                                   sigmas=sigmas).to(self.dtype)\n        x = zt\n        \n        pbar = tqdm(range(len(sigmas) - 1), desc='SDXL-DPM++2S Ancestral + Eta Decay')\n        for i in pbar:\n            sigma = sigmas[i]\n            new_t = self.timestep(sigma).to(self.device)\n            \n            with torch.no_grad():\n                denoised, _ = self.kdiffusion_zt_to_denoised(x, sigma, null_prompt_embeds, prompt_embeds, cfg_guidance, new_t, add_cond_kwargs)\n            \n            progress = i / num_steps\n            eta = 1.0 if progress < 0.8 else 0.0\n            \n            sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)\n            \n            if sigma_down == 0:\n                d = self.to_d(x, sigmas[i], denoised)\n                x = denoised + d * sigma_down\n            else:\n                t = t_fn(sigmas[i])\n                t_next = t_fn(sigma_down)\n                r = 1 / 2\n                h = t_next - t\n                s = t + r * h\n                \n                h_f32 = h.float()\n                h_r_f32 = (h_f32 * r)\n                \n                exp_h_r = torch.exp(-h_r_f32).to(h.dtype)\n                I_0_r = -(-h_r_f32).expm1().to(h.dtype)\n                \n                x_2 = exp_h_r * x + I_0_r * denoised\n                \n                with torch.no_grad():\n                    sigma_s = sigma_fn(s)\n                    t_2 = self.timestep(sigma_s).to(self.device)\n                    denoised_2, _ = self.kdiffusion_zt_to_denoised(x_2, sigma_s, null_prompt_embeds, prompt_embeds, cfg_guidance, t_2, add_cond_kwargs)\n                \n                exp_h = torch.exp(-h_f32).to(h.dtype)\n                I_0 = -(-h_f32).expm1().to(h.dtype)\n                \n                x = exp_h * x + I_0 * denoised_2\n                    \n            if sigmas[i + 1] > 0 and sigma_up > 0:\n                x = x + torch.randn_like(x) * sigma_up\n            \n            if callback_fn is not None:\n                callback_kwargs = {'z0t': denoised.detach(),\n                                    'zt': x.detach(),\n                                    'decode': self.decode}\n                callback_kwargs = callback_fn(i, new_t, callback_kwargs)\n                denoised = callback_kwargs[\"z0t\"]\n                x = callback_kwargs[\"zt\"]\n                \n        return x",
3  "filename": "CFGpp-main/latent_sdxl.py",
4  "start_line": 715,
5  "end_line": 760,
6  "op": "replace"
7}

Tool ResultStep 9

OK: Replaced lines 715..760 with 74 line(s) in CFGpp-main/latent_sdxl.py. Editable range: 713–794.

[Current file: CFGpp-main/latent_sdxl.py | editable: 713–794 | total: 1070 lines]
   710| # CFG++ version
   711| ###########################################
   712| 
   713  @register_solver("ddim_cfg++")
   714  class BaseDDIMCFGpp(SDXL):
   715      def reverse_process(self,
   716                          null_prompt_embeds,
   717                          prompt_embeds,
   718                          cfg_guidance,
   719                          add_cond_kwargs,
   720                          shape=(1024, 1024),
   721                          callback_fn=None,
   722                          **kwargs):
   723          
   724          t_fn = lambda sigma: sigma.log().neg()
   725          sigma_fn = lambda t: t.neg().exp()
   726          
   727          total_sigmas = (1-self.total_alphas).sqrt() / self.total_alphas.sqrt()
   728          num_steps = len(self.scheduler.timestep

AssistantStep 10test